Keywords: Communicating Materials, Wireless Sensor Network, In-Network Processing, Energy consumption model, Multi-Agent System

With the popularization of the Internet of Things, more and more intelligent products appear in our daily life. The McBIM project (Materials communicating with the Building Information Modelling) aims to design a new type of "communicating concrete": concrete elements equipped with embedded wireless sensor network (WSN). This communicating concrete can measure the physical environment, store the information and exchange data with BIM platforms. One objective is to demonstrate the usefulness of the concept over the whole building lifecycle and the energy management is one of the facing challenges. Meanwhile, the product, user information as well as the large amount of monitoring data bring challenges regarding data management. To solve both issues, this work proposes to couple the physical communicating concrete to an external, digital and intelligent system in order to extend network lifetime.

In this thesis, these physical and digital parts are both studied. In the physical part, energy saving methods are reviewed, to understand how to extend the network lifetime.

Network routing, in-network data processing and time synchronization are considered to build an energy model for periodic data collection. This energy model is validated with an Arduino-XBee based platform. The experiment shows that the analytic energy estimation model provides an approximate but accurate remaining energy level. It can be used online by nodes or remotely by user to the remaining energy. Energy-related data and collection modes can be managed by the digital part, based on a multi-agent system (MAS).

The MAS can not only store monitoring data of communicating elements but also provide energy efficient data collection or organization strategies. Composition of communicating elements may happen in construction or exploitation lifecycle's phase to reduce energy consumption. A recursive multi-agent architecture is proposed to meet high flexibility requirements. This multi-agent system is implemented on a 3D simulation platform to visualize the communicating concrete and verify the usefulness of our analytic energy model. The recursive multi-agent architecture provides energy efficiency solutions to extend network lifetime and respond to environmental changes.

rigor for science led me to the new world. Since the first day at CRAN, he has provided me with enthusiastic help and invaluable suggestions. I have learned many things from him, especially, the logical way of thinking which will accompany all my following research life.

I also express my warm thanks to my co-supervisor Dr. Michaël DAVID. He has shared a lot of novel ideals and provided professional insights. Besides, he has spent much precious time for reading and improving the quality of our contributions. My work could not be achieved without his patient and careful guide.

Additionally, I would also like to thank all members at CRAN, especially my dear colleagues of ISET team for sharing their experiences on studies and life. They accompanied me to explore different cultures and enriched my life. I am also grated to administrative staffs at CRAN, especially, the secretaries for their instant support.

Moreover, I would like to extend my thanks to the jury members for spending their precious time to read and review this thesis. I am greatly thankful for the funding of the French National Research Agency and the support of all partners of McBIM project.

Finally, my thanks would go to my beloved parents for their loving considerations and great confidence. They have always been with me, shared and supported me during the difficult course of the thesis, especially, the period of COVID-19 pandemic. v

Résumé

Avec la popularisation de l'Internet des Objets, de plus en plus de produits intelligents apparaissent dans notre vie. Le projet McBIM (Matière communicante au service du BIM-Building Information Modelling) vise à concevoir un nouveau type de « béton communicant » qui est le béton équipé d'un réseau de capteurs sans fil embarqué (RSCF). Ce béton communicant est capable de mesurer des phénomènes physiques, de stocker des informations et d'échanger des données avec des plateformes BIM. Pour démontrer l'utilité du concept sur l'ensemble du cycle de vie, l'exigence de service à long terme pose des défis relatifs à la durée de vie du réseau intégré dans la matière. Dans le même temps, la grande quantité de données de surveillance posent des défis relatifs à la gestion d'informations. Dans ces travaux, nous proposons de coupler le béton communicant physique à sa représentation digitale. Ce système externe et intelligent doit pouvoir permettre à terme de prolonger la durée de vie du réseau.

La partie physique étudie des méthodes d'économie d'énergie applicables à la collecte de données dans les réseaux de capteurs, afin de comprendre comment prolonger la durée de vie de réseau et construire un modèle de consommation d'énergie du réseau. Le routage du réseau, le traitement des données dans le réseau (In-Network Processing) et la synchronisation des noeuds sont pris en compte pour construire un modèle de consommation énergétique du réseau embarqué, modèle validé avec une plate-forme Arduino-XBee. L'expérience montre que le modèle d'estimation de l'énergie fournit une approximation précise de l'énergie restante, pouvant être utilisée en ligne par les noeuds ou à distance par un utilisateur. La gestion de l'énergie du réseau et des modes de collecte peut-être réalisée par un système multi-agents dédié.

La partie digitale est construite avec un système multi-agents, pouvant non seulement stocker les données de surveillance, mais également fournir des stratégies de collecte de données. La composition des éléments communicants peut intervenir en phase de construction ou d'exploitation. Une architecture multi-agents récursive est proposée pour répondre à cette exigence de flexibilité. Ce système multi-agents est implémenté sur une plateforme de simulation 3D pour visualiser le béton communicant et vérifier la validité de notre modèle énergétique analytique. L'architecture multi-agents récursive fournit des solutions efficaces pour estimer l'énergie résiduelle dans la matière, afin de prolonger la durée de vie du réseau et répondre aux changements environnementaux. Figure 21 The packet formats in application OscilloscopeRF (Jun-Zhao and Jiehan, 2008) .. [START_REF] Dron | An emulation-based method for lifetime estimation of wireless sensor networks[END_REF] ............ Figure 34 PROSA architecture ( Van Brussel et al., 1998) Team (e), Matrix (f) and Congregation (g) [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF] Figure 40 Three-level holonic architecture for urban planning [START_REF] Khelifa | A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm[END_REF] ...

Introduction: context and objectives

The rapid development of Internet of Things (IoT) leads to the intelligent product (IP) paradigm [START_REF] Meyer | Intelligent Products: A survey[END_REF]. With equipped sensors, communication modules and computing chips, intelligent product has data storage and processing abilities. Product-related information is accessible to user directly via the RFID (Radio frequency identification), NFC (Near Fielded Communication) or remotely via other wireless technologies and the internet. The use of intelligent sensors allows to monitor surrounding environment or complete special tasks. Due to the accessibility of data and computing capacities, the intelligent product paradigm has been widely applied for Manufacturing, Logistics or Building Heath Monitoring [START_REF] Mcfarlane | The intelligent product in manufacturing control and management[END_REF] ( [START_REF] Van Belle | Cependant, il existe certaines limites à l'utilisation d'un produit intelligent : premièrement, le flux d'informations est interrompu si l'appareil est endommagé; deuxièmement, la capacité de stockage au sein d'un produit est limitée et il est difficile de garantir que toutes les informations sur le produit pourront être stockées localement tout au long de son cycle de vie. Pour dépasser ces limites, cette thèse considère le paradigme du matériau communicant[END_REF] [START_REF] Lamonaca | Internet of Things for Structural Health Monitoring[END_REF]. However, there are some limits for the use of intelligent product: First, the information flow is interrupted if the equipped device is damaged; Second, the storage capacity within a product is limited and it is hard to ensure all product-related information throughout its whole lifecycle can be stored locally. To overcome these limits, this thesis considers the communicating material paradigm.

In 2009, the Research Center for Automatic Control (CRAN) began to study the "communicating material" (CM) concept. It is a material that can process, store data and communicate with the environment [START_REF] Kubler | Problem definition methodology for the "communicating material" paradigm[END_REF]. CM is a paradigm which integrates the communicating ability into the material instead of equipping an external communication device. CM can sense its surrounding environment, store its monitoring data and its productrelated information. Besides, a CM can either cooperate with others to form a composed CM or be decomposed into different parts, the composed or decomposed CM shall still be communicating materials. The concept has been applied in different works, such as [START_REF] Kubler | Premiers travaux relatifs au concept de matière communicante : Processus de dissémination des informations relatives au produit[END_REF] [START_REF] Krommenacker | Towards Product Health Monitoring throughout its life-cycle[END_REF] [START_REF] Jover | Interest of new communicating material paradigm: An attempt in wood industry[END_REF] [START_REF] Mekki | Gestion de l'information embarquée dans des matériaux communicants à l'aide de protocoles de réseaux de capteurs[END_REF]. These latter works have been proposed mainly for the traceability applications of textiles, or the data dissemination / data replication in concretes throughout their lifecycle. These works focus on communication McBIM project has two main objectives. The first is to design the communicating concrete which can monitor surrounding physical parameters, stores, process collected data and periodically transmit information to users (see Figure 1). The inner WSN consists of two types of nodes: sensing node (SN) and communicating node (CN). The SNs monitor or sense surrounding environment, and all interesting physical data (such as temperature, humidity or strength etc.) are transmitted to their dedicated CN. The CNs aggregate or process the received monitoring data and send the resulting message directly or indirectly (via other CNs) to a base station (BS).

Figure 1 McBIM communicating concrete

The second objective is to demonstrate the usefulness of communicating concrete over whole building lifecycle. In different phases, McBIM concretes may be exploited or used by different companies with their own BIM data. As consequence, their monitoring behaviors may be also different (for example the monitoring frequency). Long-term service requirement, the need of large data storage capability and high flexibility have brought different challenges.

The different partners collaborate to achieve these objectives. The LAAS designs the sensing and communicating nodes, the LIB studies data interoperability with BIM applications. 360 Smart Connect/FINAO implements the result of research partners in real application. The CRAN works on the intermediate layers between the hardware (developed by LAAS) and the application (developed by LIB and 360SC). In the framework of this project, my thesis focuses on the energy management for periodical data collection realized by the embedded WSN.

Since the communicating concrete should work at least ten or twenty years, one element may cooperate with the others to save energy. Due to the limitation of storage and computational capability of the communicating concrete, this work explores the coupling of this physical artefact with a numerical representation, in the form of a multi-agent system, to manage data and energy of embedded network (see Figure 2). Indeed, communicating nodes in the physical part can be represented by dedicated agents in digital part which can not only store the monitoring and product-related information, but also evaluate and estimate data collection strategies to provide an optimal solution with a global view. This digital part of communicating concrete can be seen as a digital twin.

Figure 2 Intelligent control system for communicating concrete

To achieve an efficient energy management and extend network lifetime, there are four challenges to tackle: two for the physical part (questions 1 and 2, related to the embedded WSN), and two associated to the digital part (questions 3 and 4, concerning data and energy management) as follows:

1. How to maximize lifetime of data collection (process) within the embedded WSN? Both data processing techniques, duty cycle management at node and data collection strategies between nodes should be taken into account to reduce energy cost for data collection.

How to predict remaining energy of network in a resource constrained environment?

To predict the remaining energy, consumption models must be established. Is it possible to build a generic model or is it necessary to specify energy models for each kind of WSN structures? 3. How to realize the link between the communicating concretes with their dedicated agents, as well as data and energy management of nodes at these agents? The physical part is linked to a digital part represented in this work by a multi-agent system. As a result, this issue studies the relation between real physical nodes and the constitution of the agent architecture in the MAS.

4.

How to achieve the composition of agents? Communicating material is a product which has to support composition/decomposition features. This could be possible at the physical level and at the digital level at the same time. To build this mechanism, the interactions between agents should be considered.

Around these mentioned challenges, the main contributions of this study, both theoretical and practical are the followings:

• Analytical energy consumption models for periodic data collection in WSN are

proposed. An energy model for data collection is detailed, as well as the model for data collection without aggregation (see Chapter 3). These predictive models are tested and validated by experimentations (see section 5.1 of Chapter 5).

• A recursive MAS architecture is presented (Chapter 4) to build a digital part for communicating material. The proposed architecture is recursive and flexible to allow communicating materials composition/decomposition. The digital part allows to provide the remaining energy level but also to provide cooperative solutions to meet high flexibility of communicating concrete.

• A 3D communicating concrete simulator is developed with the NetLogo platform, where communicating nodes are represented by agents. This digital tool allows to visualize the energy level of communicating concrete and provides the remaining lifetime information (in section 5.2 of Chapter 5).

This thesis is organized in five chapters followed by a general conclusion:

Chapter 1 introduces the context and the concepts of communicating material, its characteristics and interests in the framework of the McBIM project. It begins with the context of Product Lifecycle Management. IP and the holonic paradigm are then described. To overcome the limitations of intelligent product, the concept of communicating material (CM) is proposed. In order to efficiently manage and extend the lifecycle of communicating concrete, the solutions in WSN are discussed, meanwhile a holonic architecture is proposed for data and energy management. Finally, the research issues of this thesis are detailed.

Chapter 2 presents a state of the art both on the physical part and the digital part. For the physical part, literature solutions for in-network data processing in WSN are presented: data aggregation functions, energy efficient routing protocols, synchronization protocols and energy consumption models. For the digital part, the traditional holonic architecture for intelligent product is presented, as well as the multi-agent approaches for information management. In addition, to meet the composition/decomposition requirement of CM application, recursive solutions are reviewed and discussed. Chapter 5 presents the applicative and demonstrative results for both the physical (WSN) and digital part (MAS). The analytical energy models for data collection with/without aggregation are validated with an Arduino-XBee platform (in section 5.1). On the digital part, a 3D agentbased model is developed on the Netlogo platform for visualizing energy level of communicating node, analyzing and providing optimal solution to extend lifetime of the physical part.

Finally, the thesis ends with a general conclusion in which the contributions of our works are summarized, and some perspectives are discussed.

The structure of this thesis is presented as follows:

Introduction: context and objectives

This part describes the context motivation of this thesis, the objectives as well as facing research challenges and our main contributions (current part of the document).

Chapter 1: The concept of communicating material

This chapter introduces the concept of communicating material, its characteristics, and its application interests in the framework of the McBIM project. Besides, a holonic architecture is presented for the energy management.

Chapter 2: The state of the art on physical part and digital part

Chapter 2 presents the state of the art both on the physical part (embedded WSN) and the digital part (Multi-Agent system).

Chapter 3: Energy consumption for periodic data collection in WSN

Chapter 3 presents the energy consumption model of communicating node and data collection with/without aggregation in chain structures are detailed. Data collection with lossy or lossless aggregation are also discussed for chain, tree, and cluster structures.

Chapter 4: Recursive architecture for energy management of Communicating Material

Chapter 4 presents the proposed recursive architecture and the communicating material agent model. The composition/decomposition mechanisms are discussed, followed by the cooperation between aggregated CMs.

Chapter 5: Experiments, developments and validation

The relative experiments for data collection with/without aggregation on an Arduino-XBee platform are illustrated in this chapter. Meanwhile, a 3D-energy estimation on Netlogo platform for digital part is presented for the remaining lifetime estimation as well as the cooperation between CMs.

Conclusion

This chapter summarizes the contributions of this work and points out the following perspectives. This chapter aims at introducing the concept of communicating material (CM), its application interests and our study case called McBIM project. As explained later, CM is a sub-type of intelligent product, concept used in diverse applications (production control, maintenance, …) in past years. One important application field of this concept is the management of product data all over its lifecycle, which is also our application context. This chapter begins with a description of product lifecycle management (section 1.1), followed by a short presentation of the notion of intelligent products in section 1.2.1. In order to effectively manage the intelligent products and their information, the Holonic Paradigm is presented in section 1.2.2. The definition and characteristics of communicating material is given in section 1.3, followed by the specific application case of the ANR McBIM project in section 1.4. Finally, the proposed digital twin design for data and energy management as well as the related research issues are given in section 1.5.

Product Lifecycle Management

In the context of globalization, product lifecycle management (PLM) plays an increasingly important role in enterprises. PLM emerged in the 1990s but has been conceptualized by [START_REF] Rachuri | Information sharing and exchange in the context of product lifecycle management: Role of standards[END_REF] as a strategic approach to create and manage a company's product-related intellectual capital, from the design to the retirement of product. Since a product may be used by many different companies throughout its whole lifecycle, large amounts of information are stored, exchanged and managed.

In general, the product lifecycle consists of three main phases: 1) Beginning of Life (BOL), including the product design and manufacturing; 2) Middle of Life (MOL), including logistics, exploitation and maintenance; 3) End of Life (EOL), including remanufacturing, reuse and recycling. In different phases, the product may be used by different stakeholders or companies with their own product management systems or standards. The difference of management standards or systems can create "information silos" and may cause interruptions in product information flow. To prevent users to make decision with incomplete information, the concept of closed-loop PLM has been proposed by [START_REF] Jun | Research issues on closed-loop PLM[END_REF]). An example of information flow is shown on Figure 3, depicting direct and feedback (indirect) information flows among BOL, MOL and EOL.

Figure 3 Information flow of closed-loop PLM [START_REF] Jun | Research issues on closed-loop PLM[END_REF] Compared with traditional PLM approaches, the closed-loop PLM focuses on the product information management over the whole lifecycle. Thus, all actors (manufacturer, transporters, and customers) can make better operation decisions with the complete product information.

Intelligent Product

Substantial information distribution improves data accessibility and availability compared to centralized architectures. Product information may be allocated both within fixed databases and/or within the product itself, thus leading to products with informational and/or decisional abilities, referred to as "Intelligent Products". Several different definitions of this concept have been proposed and are presented in the next section.

Definition of intelligent product

The concept is first proposed in [START_REF] Mcfarlane | Auto ID systems and intelligent manufacturing control[END_REF], as a physical product that can support or influence operations to meet set conditions or purposes. It has five characteristics:

• Possesses a unique identity.

• Is capable of communicating effectively with its environment.

• Can retain or store data about itself.

• Deploys a language to display its features, production requirements, etc.

• Is capable of participating in or making decisions relevant to its own destiny.

According to Kärkkäinen, intelligent product is a product centric approach, which uses software agents, peer-to-peer information sharing, and coding mechanism enabling the material information flows [START_REF] Kärkkäinen | The product centric approach: A solution to supply network information management problems?[END_REF]. The definition given in [START_REF] Ventä | Intelligent products and systems: Technology theme -Final report[END_REF] focuses on the communication and reaction capabilities of the product with its surrounding environment.

According to Venta, intelligent products should have the four following characteristics: 1)

Continuously monitor their status and environment; 2) React and adapt to environmental and operational conditions; 3) Maintain optimal performance in variable circumstances; 4) Actively communicate with the user, environment or with other products and systems. [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF] defines that an intelligent product is a product system which contains the three capabilities: 1) sensing and communicating with environment; 2) storing its own data; 3) processing monitoring or received data to react to environment changes.

Product intelligence is a wide field encompassing different research areas and applications, such as manufacturing control in [START_REF] Mcfarlane | The intelligent product in manufacturing control and management[END_REF], supply chain in ( [START_REF] Van Belle | Cependant, il existe certaines limites à l'utilisation d'un produit intelligent : premièrement, le flux d'informations est interrompu si l'appareil est endommagé; deuxièmement, la capacité de stockage au sein d'un produit est limitée et il est difficile de garantir que toutes les informations sur le produit pourront être stockées localement tout au long de son cycle de vie. Pour dépasser ces limites, cette thèse considère le paradigme du matériau communicant[END_REF] and structural health monitoring in [START_REF] Lamonaca | Internet of Things for Structural Health Monitoring[END_REF]. A three-dimension classification is proposed in [START_REF] Meyer | Intelligent Products: A survey[END_REF] and illustrated Figure 4 Classification of intelligent products [START_REF] Meyer | Intelligent Products: A survey[END_REF] On the level of intelligence axis, there are three sub-levels: information handling, problem notification and decision making. An intelligent product at the information handing level can only process and transmit the monitoring information. With the problem notification ability, the product can detect unwanted situations and report warning signals. At the highest level, the product can adjust its own state and can adapt to environmental changes.

There are two possibilities under the intelligent location axis: intelligence through network and intelligence at object. In the first one, the intelligence of the product is outside the product, namely, on an external server that processes and generates operation orders to react to environment changes. In the second one, all information processing, even advanced decision, is done by the product itself.

There are two levels corresponding to the aggregation dimension: intelligent item or intelligent container. The intelligent item only manages its own information, notifications and decisions.

Level of Intelligence

Location of intelligence

Aggregation level of intelligence

If it contains components, they cannot be distinguished as individual objects. In contrast, the intelligent container is aware of the states of all components. When parts of its composition are removed, it can still work as a smart container. For example, if the engine of a car is removed, the car can still know the states of the other components.

Another classification is proposed in [START_REF] Zbib | Allocation et routage dynamique dans un FMS basés sur le concept de champ de potentiel[END_REF] which takes all lifecycle phases into account as shown in Figure 5. Compared with the classification proposed by Meyer, the location of intelligence and aggregation of intelligence axes are the same. There are two more levels in the degree of intelligence, and a new axis introduces the time dimension: Lifecycle.

Figure 5 Intelligence classification with lifetime phases

On the degree of intelligence axis, there are two additional levels: Passive Product (PP) which can only store the product-related information, and the Physical Actor Product (PAP) which can react to the environment changes based on its decisions. On the lifecycle axis, all product phases (design, manufacturing, logistics, utilization, and recycling) are considered. Indeed, the intelligence of a product may be different in different phases. Both classifications can be used to analyze the intelligence requirement of an intelligent product in different lifecycle phases.

With the communication, processing and storage capabilities, intelligent product has been widely used for PLM from manufacturing to recycling as shown in Figure 6. A large number of product-related information may be generated throughout the whole lifecycle. To overcome the limitation of data storage and information processing, product-related information can be stored in remote servers, and dedicated agents can be used to complete decision-making processing to adapt to environmental changes. As explained in the next section, an intelligent product with its dedicated decision-making agent can be seen as a product holon. As consequence, a holonic architecture could be considered for the information and energy management of intelligent product. 1992).

The architecture of EPCglobal is shown in Figure 7. Each product is labeled with a unique electronic product code (EPC). The product information is accessed via the reader, and then filtered and sent to the application layer EPCIS (EPC Information Service). In this centralized approach, it is the ONS (Object Naming Service) that indicates where to find the required information. In DIALOG, the product identification and information linking concept ID@URI was used to create a globally unique product identifier, where the URI is a computer address (for example "www.xxx.com") and ID represent the serial number of product instance. Productrelated information is managed by a product agent in server. The use of ID @ URI provides a pointer that allows users to query the product agent for their required information. Unlike the two other standards, WWAI is a peer-to-peer application-level protocol for distributed article information. The product identifier is the WWAI identity code. In this approach, there is no central storage of product information. Each participant controls its own product information and decides whether the information is public or private.

Figure 7 Architectures of information system: EPCIS, DIALOG and WWAI

Hereafter, a distinction is made between "the physical part" (the product augmented with communicating devices) and the "digital part" (software components stored on the cloud). The physical part combined with the digital part can be seen as a holon, which is presented in the following section.

Holonic paradigm

The concept of holon is first proposed by Koestler [START_REF] Koestler | The ghost in the machine[END_REF]. This term is a combination of the Greek word holos, meaning "whole", with the suffix -on standing for "part". A holon consists of a physical part and digital part. According to the Janus effect, holons can be grouped into a whole to form hierarchical structures called holarchies. According to the definition, a holon has two main characteristics:

• Autonomy: each holon can act autonomously, even in case of unpredictable circumstances.

• Cooperation: holons are able to negotiate and execute mutually acceptable plans and take mutual actions against malfunctions.

A holon can represent a physical or logical activity, like a machine or a control system. For example, in the holonic manufacturing architecture PROSA (Product-Resource-Oder-Staff) ( Van Brussel et al., 1998), products, resources and orders are represented by different types of holons as shown in Figure 8. Besides, staff holons are introduced to assist the basic holons to complete their work. In this architecture, these holons can cooperate together to achieve common goals.

Figure 8 PROSA architecture ( Van Brussel et al., 1998) These highly flexible features make holonic paradigm having been widely used in manufacturing [START_REF] Chirn | A holonic component-based approach to reconfigurable manufacturing control architecture[END_REF] [START_REF] Morariu | Formalized information representation for intelligent products in service-oriented manufacturing[END_REF] [START_REF] Sallez | On the activeness of physical internet containers[END_REF]. Multiagent Systems are often used to represent the virtual part of a holon.

1.3 A new type of intelligent product: the communicating material 1.3.1 Definition of the communicating material Thomas proposes the concept of communicating materials, i.e., materials able to communicate with their environment, process, exchange and store information in their own structures [START_REF] Thomas | RFID et nouvelles technologies de communication; enjeux économiques incontournables et problèmes d'éthique[END_REF]. Besides, they also have the capability to sense their environment and measure their own internal physical states. In the works of [START_REF] Kubler | Premiers travaux relatifs au concept de matière communicante : Processus de dissémination des informations relatives au produit[END_REF] and [START_REF] Mekki | Gestion de l'information embarquée dans des matériaux communicants à l'aide de protocoles de réseaux de capteurs[END_REF], this concept has been applied in different industrial domains, from different perspectives. Diverse early prototypes were designed (or simulated) for the needs of the manufacturing and the construction industry, by spreading micro-electronics devices into a material.

The material could be either wood, textile or concrete. The interests of such material are various:

(a) because of their data storing capacity, they can convey all information related to design, manufacturing and logistics throughout whole lifecycle, (b) given the ability to sense their environment and process related information, they can also be used during the MOL (exploitation and maintenance), such as intelligent building sensors to perform structural health monitoring. In previous works, the inserted devices were either RFID tags [START_REF] Kubler | Premiers travaux relatifs au concept de matière communicante : Processus de dissémination des informations relatives au produit[END_REF] or self-powered wireless sensor networks (WSNs) embedded into the material [START_REF] Mekki | Data Dissemination Algorithms for Communicating Materials Using Wireless Sensor Networks[END_REF]. Both works deal with the data dissemination/data replication, which is an issue related to the second capability. However, these works did not address the issue related to the first capability, i.e., the composition/decomposition of communicating materials.

No clear formal definitions of the communicating material have been provided until now. As consequence, a definition is provided hereafter. In our work, the Communicating Material is an Intelligent Product with two additional capabilities:

• The capability of being intrinsically and wholly communicating: even if the product undergoes a physical transformation (composition or decomposition), the resulting pieces shall still be communicating materials.

• The capability of managing its own data: The material should be able to manage its own data according to the events occurring in its environment. For instance, the material could decide itself to propagate/replicate specific data onto different material parts because a physical transformation is scheduled, thus avoiding data losses.

Therefore, based on the definitions of intelligent products in [START_REF] Wong | The intelligent product driven supply chain[END_REF] [START_REF] Ventä | Intelligent products and systems: Technology theme -Final report[END_REF], the communicating material has the following 7 characteristics:

1. Possesses a unique identity 2. Is capable of communicating effectively with its environment 3. Can retain or store data about itself 4. Deploys a language to display its features, production requirements, etc. 5. Is capable of participating in or making decisions relevant to its own destiny 6. Continuously monitors its status and environment 7. Can remain a communicating material under composition or decomposition.

Characteristics (1) to (5) are inherited from [START_REF] Wong | The intelligent product driven supply chain[END_REF], Characteristic (6) is from [START_REF] Ventä | Intelligent products and systems: Technology theme -Final report[END_REF]. This one is important since it underlines the capacity of a product to monitor its own status or environment. Characteristic (7) is the only one which is completely dedicated to the concept of communicating material and is mandatory.

Holonic architecture for communicating material

As a special type of intelligent product, communicating materials have composition and decomposition characteristics which is also a fundamental property of Holons as introduced by [START_REF] Koestler | The ghost in the machine[END_REF]. Therefore, a CM can be considered as a material holon which consists of communicating nodes in the physical part related to dedicated node agents in digital part as shown in Figure 9. The node agent can not only store the product-related information throughout the whole lifecycle, but also provide suitable strategies to real nodes to adapt to environment changes based on the state of nodes and on the monitoring information.

Figure 9 Structure of an elementary material Holon

As communicating material can be considered as a type of holon, we can then define a CM as a holarchy of sub-communicating material CMj i , where i is the level of holarchy the CM is attached to, and j the index of the CM in the corresponding level. Each sub-communicating material could be decomposed in other communicating materials until a primary level of decomposition called elementary depicted in Figure 9. Indeed, even if the level of the holarchy could be infinite, it is limited in practice. The elementary level is the finest granularity of the holarchy. Different representations of a communicating material are illustrated in Figure 10. As the composition can be infinite, a recursive system for the management of informational part is necessary. A communicating material should work as long as possible, however, there are physical limits, especially concerning the energy. Therefore, energy management solutions in the physical part (WSN in our work) and the digital part (MAS in our work) should be both considered for extending its lifecycle. The next sections will briefly introduce the two components of the communicating material holon, i.e. the WSN (section 1. 3.3) and the MAS (section 1. 3.4).

Overview of Wireless Sensor Networks (WSN)

The rapid development of microprocessors and sensors has made WSN widely used in various fields [START_REF] García Ansola | Distributed decision support system for airport ground handling management using WSN and MAS[END_REF], such as environment monitoring [START_REF] Paek | Image-based environmental monitoring sensor application using an embedded wireless sensor network[END_REF], smart agriculture [START_REF] Li | Wireless sensor network for precise agriculture monitoring[END_REF], building monitoring [START_REF] Bhuiyan | Deploying Wireless Sensor Networks with Fault-Tolerance for Structural Health Monitoring[END_REF] and logistics [START_REF] Xiao | Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics[END_REF]. Most applications are in resource constrained environments. The limitation of energy brings challenges related to the network lifetime and performance. In our study, since it is hard to change the battery for sensor node in concrete, we focus on maximizing the lifetime using data aggregation (MLDA) in WSN. Existing solutions will be presented in the section 2.1.

Wireless Sensor Networks are often made up of a large number of nodes as shown in Figure 11. Sensor nodes have the capabilities of sensing, processing and communicating data. They monitor their surrounding environment (such as temperature, humidity, pressure, motion, or other physical phenomena) and transfer their data directly or indirectly (through other nodes) to a base station (BS) also named sink node.

Figure 11 Example of WSN

Sensor nodes send monitoring data to their neighbor up to the sink node (in blue) which then transfers data over the internet. Over last two decades, how to extend the network lifetime has attracted the attention of many researchers [START_REF] Kalpakis | A combinatorial algorithm for the maximum lifetime data gathering with aggregation problem in sensor networks[END_REF] [START_REF] Aziz | A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks[END_REF]) [START_REF] Yetgin | A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks[END_REF] [START_REF] Wang | Event-aware hierarchical routing with differential compression to extend WSN lifetime[END_REF]. Due to the limitation of technologies, the processing power and storage capability of nodes is limited. Nodes can not always have a global view of the network, thus optimization over the network is often suboptimal. As consequence, applying an efficient external management system with a global view of the network could provide suitable solution to extend network lifetime.

Multi-Agent System

As mentioned above, to represent the physical part in holarchies, multi-agent systems are often implemented, since they can solve complex tasks [START_REF] Sardouk | Crisis management using MAS-based wireless sensor networks[END_REF] [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF]. A MAS is composed of multiple autonomous agents that are capable to achieve their individual goal [START_REF] Giret | Towards a recursive agent oriented methodology for large-scale MAS[END_REF]. The agent is a virtual entity which can process monitoring information and make decisions based on a given goal. According to Wooldridge [START_REF] Wooldridge | Intelligent agents: theory and practice[END_REF], an agent has four characteristics:

• Autonomy: the agent can work alone without human intervention.

• Social ability: the agent can interact with other agents via a communication language.

• Reactivity: the agent senses its environment and responds in a timely manner to modify the course of events if necessary.

• Pro-activeness: each agent uses its history, monitored data and information of other agents to predict future actions.

Due to its autonomy and high flexibility, it has been widely used from industry to our daily life. However, to face the non-deterministic environment, two major questions are pointed out by Wooldridge: 1) how to build autonomous agents which can make their actions independent to complete delegated tasks even with environment changes; 2) how to define relationship for agents with common goals or different goals [START_REF] Wooldridge | An Introduction to MultiAgent Systems[END_REF].

Different authors have attempted to answer and validate these two questions. According to Caridi, optimal global performance could be achieved if several decision-making agents cooperate for the common interest or task [START_REF] Caridi | Multi-agent systems in production planning and control: an overview[END_REF]. From Isern, even if the agents are perceived as autonomous entities, the societal relation is necessary to allow the communication between the agents to achieve a global optimization [START_REF] Isern | The Journal of Systems and Software Organizational structures supported by agent-oriented methodologies[END_REF]. Agents can communicate with each other via some defined architecture to achieve common goal.

In the following section, the CM application in McBIM project will be presented. The interests of communicating concretes for PLM will be discussed as well as its position in the CM paradigm.

Figure 13 McBIM communicating concrete lifecycle

During the construction phase, communicating concretes will be assembled together. In this case, auto-organization is then needed to dynamically define a 3D network to achieve better data collection and thus energy savings. Due to the high flexibility in this step, concrete has to report its status frequently (such as every minute) to ensure the safety of construction and update the network information. When the construction is completed, the large 3D static WSN will regularly (by example once a day or once a week) monitor structural health data (such as cracks, temperature, corrosion, etc.) to ensure the safety of the building.

Since communicating concretes should work from manufacturing to exploitation (twenty or thirty years), long-term and high-quality service requirements bring challenges related to energy management in this inaccessible environment. Therefore, being able to monitor and/or determine the consumption of each node of the network is crucial for its management. Besides, a large number of product-related information will be generated throughout the whole lifecycle.

Due to the limitation of data storage, it is hard to store all information in the concrete. Therefore, applying the concept of Digital Twin (DT) seems to be a suitable solution. It would bring a WSN virtual model to store and present all information concerning the physical part and provide optimal behavior decisions (with a centralized solution) to extend its lifetime.

Position of communicating concrete in the CM paradigm

Concerning the intelligence levels of communicating concrete application in McBIM project, the mentioned classifications in [START_REF] Meyer | Intelligent Products: A survey[END_REF] and [START_REF] Zbib | Allocation et routage dynamique dans un FMS basés sur le concept de champ de potentiel[END_REF] could be used to position this application of the CM paradigm, that can be compared with the previous works of [START_REF] Kubler | Premiers travaux relatifs au concept de matière communicante : Processus de dissémination des informations relatives au produit[END_REF] and [START_REF] Mekki | Gestion de l'information embarquée dans des matériaux communicants à l'aide de protocoles de réseaux de capteurs[END_REF]. Although the behavior of communicating concrete may be different throughout the whole lifecycle, its intelligence on the axis of information processing level, intelligence location and aggregation level stay the same all along its lifecycle. Therefore, the classification of [START_REF] Meyer | Intelligent Products: A survey[END_REF] is used to position CM works as depicted in Figure 14.

Figure 14 Intelligence levels of CM applications

For the axis level of intelligence, communicating textile can only store product-related information (Information handing level). Thanks to the computing ability of sensor nodes, the communicating concrete developed in [START_REF] Mekki | Gestion de l'information embarquée dans des matériaux communicants à l'aide de protocoles de réseaux de capteurs[END_REF] equipped with an embedded WSN can send warning messages to the server (for example, a too high temperature) if the monitoring data exceeds the set value (Problem notification level). As mentioned above, communicating concrete in McBIM project can make decision locally or by their dedicated agent to extend product lifecycle. Therefore, the level of intelligence for CM application in McBIM is the decision-making level.

Mekki proposes network algorithms for data collection, based on routing choices made locally, by each node. Even if it can be locally optimal, the solution is globally suboptimal. In the

McBIM Project, the MAS can provide an optimal data collection strategy because it owns a global view of the embedded network. This global strategy could locally be mitigated by the physical node view. This explains why the location of intelligence is between intelligence at object and intelligence through network.

Concerning the aggregation level dimension, composition/decomposition is not considered in the application of communicating textile. For the communicating concrete application in [START_REF] Mekki | Gestion de l'information embarquée dans des matériaux communicants à l'aide de protocoles de réseaux de capteurs[END_REF] Figure 16 The holonic approach for communicating concrete

In this chapter, the communicating material paradigm was presented. The McBIM project proposes to apply the CM concept to the construction industry and to develop communicating concrete. The concept of communicating concrete has been defined and a holonic architecture is introduced for the management of its physical part and its informational part. Intelligence has been discussed and compared with previous CM applications by applying the classification proposed by Meyer [START_REF] Meyer | Intelligent Products: A survey[END_REF]. To achieve the design of the concrete CM, research issues are splitted in two parts: the physical part (energy assessment and saving in WSN) and the digital part (recursive architecture for the management of agents).

For the physical part, communicating concrete should work as long as possible (from manufacturing to exploitation). To overcome the battery limit in a resource constrained environment, using efficient data collection strategies can reduce energy consumption. Besides, knowing network energy level can be helpful to change data collection strategy to extend network lifetime. Therefore, the first research issue is: how to maximize lifetime of data collection within the embedded WSN and predict remaining energy of network with a simple analytical energy estimation model?

For the digital part, due to the limitation of processing power and storage capability at nodes, the consistency of the information throughout the lifecycle is a problem. Besides, WSN's decentralized self-organizing solutions can only provide local optimizations. Building a digital model allows storing and presenting all information about the physical part. This approach could also provide optimal behavior decisions (with a centralized solution) to extend its lifetime.

In addition, this digital model can be developed to digital twin in the future work. Realizing the digital concrete leads to the research issues: how to manage monitoring data and energy information at agents? For the composition of communicating concretes, the embedded networks can be grouped into one network. As consequence, the dedicated agents in digital part should also be grouped into aggregated agents. Furthermore, as required by the holonic paradigm, this agent architecture must support recursion. Therefore, how to organize the agents to achieve a recursive architecture and how to realize the composition or decomposition? is another research issue.

In the following chapter, literature solutions around these research issues will be reviewed. For the physical part, the state of the art around energy saving and assessment in WSN is presented in the section 2.1. The proposed analytical energy estimation model is detailed in Chapter 3

and its validation is in section 5.1. For the digital part, recursive holonic and MAS architectures are compared in section 2.2. The proposed recursive MAS architecture is detailed in Chapter 4

and its software application and validation in section 5.2.

Chapter 2 The state of the art on physical/digital part

... 59

To address the mentioned research issues: energy saving and network organization in WSN; energy management of physical part in MAS, this chapter presents the state of the art on the physical part in section 2.1 and on the digital part in section 2.2.

To extend the network lifetime and build an analytical energy model for the physical part, energy saving solutions are reviewed. We focus especially on three aspects: data reduction, energy-efficient routing and time synchronization in section 2.1.1, section 2.1.2 and section 2.1.3 respectively. Besides, energy consumption models are reviewed in section 2.1.4 to build an analytic energy model for remaining energy prediction. As a conclusion, a synthesis for the physical part literature is given in section 2.1.5.

On the digital part literature, our objective is to build a recursive architecture for the energy management of communicating concrete. To achieve this goal, representative holonic control architectures are introduced in section 2.2.1, multi-agent system (MAS) paradigms and main adapted works are given in section 2.2.2. Due to the CM composition/decomposition property, the supporting MAS needs to support of recursive functions. Therefore, recursive holarchy or multi-agent architectures are presented in section 2.2.3. Finally, a general conclusion on the state of the art is presented in section 2.3.

Physical part: Energy saving and estimation in WSN

To better understand energy consumption of sensor nodes in WSN, it is necessary to detail the energy consumption of their components. A sensor node has three main components (see Figure 17):

• Sensor: it measures physical phenomena of surrounding environment, for example, temperature, humidity, corrosion, etc. Analogical sensed data are then converted into digital data and transferred to the processor.

• Central Processing Unit (CPU): it is the core part of the sensor node which manages the functionalities of all components (sensors, communication module and so on).

Thanks to its memory and information processing capabilities, monitoring data can be stored locally or transmitted to other nodes via the communication unit.

• Communication unit: it has two main functions, receiving and transmitting data from/to other nodes. As described by [START_REF] Rault | Energy-Efficiency in Wireless Sensor Networks[END_REF], there are five energy efficient ways to extend WSN lifetime as shown in Figure 18 [START_REF] Rault | Energy-Efficiency in Wireless Sensor Networks[END_REF] Over last decades, some research works focused on reducing the energy cost of radio, using adaptive power control [START_REF] Teng | Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks[END_REF], or energy efficient radio [START_REF] Naeem | Energy-efficient cognitive radio sensor networks: Parametric and convex transformations[END_REF].

Meanwhile, lot of research focus on improving communication efficiency at nodes [START_REF] Alfayez | A Survey on MAC Protocols for Duty-cycled Wireless Sensor Networks[END_REF]. To do so, some approaches as in-network processing techniques try to minimize the number of generated messages and data reduction functions are implemented at intermediate nodes to reduce the size of transmitted messages [START_REF] Fasolo | in-Network Aggregation Techniques for Wireless Sensor Networks: a Survey[END_REF]. At the same time, some approaches reduce active duration at nodes by optimizing communication schedule [START_REF] Pal | A Survey on TDMA-based MAC Protocols for Wireless Sensor Network[END_REF] [START_REF] Afroz | Energy-efficient MAC protocols for wireless sensor networks: A survey[END_REF]. Besides, some approaches based on WSN topology help to reduce gathering path from source nodes to the sink node [START_REF] Ogundile | A survey on an energy-efficient and energy-balanced routing protocol for wireless sensor networks[END_REF]. In addition, some works focus on power supply to overcome the limitation of resource, such as energy harvesting techniques proposed in [START_REF] Kaur | Recent Advances in MAC Protocols for the Energy Harvesting Based WSN: A Comprehensive Review[END_REF] and [START_REF] Loubet | LoRaWAN batteryfree wireless sensors network designed for structural health monitoring in the construction domain[END_REF].

The methods for radio optimization and battery repletion focus on reducing the energy needs of hardware (the design of efficient communication module and the processor). However, the used hardware depends on its application. In our study, we would like to propose a generic energy saving solution which can be applied for all constrained applications. As consequence, instead of studying energy saving for a special kind of hardware, we focus on optimizing the energy cost of communication by applying in-network data processing techniques. [START_REF] Kalpakis | A combinatorial algorithm for the maximum lifetime data gathering with aggregation problem in sensor networks[END_REF] states that Maximizing Lifetime using Data Aggregation (MLDA) requires to take simultaneously into account the data aggregation technique, routing protocol and time synchronization as shown in Figure 19. As a result, the main works proposed in the three aspects are described hereafter. As said earlier, energy optimization could also require monitoring the energy consumption. Energy models related to node and data collection are also reviewed in the following.

Figure 20 Data aggregation example

Aggregation functions can be classified into two main approaches depending on the processing way: lossy data aggregation or lossless data aggregation. Basic lossy aggregation functions are simple operations on data like "Average, Max or Min" etc. These operations reduce transmission cost, but this reduction is obtained by losing data accuracy [START_REF] Cui | Data Aggregation in Wireless Sensor Networks[END_REF]. For example, a network monitors and reports average temperature of its nodes and only the average temperature is sent. At the end, the sink node only knows the average temperature of the network. The original information can not be recovered.

In contrast, data collection with lossless aggregation compresses or merges data while preserving all original data. In general, the packet format for communication contains two parts (header and data) as shown in Figure 21. The header part contains numbering and addressing, for example, node ID, request ID, addresses and etc... The sensor reading part contains sensor data. In the case of a periodic request, most of the information in the packet header is the same with all reads, and therefore can be shared for data collection with lossless aggregation method (data fusion) as in (Jun-Zhao and Jiehan, 2008). However, the energy cost for transmission is higher because there are more data to be transferred.

Figure 21

The packet formats in application OscilloscopeRF (Jun-Zhao and Jiehan, 2008) Data compression is another reduction technique, it reduces the size of data by using compression algorithms [START_REF] Sheltami | Data compression techniques in Wireless Sensor Networks[END_REF]. Although energy cost for data transmission is reduced with compression method, accessing memory during the compression process increases runtime at nodes. Therefore, the selection of appropriate compression algorithm depends on the accuracy requirement.

Besides, some predictive models are used for practical monitoring application (J. Lue et al., 2010) [START_REF] Wei | Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter[END_REF]. The idea is to use received monitoring data to predict new data when the required accuracy of predictive is sufficient. For example, as described in [START_REF] Cui | Data Aggregation in Wireless Sensor Networks[END_REF], sensor nodes do not need to send all the raw data if the sink can recover the high-fidelity data from the predictive models.

To summarize, all the mentioned reduction methods aim to reduce the size of transmitted data to extend network lifetime. Although communication activities are decreased with data reduction techniques, energy cost for data processing (fusion or compression) could not be ignored. The trade-off between processing power requirement and energy efficiency is shown in Figure 22. Compared with data aggregation and data compression, data prediction is an out network processing method which has higher processing power requirements and is generally performed in cloud. Although prediction or compression methods could greatly reduce the size of transmitted data and so the radio activity duration, the time for message accessing and processing could not be ignored. This could also increase energy cost and shorten the network lifetime.

Figure 22 Tradeoff of data reduction functions

In a resource constrained environment, the energy supply of node is limited. As consequence, the trade-off between energy efficiency and processing power requirement should be considered for selecting suitable data reduction functions (Jun-Zhao and Jiehan, 2008) [START_REF] Sheltami | Data compression techniques in Wireless Sensor Networks[END_REF] [START_REF] Cui | Data Aggregation in Wireless Sensor Networks[END_REF].

Data aggregation energy-efficient routing protocols

A short gathering path helps to reduce the transmission cost from source nodes to BS. As a result, many routing protocols for data collection have been proposed since the last two decades.

Concerning the data gathering structure, routing schemes can be classified in two categories: flat routing and hierarchical routing (Khedre et al., 2021). In flat-based routing, messages are flooded between sensors nodes until reaching the sink. In the second one, message transmission follows a hierarchical structure, which allows to define guided paths to the BS and limit the energy consumption of the network. There are three kinds of structures used for hierarchical topology as shown in Figure 23: cluster-based, chain-based and tree-based routing structures.

Figure 23 Hierarchical data collection structures in WSN

In a chain-based WSN, nodes transmit information to BS through a linear structure [START_REF] Lindsey | Data gathering algorithms in sensor networks using energy metrics[END_REF] [START_REF] Rani | Energy efficient chain based cooperative routing protocol for WSN[END_REF]. In a cluster-based architecture, few nodes are elected as cluster head (CH) by a selection algorithm for each cluster. Cluster heads aggregate information from other cluster members and transmit aggregated data to BS [START_REF] Heinzelman | An application-specific protocol architecture for wireless microsensor networks[END_REF] [START_REF] Braman | A Comparative Study on Advances in LEACH Routing Protocol for Wireless Sensor Networks : A survey[END_REF]. In a tree-based structure, nodes transmit information along a tree structure as in [START_REF] Tan | Power efficient data gathering and aggregation in wireless sensor networks[END_REF] [START_REF] Han | A general self-organized tree-based energy-balance routing protocol for wireless sensor network[END_REF]. Some representative routing protocols from the three structures, adapted to data aggregation, are presented and discussed hereafter.

Cluster-based routing protocols

LEACH (Low Energy Adaptive Clustering Hierarchy) is the first proposed cluster-based protocol [START_REF] Heinzelman | Energy-efficient communication protocol for wireless microsensor networks[END_REF]. As illustrated in Figure 24, it is a distributed and selforganized clustering protocol. A random mechanism is used to rotate the CHs in order to balance the energy consumption.

Figure 24 LEACH routing protocol

LEACH works with two phases per round: setup phase and steady state phase. During the setup phase, all nodes organize themselves into clusters. A distributed probabilistic approach is used to elect cluster leader. The threshold Pi(t) is defined as in (1):

𝑃 𝑖 (𝑡) = 𝑘 𝑁-𝑘 * (𝑟 𝑚𝑜𝑑 (𝑁/𝑘)) * 𝑐 𝑖 (𝑡) (1)
Where k is a predetermined cluster number, N is the total number of nodes, r is the current round number, i is the node's identification (ID) and ci(t) is a function which indicates if the node has already been a CH in a previous round. For each round, sensor nodes randomly pick a number from 0 to 1, and then compare the picked value with the threshold. A node acts as leader if its picked number is bigger than the threshold. This probabilistic approach balances energy consumptions within clusters. When a CH is elected, it broadcasts a message to announce that it is the cluster leader. Surrounding nodes then decide the cluster to join, the decisions depending on the signal strength of received messages. The second phase of the process is data collection.

All non-headers transmit data to its CH. Once cluster leaders receive all data from their cluster members, they can then transmit data directly to the sink. This algorithm improves the lifetime of network, but the single-hop routing is inefficient for a large network. The leaders who are far from the sink consume a lot of energy. Furthermore, aggregation function could be implemented at cluster-heads to reduce transmission cost. Based on LEACH, the authors also proposed a centralized method LEACH-C where the clustering of nodes is performed at the sink to improve the performance [START_REF] Lindsey | Data gathering algorithms in sensor networks using energy metrics[END_REF].

Unlike the random election of leader in LEACH, Hybrid Energy-Efficient Distributed clustering (HEED) takes residual energy and communication cost into account [START_REF] Younis | HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks[END_REF]).

An Average Minimum Reachability Power (AMRP) is defined for communication cost estimation. It is the average of minimum power levels for the connection from non-leader nodes to its CH. In each round of data collect, every node computes its probability PCH which is defined as in [START_REF]'énergie restante du réseau dans un environnement à ressources limitées? Pour prédire l'énergie restante, des modèles de consommation doivent être établis. Est-il possible de construire un modèle générique ou est-il nécessaire de spécifier des modèles énergétiques pour chaque type de structures RCSF?[END_REF]. A node becomes the leader if its PCH reaches 1.

𝑃 𝐶𝐻 = 𝐶 × 𝐸 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝐸 𝑚𝑎𝑥 (2)
Where C is the defined percentage of cluster heads, Eresidual is the current residual energy, and

Emax is the initial energy. Once the leaders are elected, the surrounding nodes can then choose a CH with the lowest AMRP cost. Compared with LEACH, this residual energy-based election mechanism avoids excessive energy consumption of some nodes and thus extends network lifetime. An improved algorithm, DWEHC (Distributed Weight-based Energy-efficient Hierarchical Clustering protocol) is proposed by [START_REF] Gupta | Load-balanced clustering of wireless sensor networks[END_REF]. Like HEED, the residual energy is accounted in DWEHC. Within cluster, the no-leader nodes use the knowledge of distance to decide to be a single-level (transmit data directly to CH) or multi-level member (transmit data via another member to CH). This location awareness balances the cluster size and improves the energy efficiency.

Over the last two decades, some LEACH-based extensions have been proposed to improve energy efficiency [START_REF] Singh | A Survey on Successors of LEACH Protocol[END_REF]. Such as TL-LEACH in [START_REF] Loscrì | A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH)[END_REF], LEACH-CC in (Yassein et al., 2009), W-LEACH in [START_REF] Abdulsalam | W-LEACH: Weighted low energy adaptive clustering hierarchy aggregation algorithm for data streams in wireless sensor networks[END_REF]) and so on. The main differences between cluster-based protocols can be summarized in three aspects: the selection mechanism for the CHs, the inter-communication between CHs and the sink, the intracommunications between the cluster members and their CH.

Tree-based routing protocols

Another widely used approach in WSN is the tree-based topology, where sensor nodes transmit data following a tree structure network via relay nodes to the sink. Processing operations such as data aggregation can be performed at intermediate nodes to reduce data transmission and save energy.

The Tiny AGgregation (TAG) approach is a data-centric tree-based protocol [START_REF] Madden | TAG: A Tiny AGgregation Service for Adhoc Sensor Networks[END_REF]. A declarative interface is used for data collection and aggregation. With a given query, relevant information is sent to the sink periodically. This algorithm has two phases: construction phase and collection phase. In the first phase, the sink broadcasts a message which contains the distance to sink. When a node (not already in the tree) receives a message, it sets the sending node as its parent and then broadcasts again the same message with its own ID and distance. After the tree is constructed, a semantic query which specifies required data (such as temperature or humidity) is sent from the sink to all nodes. Relevant nodes send data to its parent then go to sleep. A parent node fuses all received data with its own and transmits the aggregated message. This in-network aggregation processing algorithm reduces transmission at intermediate nodes and improves network lifetime. However, it is not robust to link failure and re-organization is highly energy consuming.

An Energy-Aware Distributed Aggregation Tree (EADAT) is proposed in [START_REF] Ding | Aggregation tree construction in sensor networks[END_REF].

This approach combines residual energy of nodes and their number of hops (or distance) to the sink to construct the tree. A control message which contains five fields (ID, parent, power, status, hopCnt) is defined for construction. Where ID and parent record the node's identification, and its parent node respectively; power represents its residual energy; status indicates the state of the sending node in the network (undefined state, leaf node, non-leaf node, or danger state);

hopCnt is the number of hops to reach the sink. At the beginning of the construction process, the sink node broadcasts an initial control message. A node v sets up its timer after receiving the first message. Based on the received message, the node v chooses a neighbor as its parent which has higher residual power and shortest path to the sink. It then broadcasts a message with its own hopCnt. All nodes will broadcast their message and find their corresponding status in the network until the construction of tree is completed. Compared with TAG, residual energy is taken into account to increase robustness and to reduce maintaining cost. However, due to the lack of a global view of whole network, it is hard to ensure that gathering paths in this distributed construction tree is optimal.

A Power Efficient Data gathering and Aggregation Protocol (PEDAP) was proposed by Tan in [START_REF] Tan | Power efficient data gathering and aggregation in wireless sensor networks[END_REF]. It is a near optimal minimum spanning tree-based (MST) protocol.

In this approach, the computation of the routing tree is completed in a centralized manner at the BS. To construct the MST, the edge eij between nodes i and node j is computed by the equation [START_REF]réaliser le lien entre les matériaux communicants avec leurs agents dédiés, ainsi que la gestion des données et de l'énergie des noeuds chez ces agents? La partie physique est liée à une partie digitale représentée dans cet mémoire par un système multi-agents (SMA)[END_REF]. The latter presents communication cost between these two nodes.

𝐶 𝑖𝑗 = 2 × 𝐸 𝑒𝑙𝑒𝑐 × 𝑘 + 𝐸 𝑎𝑚𝑝 × 𝑘 × 𝑑 𝑦 2 (3)
Where 𝐸 𝑒𝑙𝑒𝑐 is energy consumption of transmission or reception, 𝐸 𝑎𝑚𝑝 is consumption of transmit amplification, k is the size of packet and d is the distance. The construction begins with the root (the sink node). An MST example is shown in Figure 25. For each iteration, the root adds the vertex which has the minimum weighted edge into the tree. At the end, a network with a minimal weight route is constructed. However, the runtime complexity of network construction can not be ignored in this approach, which may increases maintaining cost, especially for a large network, it is equal to O(n 2 ) where n is the size of the network. The larger the network, the longer its network construction time. In order to improve the robustness of this structure, a power aware version PEDAP-PA which takes residual energy of node into account has been proposed. In PEDAP-PA, the weight is replaced by 𝐸𝐶 𝑖𝑗 (𝑘) = 𝐶 𝑖𝑗 (𝑘)/𝑒 𝑖 where ei is the normalized residual energy of node i.

Figure 25 Minimum spanning tree-based routing protocol

These mentioned tree-based protocols aim to construct an efficient gathering tree based on different aspects: distance, residual energy, the number of hops to sink and the total gathering path. There are many other solutions that have been proposed for tree construction in a distributed or a centralized manner, like the use of Ant Colony Optimization in [START_REF] Lu | Dynamic data aggregation protocol based on multiple objective tree in Wireless Sensor Networks[END_REF], the residual energy considered tree in [START_REF] Virmani | Adaptive Energy Aware Data Aggregation Tree for Wireless Sensor Networks[END_REF], the shortest path tree for dynamic sink application in [START_REF] Hawbani | Sinkoriented tree based data dissemination protocol for mobile sinks wireless sensor networks[END_REF].

Compared with cluster-based, tree-based protocols aim to optimize energy consumption over whole network and network construction is always completed at sink node. However, maintaining energy cost is higher than in cluster-based structures. When an intermediate node runs out of energy, tree-based approach may need to completely reconstruct the network where only one part of the network has to be reconstructed with a cluster-based approach. Apart from these two kinds of structure, chain-based structure has also been widely applied. Some examples are given in following section.

Chain-based routing protocols

In a chain-based routing protocol, data are transmitted from the furthest node to the BS through a line structure. Data aggregation can be performed at intermediate nodes to reduce communication cost and extend network lifetime.

The first historical chain-based routing protocol using data aggregation is PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [START_REF] Lindsey | Data gathering algorithms in sensor networks using energy metrics[END_REF]. In this protocol, the greedy algorithm is used for chain construction. It selects closest neighbor as next hop. During the data collection phase, each node takes turn to act as the leader with the probability 𝑖 𝑚𝑜𝑑 𝑛 where i is the node number and n is the number of nodes. Data collection begins with one of the end nodes (C0 or C4) as shown in Figure 26 and data aggregation is performed at all intermediate nodes. Once the chain leader receives all messages from a side, the collection process is then repeated for the other side. At the end, the chain leader fuses all received data with its own and transmits aggregated message to BS.

Figure 26 PEGASIS chain-based protocol

The random leader selection mechanism used in PEGASIS balances energy consumption at nodes. Besides, it uses in-network data aggregation to reduce the amount of transmitted data.

However, a long chain structure may have a huge impact on the latency and on the delivery time.

CRBCC (Chain-Cluster Based Mixed routing) is a two-layer hierarchical protocol proposed by [START_REF] Zheng | Chain Routing Based on Coordinates-Oriented Clustering Strategy in WSNs[END_REF]. The authors suppose that each node is assigned (x, y) coordinates.

During the construction, the network is divided into balanced clusters by using y coordinates.

In each cluster, the Simulated Annealing (SA) algorithm is performed to construct the lowest energy consumption chain. The initial objective of SA algorithm is to compute the shortest way with the lowest energy consumption. The chain leaders in the low-level are elected in the same

x coordinates order as shown in Figure 27. The SA algorithm is then performed again for the construction of leader-leader chain. At the end of the construction process, a top leader is randomly elected to communicate with the BS. In the phase of data collection, data fusion is performed in both low-level chain and leader-leader chain. Once data collection is completed, the top leader then fuses all data and transmits it directly to BS. This protocol combines the advantages of chain-based and cluster-based protocols to overcome the poor latency and high reconstruction cost in a long chain structure.

Figure 27 CRBCC protocol

To avoid high maintaining cost of long chain structures, a sub-network chain-based routing protocol (BCBRP for Balanced Chain-Based Routing Protocol) has been proposed in [START_REF] Ahn | Balanced chain-based routing protocol(BCBRP) for energy efficient wireless sensor networks[END_REF]. Compared with the previous algorithms, chain construction in BCBRP is more complex. It has four phases: dividing network, selecting bridge, chain construction with MST, and selecting leader. Although data gathering in BCBRP is also along a single chain structure, chain reconstruction takes place only in the sub-network which contains a dead node. Therefore, energy maintaining cost is reduced, and thus network lifetime is extended. However, the chain construction is complex, and the drawbacks of the long-chain structure in terms of gathering delay are not solved.

In order to improve energy efficiency and extend network lifetime, many other chain-based protocols have been proposed. According to [START_REF] Khedr | Successors of PEGASIS protocol: A comprehensive survey[END_REF], recent chain-based solutions can be divided into two categories based on the intra-cluster and inter-chain communication:

first, multi-hop/single-hop; second, multi-hop/multi-hop. As illustrated in Figure 28, first category protocols are in blue color, such as the AI-PEGASIS [START_REF] Ndawi | An Improved Pegasis Protocol for Energy Efficient Wireless Sensor Network by Ant Colony Optimization[END_REF], DLRP [START_REF] Marhoon | Direct line routing protocol to reduce delay for chain based technique in wireless sensor network[END_REF]; and the second category in red, like the EPEGASIS [START_REF] Mahdi | Low Transmission Delay Based on LEACH and PEGASIS Protocol in WSN[END_REF] or PEG-ACO in [START_REF] Ramluckun | Energy-efficient chain-cluster based intelligent routing technique for Wireless Sensor Networks[END_REF].

Figure 28 Timeline for chain based routing protocols [START_REF] Khedr | Successors of PEGASIS protocol: A comprehensive survey[END_REF] Energy saving for data gathering in WSN is still a hot research issue. In this thesis, we can not review all solutions because new protocols are proposed every week. Instead of, we would like to find out which kind of structure is suitable for communicating concrete application and for building analytical energy consumption models. Since the behavior of communicating concrete and applicative needs can be different throughout its whole lifetime, a unique routing protocol should not be suitable for all CM lifecycle phases.

During manufacturing, the communicating concrete is mainly used to monitor its status, each element may work independently. As the network of CNs within a single concrete element is not large, a simple and efficient data aggregation protocol will be preferred. For construction phase, the problems are not only the minimization of energy within the concrete, but also the adaptation of network when new concrete elements arrive. Thus, a data aggregation technique favoring dynamic and fast network reconfiguration should be privileged. After the building construction, a large network will be completed and used for the exploitation phase.

Consequently, a robust topology with a low maintaining cost protocol should be more appropriate.

To sum up, the required performance metrics for data aggregation routing protocols are described as follows: energy efficiency which represents the algorithm efficiency for data collection. It is computed by the amount of consumed energy by the number of received messages; Maintaining cost shows its energy needed for the reconstruction of the architecture;

Resilience to link failure indicates its robustness facing the loss of link when a node dies;

Scalability stands for its capability to scale the scenario. The different types of structures presented above are compared with the mentioned performance metrics in Table I. The plus and minus are used to represent the strengths and weaknesses of protocols. All mentioned protocols are compared to find which kind of structure is suitable for each lifecycle phase. As mentioned above, the communicating concrete mainly monitors its status during the manufacturing. Therefore, a simple and efficient data aggregation protocol should be preferred. We can see from the table that PEDAP for tree structure and CRBCC for chain structure have high energy efficiency and low maintaining cost. Meanwhile, the construction of chain and tree structure is easier and less energy intensive than for cluster, therefore, a chain or tree structure may be preferred for the manufacturing.

During building construction, concretes should "cooperate" and the network structure could be modify. Thus, a data aggregation technique favoring dynamic and low maintaining cost network should be privileged. Compared with chain-based structures, cluster-based approaches (like HEED) and tree-based approaches (like TAG) provide a good scalability. However, the high maintaining costs in cluster-based structure are not suitable for this phase. In contrast, PEDAP and TAG (tree-based) have a low maintaining cost with a good scalability and high energy efficiency and thus may be a good choice for the construction phase.

After the construction, a large network will be completed and used for building's exploitation.

An efficient, robust and low maintaining cost protocol should be adapted. CRBCC may have good result, however, the delay delivery drawback of a long chain structure cannot be neglected.

Cluster-based structure have better resilience to link failure than tree-based. An energy efficient cluster-based protocol such as DWEHC may lead to good results for periodical monitoring.

Tree-based routing protocol like TAG should be considered for data-centric applications.

These three types of structures have their own advantages and drawbacks according to the CM lifecycle phase. We will use these different types of structures in communicating concrete lifecycle. A lot of these protocols are based on local decisions taken by nodes. Indeed, each node independently reacts, by considering its own states and its neighborhood, to take routing decisions. This leads to local optimal solutions but could result in suboptimal global ones. These decisions may be more optimal if taken in the digital part, in a centralized manner, that may help to reach a global optimum. Meanwhile, energy cost for inactive state is often ignored in these routing protocols which should be also taken into account for energy estimation. To ensure nodes can wake up at the right moment, some time-synchronization solutions are thus presented as follows.

Time synchronization

As said previously, using sleep mode on radio module allows to save energy at nodes. However, to ensure the communication with sleep mode, the time synchronization problem should be considered. Therefore, some representative time synchronization protocols for WSN are presented and discussed hereafter. MAC protocols can be classified in three groups according to synchronization mechanism: sender-based synchronization model, sender-receiver interaction model and receiver-receiver interaction model. In this section, representative protocols for each model are presented and discussed.

DMTS (Delay Measurement Time Synchronization

) is one of the Sender-based synchronization models [START_REF] Ping | Delay measurement time synchronization for wireless sensor networks[END_REF]. For a one-hop network, fewer messages are transmitted.

The synchronization message that contains the global time t is broadcasted by the BS. Each child node records the message arrival time t1 and the time before adjusting its clock t2. Besides, the delay for transmission te can be estimated by the transmission rate of radio module, such as 20 kbps with Mica. Therefore, the delay tdelay is presented as in [START_REF]Cela pourrait être possible au niveau physique et au niveau digitale. Pour construire ce mécanisme, les interactions entre agents doivent être considérées[END_REF]. Child nodes then adjust their local times to the global time with measured path delay.

𝑡 𝑑𝑒𝑙𝑎𝑦 = 𝑡 𝑒 + (𝑡 2 -𝑡 1 ) (4)
This one-hop method can achieve high energy efficiency. However, it is hard to ensure that each node can directly communicate with the BS. Therefore, the authors also discussed the multi-hop version. It uses source levels to identify the distance from the master to another node.

Source level of the leader is zero. The synchronization process works as follows: first, a leader selection algorithm is used to select a time master. The leader then broadcasts its time to other nodes. When receiving a time signal, a node checks the level of the time source n. If it is from a source of lower level than itself, it accepts the time and set its source level to n + 1. Otherwise, it silently discards the signal. The root node periodically broadcasts its time. All nodes are synchronized by the multi-hop method.

Reference Broadcast Synchronization (RBS) was proposed by Elson et al. [START_REF] Elson | Fine-Grained Time Synchronization Using Reference Broadcasts[END_REF].

Unlike the previous one, this method is based on a receiver-receiver interaction. In this protocol, the reference node (call sender in Figure 29) broadcasts a reference packet to others. Timing-Sync Protocol for Sensor Networks (TPSN) is a sender-receiver interaction method proposed by Ganeriwal [START_REF] Ganeriwal | Timing-sync Protocol for Sensor Networks Categories and Subject Descriptors[END_REF]. This protocol is inspired by the NTP (Network Time Protocol) which has been widely used in Internet. A two-way time exchange method is used for synchronization as shown in Figure 30. Node A first sends a synchronization message that contains its time T0. Node B records the received time T1 that is the sum of T0, α and d.

Here, α represents the clock drift between the two nodes, d is the propagation delay. B then sends back an acknowledgement message that contains the three times T0, T1 and the sending time of message T2.

Figure 30 Synchronization between two nodes in TPSN

Node A records the received time T3, it can then calculate the clock drift and the propagation delay with the four timestamps as in ( 7) and ( 8):

𝛼 = (𝑇1 -𝑇0) -(𝑇3 -𝑇2) 2 (7) 𝑑 = (𝑇1 -𝑇0) + (𝑇3 -𝑇2) 2 (8)
The authors also introduce the notion of real time for error analysis. All packet delays at sender, propagation of messages and receiver are taken into account. Compared with RBS, this method gives better accuracy, but it consumes more energy. In the following section, energy models related to node and data collection are presented.

Energy consumption models for WSN

The distance-based dissipation First Order Radio Model (FORM) in [START_REF] Heinzelman | An application-specific protocol architecture for wireless microsensor networks[END_REF] has been widely used for energy consumption estimation. It is based on the distance between a transmitter and a receiver and on the size of the data to be transmitted (see Figure 31). In this model, the transmission cost 𝐸 𝑇𝑥 of a k-bit message with distance d is given by ( 9).

𝐸 𝑇𝑥 (𝑘, 𝑑) = { 𝑘𝐸 𝑒𝑙𝑒𝑐 + 𝑘 ∈ 𝑓𝑠 𝑑 2 , 𝑑 < 𝑑 0 𝑘𝐸 𝑒𝑙𝑒𝑐 + 𝑘 ∈ 𝑚𝑝 𝑑 4 , 𝑑 > 𝑑 0 (9)
Where,

• 𝐸 𝑒𝑙𝑒𝑐 is the transmitting circuit energy loss,

• ∈ 𝑓𝑠 and ∈ 𝑚𝑝 are the amplifier energy for free space and multipath fading respectively,

• 𝑑 0 is the distance threshold.

The receiver energy consumption depends only on message size k: 𝐸 𝑅𝑥 (𝑘) = 𝑘𝐸 𝑒𝑙𝑒𝑐 . This model has been widely used to estimate radio node energy consumption [START_REF] Younis | HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks[END_REF]) [START_REF] Jan | Energy evaluation model for an improved centralized clustering hierarchical algorithm in WSN[END_REF]. However, some authors argue that the cost related to state transitions of the communication module should also be taken into consideration [START_REF] Margi | Instrumenting network simulators for evaluating energy consumption in power-aware Ad-hoc network protocols[END_REF]) [START_REF] Howitt | Extended energy model for the low rate WPAN[END_REF]. Some energy consumption evaluation models or lifetime estimation models have been proposed

for data gathering in WSN. For example, the energy evaluation model proposed by Jan for data gathering in centralized clustering structure [START_REF] Jan | Energy evaluation model for an improved centralized clustering hierarchical algorithm in WSN[END_REF]. The authors propose a cluster structure which has three types of nodes: the sink node, high-energy nodes as cluster leader and sensor nodes. They apply the FORM model to estimate the energy consumption of the three types of nodes. The FORM model is also used in [START_REF] Ren | Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks[END_REF] to estimate the entire network lifetime. For a given network, the authors analyze traffic load to compute the number of data packets to be transferred at nodes, which is then used to estimate energy consumption and calculate network lifetime.

Although the FORM has been widely applied for network lifetime estimation, the distance is not always available in some applications and energy consumption in sleep or idle should be also considered. [START_REF] Dron | An emulation-based method for lifetime estimation of wireless sensor networks[END_REF] present an emulation-based method for lifetime estimation.

The authors use MSPSim/Cooja emulator to obtain energy consumption information of different devices as shown in Figure 33. This energy information is then used in a network simulator to estimate energy consumption in a special data gathering structure.

Synthesis

Using data reduction techniques at nodes allow to reduce transmission messages and extend network lifetime. Each technique has advantages and drawbacks. Although approximation or compression methods could greatly reduce the size of transmitted data, the time for message accessing and processing can not be ignored which increases the energy cost at node. Data aggregation techniques reduce the size of transmitted data, but all data may not be reconstructed in the case of lossy aggregation. It is the trade-off between accuracy and energy efficiency. Due to the limitation of battery and processing power of node, data aggregation methods will be preferred for the application of communicating concrete. This approach should reduce transmitted data but does not significantly increase the cost of data processing.

Grouping nodes in a short gathering path reduces transmission cost from source nodes to the BS. The chain-based approach is easy to implement and very adapted for small networks, but it may cause a long delay. The strong scalability of tree-based protocol makes the autoorganization of communicating concretes possible for the construction phase. However, low resilience to the link failure, and high maintaining cost are unavoidable drawbacks. Clusterbased approach provides a robust network and low delay for a large network, it may be a good choice for the building exploitation phase.

The mentioned time synchronization protocols enable time scheduling which provides an efficient way of using radio sleep mode at nodes. In our applicative case, the network within one McBIM element is not so large. Due to the influence of material, the communication range in concrete is shorter than that in the air. As consequence, a send-receiver based method is more suitable than the two other types. Based on this approach, processes for collection in a chain structure network with or without aggregation are analyzed and models for energy consumptions are constructed in next chapter.

Distance-based energy models are widely used for the network structuration. Distance between nodes is not always suitable for WSNs, especially for distributed algorithms, because positioning is an intensive-energy task for sensor nodes. Most of time, positioning in WSNs is not possible or energy unrealistic. For the communicating concrete application, even if positions of nodes can be known because they can be manually deployed, classical propagation models (free space or multipath models) can not be applied because sensor nodes are placed in concrete (heterogeneous environment).

In conclusion, to extend embedded network lifetime, different routing protocols may be applied over the whole lifecycle of communicating concrete. Using in-network data aggregation reduces communication consumption, and synchronization method should be considered for ensuring the communication between nodes. Both these energy saving solutions are considered for building the dedicated energy model which is detailed in chapter 3. To overcome the limitation of processing power and storage capability at nodes, intelligent management solutions can be added to improve further energy efficiency of the network. Representative approaches are detailed and discussed in the following section.

Digital part: energy management for communicating concrete

To realize the holonic architecture for the communicating concrete, this section begins with the representative traditional holonic control architectures in section 2.2.1 as well as multi-agent architectures for building the informational part of holon in section 2.2.2. Due to the composition characteristic of communicating material and the high flexibility requirement throughout its whole lifecycle, recursive architectures are reviewed in section 2.2.3 to find or design a recursive architecture for the communicating concrete application. At the end, a synthesis is given in section 2.2.4.

Holonic Control Architecture (HCA)

In the manufacturing field, the flexibility of the control system has always been required to adapt to environmental changes. One of the first holonic manufacturing architecture, PROSA (Product-Resource-Oder-Staff) is proposed by Van Brussel (Van Brussel et al., 1998). This architecture consists of three types of basic holons: order holons, product holons, and resource holons. The general architecture is shown in Figure 34. The authors introduced staff holons which can be added to assist the basic holons to complete their work. Holons can cooperate to achieve adaptation and flexibility requirements.

Figure 34 PROSA architecture ( Van Brussel et al., 1998) Holons are structured by using object-oriented concepts. The resource holon contains a physical part which is production resource and an informational part that controls the resource; The product holon holds the process and product knowledge; The order holon assigns tasks to other holons to ensure the production.

These holons cooperate by exchanging the manufacturing system knowledge. The order holons communicate production knowledge with the product holons, which can then exchange process knowledge with the resource holons to produce the required order. Meanwhile, the resource holon shares the process execution knowledge with the order holon. Based on the process execution knowledge, the order holon can ensure the production on time. The staff holon has an overview of all resources and all orders, it generates a schedule as advice to the order holons and resource holons. These basic holons can follow the advice when there are no disturbances.

However, once the disturbances or changes occur in the system, the basic holons can ignore the advices from the staff holons and make their own decisions.

ADACOR (ADAptive holonic COntrol aRchitecture) is a nature-inspired holonic architecture proposed by [START_REF] Leitão | ADACOR: A holonic architecture for agile and adaptive manufacturing control[END_REF]. A pheromone-like propagation mechanism is used to distribute information when disturbances occur. In this architecture, four holons are defined:

product holon, task holon, operational holon and supervisor holon. These holons are structured in a decentralized control architecture to adapt to environmental changes. The defined holons are quite similar from PROSA. Product, task, and operational holons have the same functionalities as the product, order and resource holons in PROSA. These holons cooperate via an architecture as shown in Figure 35. In this approach, the supervisor holon acts an important role because it is responsible for forming and coordinating groups of holons.

In this architecture, holons are organized in a hierarchical structure with four levels: planning, for Isoarchy Structure) [START_REF] Pujo | PROSIS: An isoarchic structure for HMS control[END_REF]. This architecture inherits from PROSA. It uses the concept of isoarchy to define a flat holonic form where holons are in a unified level. This decentralized architecture is designed to increase the productivity, the flexibility and the responsiveness of the production system. In this unique decision level structure, the staff holon is replaced by a Simulation Holon, which is designed to evaluate the prospects for evolution of the production system and prevent emergences.

These holonic control architectures present the approaches to manage the resource of a product (physical part) and the information (digital part). Holons are organized in hierarchies, which can evolve according to certain mechanisms. This ability confers to these architectures some adaptation features. To realize the informational part, Multi-Agent Systems are generally used for the management of the digital part.

Multi-Agent System (MAS)

In the past two decades, multi-agent system has attracted a lot of attention. Agents can work independently and cooperate to resolve complex tasks. Due to its high flexibility, the agent paradigm has been widely applied in different areas [START_REF] Sardouk | Crisis management using MAS-based wireless sensor networks[END_REF]) [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF].

According to Dorri [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF], there are seven different types of organizations as shown in Figure 36: Flat (a), Hierarchical (b), Holonic (c), Coalition (d), Team (e), and Matrix (f) and Congregation (g). The flat is the most basic organization structure, in which all agents can communicate with their neighbors. The agents with common objectives can directly cooperate.

In a hierarchical organization, agents are grouped in a tree-like structure, leaf agents can communicate with others via their parent agent [START_REF] Ma | Hierarchical social network analysis using multi-agent systems: A school system case[END_REF]. As the parent agent has knowledge of its children, a local optimal decision can be provided. Meanwhile, the root agent which has a global view could provide optimal solution to face environmental changes. In holonic organization, the agents are considered as the informational part of holons as in [START_REF] Esmaeili | A socially-based distributed selforganizing algorithm for holonic multi-agent systems: Case study in a task environment[END_REF]. The holons with the same interest or with same features can be grouped into a higher layer holon. Agents with the same parent can communicate with each other.

In the four other kinds of organization (Team, Coalition, Congregation and Matrix), agents are temporarily or permanently gathered into groups based on their goal. For example, an agent can be part of more than one group in coalition structure [START_REF] Manisterski | Enhancing MAS cooperative search through coalition partitioning[END_REF], and the different groups communicate with the others via a flat structure to achieve a common goal.

Figure 36 Organization approach of agents: Flat (a), Hierarchical (b), Holonic (c), Coalition (d), Team (e), Matrix (f) and Congregation (g) [START_REF] Dorri | Multi-Agent Systems: A Survey[END_REF] In matrix organization, each agent is controlled by at least two other agents. This multiple supervision could reduce the error rate during production [START_REF] Meszyński | Modeling of Complex Multiagent Behavior Using Matrix Representation Modeling of Complex Multiagent Behavior Using Matrix Representation[END_REF]. It is inspired from human society where a person can be guided by a supervisor and a team leader.

However, the mentioned structures define relative static cooperation between agents but only one (the holonic organization) facilitates the dynamicity of the structure and the use of recursive patterns, which are mandatory for communicating concrete application. In the Holonic or MAS communities, several attempts were made to address recursion in different ways. The recursive solutions are reviewed in the following section. is a sub-system of S11. In this hierarchical structure, each level takes the results from lower layer into account and reports results to upper layer. This approach presents the information exchanges on the same level and the recursion for different levels. This work describes a modeling framework but does not detail the composition and decomposition mechanisms needed to manage a dynamic system.

Since the informational part of holon is always realized by agents, recursive agent architecture can be considered for the digital part of our application. A recursive agent meta model (RAMM) was proposed by [START_REF] Vo | An operational meta-model for handling multiple scales in agent-based simulations[END_REF] for multi-scale modeling (MSM). This meta-model has four main parts: species, agent, environment, and scheduler. Agents of different species can form a hierarchical structure. For example, a root agent located at level 0 consists of two agent species on level 1 (species 1 and species 2). At level 2, species 3 is a micro species of species 1 on the level 1. The authors define the capture and release methods to add or remove micro species.

However, the interactions between agents are not detailed.

Abdoos et al. use holonic multi-agent system to model a large traffic network [START_REF] Abdoos | Holonic multi-agent system for traffic signals control[END_REF]. Different intersection controllers and regions are represented by holons in a two-level hierarchical architecture. Each intersection controllers are represented by a holon in the first level, and region control in the second level. The authors propose the holonic Q-learning method which helps agents to decide their next action based on states to analyze the signal control. Holons in the lower level send the information from the environment to the holons in the higher level, which estimate and provide the actions. The experimental results shows that this method can reduce the average delay. But the aggregation and decomposition of holons are not detailed in this method. [START_REF] Sallez | On the activeness of physical internet containers[END_REF] present a recursive holarchy in logistics for Physical-Internet using (PI)containers. These containers have some communicational and decisional capabilities.

According to their size and function, three types are defined from the largest to the smallest: transport container, handling container and packaging container. Encapsulation and composition concepts are defined for each kind of container. Indeed, containers of the same type can be composed as a "composite" PI-container. Small containers (handling and packaging) can be encapsulated in a bigger one. However, the compositional mechanisms are not formally specified here too.

In Esmaeili et al. [START_REF] Esmaeili | A socially-based distributed selforganizing algorithm for holonic multi-agent systems: Case study in a task environment[END_REF], recursive holarchies are used to build and manage holonic structures for large-scale MAS (LS-MAS). The four-level holarchy representation is illustrated as in Figure 38 where the heads of holons are shown in red. The holons can communicate with the ones in the same level, or with holons in upper or lower levels through the heads of the holons. The authors detailed the location information-based algorithm to construct the holarchy, the head selection algorithm among holons, and the reorganization mechanism for the architecture. This model can be used well with large-scale multi-agent systems. Task assignment application is evaluated to prove the usefulness of recursive architecture. The hierarchical MAS-R structure is illustrated in Figure 39. There are three levels. At the level 0, agents a0 i are "Elementary" agents which have a physical applicative part and a recursive agent part. The agent ai j represents the agent j on the i th level. These agents on high levels (i is greater than zero) are called "Partial" agent. A "Complete Elementary" agent is composed by its elementary agent and all theirs directly related "Partial" agents (from abstraction levels).

"Composed" agents are the gathering of few "Partial" agents from the same abstraction level.

Figure 39 The MAS-R architecture

The authors presented four types of transformations functions for each component of a MAS (Agent, Interactions, Organization and Environment) respectively PA, PI, PE and PO:

• PA is an operator grouping agents from one level and associating them to an agent of the upper level;

• PE is an operator grouping elements of the environment from one level into an element of the environment from the upper level;

• PI is an operator transforming interactions from agents of a level to interactions to the upper level. Perceptions/actions and messages of agents of a level N are transformed thanks to PI to perceptions/actions and messages of agents of level N+1.

• PO is an operator that transforms relations between agents. A relation between agents of level N grouped in different agents of level N+1 is transformed by PO into a relation between agents of level N+1.

Besides, the authors also detailed the composition mechanism for this recursive system. Once composition conditions are met, a composed parent agent can be created and other agents are invited to join it.

Khelifa and Laouar propose a holonic intelligent decision support system for urban project planning (UPP) [START_REF] Khelifa | A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm[END_REF]. The proposed three-level holonic architecture is shown in Figure 40. At the top level, the project holon, urban area holon and the authority holon share the same resource. The authority holons records the authority relations between the area and its sub-area, the project and the sub-project. These three holons are structured in a

Figure 41 Holonic architecture for microgrid [START_REF] Nurdin | Holonic Multi-Agent System for Microgrid Hierarchical Control[END_REF] The concept of recursion has been discussed or applied in mentioned works. A summary is given in Table II. As we try to define a dynamic recursive architecture for CM applications, From Table II, we can see that the need of recursion is expressed in different applicative areas.

The WSN application is closest to our needs and may provide an adapted architecture. The approach given by (Le [START_REF] Mortellec | Embedded holonic fault diagnosis of complex transportation systems[END_REF] is applied for static systems as well as the proposal of [START_REF] Esmaeili | A socially-based distributed selforganizing algorithm for holonic multi-agent systems: Case study in a task environment[END_REF], and (Khelifa and Laouar, 2020). These approaches are not adapted for dynamic systems, such as the communicating concrete application. Although the aggregation notions (encapsulation and composition) and the interactions between agents are given by [START_REF] Sallez | On the activeness of physical internet containers[END_REF], the authors do not detail the aggregation mechanism. [START_REF] Occello | A multi-level generic multi-agent architecture for supervision of collective cyber-physical systems[END_REF] presents a recursive agent model in MAS-R, and details the interactions and aggregation mechanisms for a dynamic architecture. This method seems to be suitable for CM applications. Thus, this approach is a good candidate for application to the CM context. Agents and holons have much common properties. They can both react independently or cooperate with others to adapt to environmental changes. There are still some differences between holons and agents. One of the most important is that agents have not internal separation between informational and physical part. Barbat et al. [START_REF] Bărbat | Holons and Agents in Robotic Teams A Synergistic approach[END_REF] claim that holonic approach overcomes the agent limitations in the field of forming multi-level hierarchies or creating temporary structures in dynamic environment. The similar opinion was expressed by

Leitão [START_REF] Leitão | Industrial Agents: Emerging Applications of Software Agents in Industry[END_REF], who claimed that "the holonic paradigm, extended by agent norms and policies, offers a starting point for MAS-based systems that also support hierarchical (control) structures and decision making". Vrba et al. [START_REF] Vrba | Rockwell automation's holonic and multiagent control systems compendium[END_REF] go further and use the term "holonic agent".

Synthesis

In this chapter, energy modeling and saving mechanisms to extend WSN lifetime in the physical part and recursive architecture for the management in digital part are both reviewed. In the physical part, three aspects are especially pointed out: collection structure for data gathering, time synchronization protocols for optimizing radio sleep mode and aggregation function for reducing the transmitted data. As these three aspects are important for energy saving during data collection, they should be considered for constructing the analytical energy consumption model. This model should provide an approximate and rapid remaining energy information for the usage and maintaining of the CM products. Therefore, in the following chapter, an analytical energy consumption model for a simple chain structure is proposed in section 3.2 considering data collection without aggregation and with aggregation in section 3.2.1 and section 3.2.2

respectively. Besides, a general analytical model is proposed in section 3.3 for the every kind of hierarchical WSN structures, considering lossy or lossless aggregation.

To overcome the limitation of processing power and storage capability at nodes, the intelligent holonic control system is reviewed for the management of the digital part. To represent the physical part in the digital world, different MAS approaches are analyzed to find solution compatible with our requirements. Due to the composition and decomposition characteristics, a recursive architecture is needed for the communicating concrete application. Since the MAS-R architecture seems to meet our needs, an extension of this approach to the CM context is proposed in the Chapter 4. The recursive architecture and the adapted agent model are presented in section 4.1 and section 4.2 respectively. The composition mechanism, interaction between agents, the cooperation between aggregated elements as well as the application for lifetime estimation are detailed from section 4.3 to section 4.6. 42.

Chapter 3 Energy consumption for periodic data collection in WSN

Transition delays depend on the radio module device and especially on its host microcontroller.

These transition costs have been studied [START_REF] Zhou | Équipé avec des capteurs, des modules de communication et des puces informatiques, le produit intelligent dispose de capacités de stockage et de traitement de données[END_REF]. Compared with active duration for transmission and data processing, these delays are small (some microseconds). Therefore, these delays are considered as negligible and thus are ignored in our model. As a consequence, to give an approximate WSN lifetime, our model only takes into account the active and inactive states of the radio module.

Figure 42 different states of radio module

The energy consumption of the radio communication module can be simplified with the active and sleep states as in [START_REF] Hoang | Un modèle multi-agent récursif générique pour simplifier la supervision de systèmes complexes artificiels décentralisés[END_REF]:

𝐸 = 𝐸 𝑇𝑥 + 𝐸 𝑅𝑥 + 𝐸 𝐼𝑑𝑙𝑒 + 𝐸 𝑆𝑙𝑒𝑒𝑝 (10)

Data collection in a chain communication structure

In this section, we model a collection process that is as generic as possible, in order to be applied to different technologies. The collection begins from the furthest node. Its workflow is illustrated in Figure 43. At the beginning, its communication module is activated to transfer messages to its parent node. This node then waits an acknowledgment (ACK) message from its parent node before letting its communication module enter into sleep mode. A delay is used to ensure the reception of an ACK message from its parent node. Its message will be sent again if the waiting time exceeds the prefixed delay. This node periodically reactivates its communication module for a new transmission process. The modules are in sending or receiving states with Tx or Rx respectively. The transmission activities are represented by the node ID and its transfer message number. For example, Txij is the j th message transfer from node i. Meanwhile, the reception activity is presented with the sender ID and the message number (like Rx11 is the message 1 from node 1). The modules are in idle state (grey color) when they are active but not sending or receiving data.

Synchronization process begins with the furthest node (here, node 1) at the start time t11_begin.

The time before turning off communication module is recorded as t11_sleep after it received an ACK message from its parent (node 2). It will turn communication module on for next iteration at time t = t12_begin where t12_begin -t11_begin = T. The total active duration of the furthest node can then be computed with the recorded start time t11_begin and sleep time t11_sleep.

Unlike the furthest node, start time at intermediate nodes (here node 2) depends on the previous node. It records the message arrival time of node 1 as its start time t21_begin. For data collection without aggregation, it first transmits all messages from its child (node 1) and then sends its own message to its parent (node 3). Once receiving ACK message from its parent, it records the time t21_sleep and lets its communicating module enter in sleep mode. For next iteration, it will reactive the communication module a little earlier than its child to ensure the reception of messages. Although using sleep mode can reduce energy consumption at nodes, the hot-spot problem in chain structure is not solved: the closer the node is to the BS, the more energy the node consumes. Therefore, using data aggregation technique could be suitable. It can not only balance energy consumption in WSN, but also extend lifetime. Instead of transmitting all messages from child, only the aggregated message is sent for all intermediate nodes in the case of data collection with aggregation. The associated chronogram is shown in Figure 46.

Figure 46 Chronogram of radio module's activities with aggregation

The process of the furthest node is the same. However, intermediate nodes first stores messages from their child and replies with an ACK message. The received messages are then aggregated with their own message. Only the aggregated message will be sent to parent node. In this case, the activity duration (tij_sleep -tij_begin) for communication at all nodes is less than that in data collection without aggregation. Duty cycle at these nodes is reduced, and lifetime is increased.

Analytical model for data collection without aggregation

Since lifetime is the most important performance metrics for our application, knowing the residual energy of nodes is necessary for energy management. Building a theoretical energy consumption model could help us to know how the energy is consumed. As a consequence, an energy consumption estimation model, based on the mentioned data collection process, is presented hereafter.

Energy consumption of nodes depends on their activities. In this study, the power on active mode and sleep mode are Pactive and Psleep respectively. T is the period between two cycles of data collection. For building our model and in order to simplify those variables, we replace the mentioned transmission Tx, durations of IFS, time for sending or receiving ACK message and the time needed for the node synchronization (i.e. node should wake up a little bit earlier before receiving messages from a child node) by some symbols: α, β, γ and 𝑑 𝑠𝑦𝑛 (see Figure 47). In this section, we suppose that the size of ACK message is the same as one transmission message, therefore, α is equal to γ. The last node (N th ) of a chain works as intermediate nodes for the first two steps (activity duration is α +( i -1) * 2 * ( 𝛼 + 𝛽)). But its last step takes less time: once received its message (𝛼 + 𝛽) , the BS will reply with an ACK message ( 𝛼 + 𝛽 ). Therefore, the active duration of node i in a N-node (𝑁 ≥ 3) chain structure can be expressed in (11).

𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) = { 𝑘 1 , 𝑤ℎ𝑒𝑛 𝑖 = 1 (𝑖 -1) * 2 * (𝛼 + 𝛽) + 𝑘 2 , 𝑤ℎ𝑒𝑛 2 ≤ 𝑖 ≤ 𝑛 -1 (𝑖 -1) * 2 * (𝛼 + 𝛽) + 𝑘 3 , 𝑤ℎ𝑒𝑛 𝑖 = 𝑛
The data collection is performed periodically (cycle collection period T). With the duration of activity above, the duration of sleep mode for node i in a N-node chain structure can then be computed as in (12).

𝐷 𝑠𝑙𝑒𝑒𝑝 (𝑖) = 𝑇 -𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) (12)
In our study, we only consider the active and sleep mode, where the power consumption can be presented by 𝑃 𝑠𝑙𝑒𝑒𝑝 and 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 for sleep and active states respectively. Energy consumption per round for node i in N-node chain structure can then be estimated with their duration. The energy consumption 𝐸 𝑖 of the node i can be calculated as in ( 13):

𝐸 𝑖 = 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 + 𝐷 𝑠𝑙𝑒𝑒𝑝 (𝑖) * 𝑃 𝑠𝑙𝑒𝑒𝑝 (13)
Knowing the energy consumption of all nodes, it is then possible to estimate energy cost of whole network for each round. The total cost for one round can be calculated as in ( 14): The total active duration is computed as in (15):

𝐸 𝑡𝑜𝑡𝑎𝑙 = ∑ 𝐸 𝑖 𝑛 𝑖 = 1 = ∑ (𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 + 𝐷 𝑠𝑙𝑒𝑒𝑝 (𝑖) * 𝑃 𝑠𝑙𝑒𝑒𝑝 ) 𝑛 𝑖 = 1 (
∑ 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 𝑛 𝑖 = 1 = 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (1) + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (2) + ⋯ + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑛) (15) = k 1 + 2 * (α + β) + k 2 … . . +(n -1) * 2 * (α + β) + 𝑘 3

Analytical model for Data collection with aggregation

Like section 3.2.1, an estimation model is required for data collection with aggregation. The computation for the durations of activity and sleep are presented hereafter, as well as the energy consumption of estimation model for nodes and the whole network.

In this approach, we also begin with the furthest node. It first sends messages to parent node after an IFS (𝛽), and then waits for an ACK message. However, intermediate node (node 2)

does not retransfer messages as in the approach without aggregation. Instead of, only an aggregated message is transmitted in order to reduce transmission cost. Therefore, an intermediate node first replies an ACK to its child with (𝛼 + 𝛽) and then aggregates received message before the transmission. The details are given in the following paragraph. Once received the ACK message (𝛼), the furthest node shifts its radio module to sleep mode and records the sleep time. Therefore, the total active duration of node 1 is 2𝜶 + 2𝜷.

Activity duration at intermediate nodes is also in three parts for data collection with aggregation:

First, it sends an ACK message to its child (𝛼 + β + 𝛼) . Second, it aggregates the received message from child node with its own message. Third, it transmits the aggregated message and waits for the reply from its parent (2𝛼 + 2𝛽). Compared with the transmission time, data processing at CPU for the second time is small. Hence, we neglect it to build the model. At the end, the duration of node i for data collection in this approach can be expressed as in ( 16):

𝑫 𝒂𝒄𝒕𝒊𝒗𝒆 (𝒊) = { 𝟐𝜶 + 𝟐 𝜷 , 𝒘𝒉𝒆𝒏 𝒊 = 𝟏 𝟒𝜶 + 𝟑 𝜷 + 𝒅 𝒔𝒚𝒏 , 𝒘𝒉𝒆𝒏 𝐢 ≠ 𝟏 (16)
Similar to the approach without aggregation, each node will set its communication module into sleep mode after receiving an ACK message from its parent. Therefore, the duration of sleep for node i can be computed with (11). The constant 4𝛼 + 3 𝛽 + 𝛾 can be replaced by k2. The total active duration can be calculated in ( 17):

∑ 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) 𝑛 𝑖 = 1 = 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (1) + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (2) + ⋯ + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑛) (17) =2α + 2 𝛽 + 4𝛼 + 3𝛽 + 𝑑 𝑠𝑦𝑛 + ⋯ . . + 4𝛼 + 3𝛽 + 𝑑 𝑠𝑦𝑛 = 2α + 2 𝛽 + (𝑛 -1) * (3𝛼 + 4𝛽 + 𝑑 𝑠𝑦𝑛 ) = 2α + 2𝛽 + (𝑛 -1) * k 2
The energy consumption of the whole network can be computed as in ( 18 The previous study shows that using aggregation at intermediate nodes could reduce energy consumption as mentioned above. As consequence, aggregation methods (lossless and lossy) are considered for building a generic analytical energy estimation model which can be applied for all WSN. Therefore, a new analytical model based on the communication relations between nodes and on the application of in-network processing at nodes is proposed hereafter.

The framework for the generic energy consumption model is presented in Figure 49. Some elements are introduced to compute energy consumption of nodes:

• The relationship matrix P records the communication relationship between nodes which depends on the used data collection routing protocol;

• The aggregation coefficient 𝜆 depends on the selected aggregation method by users;

• The gathering matrix G stores aggregation processing method and the gathering path, it can be calculated with P and with aggregation coefficients 𝜆;

• Mrx and Mtx are vectors where each element represents respectively the size of received message and transmitted message for each node. They can be computed by using the matrices P and G.

• Active duration D is a vector recording the total duration in active state for each node, it can be computed with Mrx and Mtx.

• Energy consumption E is a vector recording the estimated energy consumption of nodes per round, which depends on the active duration and the communication module.

Figure 49 WSN energy consumption method's overview

To compute active duration for a node i, it is necessary to first calculate the size of received message 𝑀 𝑟𝑥 (𝑖) and transmitted message 𝑀 𝑡𝑥 (𝑖) at nodes. The transmitted message depends on the received messages, the monitoring data at nodes and the aggregation method. We assume that the format and the size of monitoring data are the same at each node. Each node can process its received messages and its own message with same or different aggregation coefficients as in described Figure 50. For example, the intermediate node i has two children. The size of transmitted message from node j and node k are S1 and S2, and the size of monitoring data at the node i is S3. Therefore, the size of received message Mrx(i) at node i is the sum of the messages from j and k (S1 and S2). In case of lossless data aggregation, nodes compress or merge all received messages and its own monitoring data in one or multiple messages (depending on the maximum length of packet size), all raw data is retained and transferred until the sink node. Therefore, all aggregation coefficients for a node i are equal to 1 as in ( 19):

λ 𝑖𝑗 = 1, 𝑗 𝜖 {𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑛𝑜𝑑𝑒 𝑖} (19)
In contrast, all the original data values cannot be recovered in lossy data aggregation (like the aggregation functions: mean, max or min). Each node sends only aggregated and data reduced messages. In case of a node i has m children, it can merge received messages and its own message with the same aggregation coefficient 𝟏/(𝒎 + 𝟏) into an aggregated message. The sum of these aggregation coefficients is equal to one. Therefore, aggregation coefficients at node i can be expressed as in (20):

λ 𝑖𝑖 = λ 𝑖𝑗 = 1/(𝑚 + 1), 𝑗 𝜖{𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑛𝑜𝑑𝑒 𝑖} (20)

λ 𝑖𝑖 + ∑ λ 𝑖𝑗 𝑗 𝜖{𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑛𝑜𝑑𝑒 𝑖} = 1
An example for lossy aggregation is shown in Figure 51 where the node i has two children, the node j and the node k. All nodes report their temperature to the next node and only the maximal temperature is reported until the sink. In this example, the node i receives the sampling information from its children (15 degrees for node j and 16 degrees for node k), it then compares the received information and its own monitoring information (15.5 degrees). Only the maximal temperature is reported to next node, therefore, all aggregation coefficients at node i are 1/3.

Figure 51 Data collection example with lossy aggregation

The size of a data packet may vary from minimal size (𝑆 𝑚𝑖𝑛 ) to maximum size (𝑆 𝑚𝑎𝑥 ). A data packet includes at least the header part and the data part of node, where the header part records the node ID, destination ID and other communication information. The data part could record the monitoring data of a node, request or reply information like ACK and so on. The sizes of 𝑆 𝑚𝑖𝑛 and 𝑆 𝑚𝑎𝑥 depend on the applied communication technologies. One more message is transmitted if the data part of a packet is full (when transmitted data is larger than 𝑆 𝑚𝑎𝑥 ).

Corresponding transmitting durations are illustrated in Figure 52. From the minimal size to maximum size, the transmitting duration increases linearly from the minimal transmitting duration 𝛼 𝑚𝑖𝑛 to the maximum transmitting duration 𝛼 𝑚𝑎𝑥 . To estimate the total active duration, the size of transmitted and received messages are detailed in the following.

Figure 52 Transmitting duration for different size of data packet

The size of transmitting message depends on the gathering path and on the applied aggregation method. To generalize the relationship between nodes in the network, parent relationship matrix P is used to record the interaction between child nodes and their parent as in ( 21). For an intermediate node i, Pij is equal to 1 when the node i is the parent node of the node j. Based on the relationship matrix between nodes and on the aggregation coefficients, a gathering matrix G is used to record the collection method as in [START_REF] Thomas | RFID et nouvelles technologies de communication; enjeux économiques incontournables et problèmes d'éthique[END_REF]. The element Gij records message processing coefficient from the node j at the node i.

𝐺 = [ G 11 ⋯ G 1𝑗 ⋯ G 1𝑁 ⋮ ⋮ ⋮ ⋮ ⋮ G 𝑖1 ⋯ G 𝑖𝑗 ⋯ G 𝑖𝑁 ⋮ ⋮ ⋮ ⋮ ⋮ G 𝑁1 ⋯ G 𝑁𝑗 ⋯ G 𝑁𝑁] (22)
For example, in a chain structure as shown in Figure 53, Pj, j+1 = 1. We suppose that the size of the sent message from node j is k and the data aggregation coefficient at a node j+1 is 𝜆 𝑗+1𝑗 .

Therefore, the processing coefficient G 𝑖𝑗 in this chain structure can be expressed as in (23). Repeat until E is null;

The received message at a node is the sum of the aggregated messages from their child nodes.

And its transmitted message can be computed by the sum of received messages and its own monitoring message with their corresponding aggregation coefficients as in ( 24), where vectors

Mtx [N] and 𝑆 [N] record the size of transmitted message and monitoring data size at nodes. Based on the vector Mtx[N] and the relationship matrix P, the size of received message for a node i can then be computed as in (25).

M 𝑡𝑥 = 𝐺 × 𝑆 = [ G 11 G 12 ⋯ G 1𝑁 ⋮ ⋮ ⋮ ⋮ G 𝑖1 𝐺 𝑖2 ⋯ G 𝑖𝑁 ⋮ ⋮ ⋮ ⋮ G 𝑁1 G 𝑁2 ⋯ G 𝑁𝑁 ] [ S 1 ⋮ S 2 ⋮ S 𝑁 ] (24) = [ G 11 S 1 + ⋯ + G 𝑖𝑖 S 𝑖 + ⋯ + G 1𝑁 S 𝑁 ⋮ G 𝑖1 λ 11 + ⋯ + G 𝑖𝑖 λ 𝑖𝑖 + ⋯ + G 𝑖𝑁 λ 𝑁𝑁 ⋮ G 𝑁1 λ 𝑁𝑁 + ⋯ + G 𝑖𝑖 λ 𝑖𝑖 + ⋯ + G 𝑁𝑁 λ 𝑁𝑁 ] 𝑀 𝑟𝑥 = 𝑃 × M 𝑡𝑥 (25)
For example, the gathering matrix for a 3-node chain where node 1 transmits data to node 2 until the BS (node 3) can be computed as in (26). With this matrix and the monitoring data size

vector 𝑆 [N], the vector of transmitted message can be computed as in ( 27). Using the transmitted message matrix and the relationship matrix, the received message matrix can be computed as in (28).

𝐺 = [ G 11 G 12 G 13 G 21 G 22 G 23 G 31 G 32 G 33 ] = [ λ 11 0 0 λ 21 λ 22 0 λ 21 λ 32 λ 32 λ 33 ]
(26) For a given network, the gathering matrix G can be computed by the network relationship between nodes (the matrix P) and the data aggregation coefficients. This gathering matrix can be used to calculate received and transmitted messages sizes. The total active duration of a node i can be computed in (29). The transmitting duration 𝐷 𝑡𝑥 depends on the size of transmitted data packet. Messages are merged as much as possible into aggregated messages according to the maximal length. The receiving duration 𝐷 𝑟𝑥 depends on the size of received data packet and the number of children, because an ACK message is replied to each child after the IFS (𝛽 + γ ).

M 𝑡𝑥 = [ 𝑀 1 𝑀 2 𝑀 3 ] = [ G 11 G 12 G 13 G 21 G 22 G 23 G 31 G 32 G 33 ] [ S 1 S 2 S 3 ] (27
With the total active duration, energy consumption of this node can then be computed as follows:

𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) = 𝐷 𝑟𝑥 (𝑖) + 𝛽 + 𝐷 𝑡𝑥 (𝑖) (see Figure 47 = 𝑃 𝑠𝑙𝑒𝑒𝑝 * 𝑇 + ( 𝐷 𝑟𝑥 (𝑖) + 𝛽 + 𝐷 𝑡𝑥 (𝑖)) * (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 )

Model for tree structure

In some phases of CM lifetime, different communicating concretes may cooperate with each other to form a tree structure. The example of a tree structure with N nodes is shown as Figure 54 where the node i is the root node. All nodes send their message to their parent until the root node is reached.

Figure 54 Root node is an intermediate node during data collection

Data collection in this type of network can be considered as the collection of two small chains, an example of 5-node tree structure is shown Figure 55. These two small chains can perform data collection simultaneously, e.g., by using TDMA as in LEACH [START_REF] Heinzelman | An application-specific protocol architecture for wireless microsensor networks[END_REF] or step by step with a token in PEGASIS [START_REF] Lindsey | Data gathering algorithms in sensor networks using energy metrics[END_REF]. In our study, we focus on data collection with TDMA methods. Before the phase of data collection, nodes are synchronized.

All nodes can turn on the communication module at the scheduled time to receive and transmit messages. The chronogram example for this 5-node tree structure is illustrated in Figure 56. consequence, the duration for reception of node i with many children can be computed as in (30) where 𝐷 𝑡𝑥 (𝑗) is the transmitting duration of one of its children. To ensure that nodes can wake up at the right time, a synchronization delay is introduced in (31).

𝐷 𝑟𝑥 (𝑖) = ∑ (𝐷 𝑡𝑥 (𝑗) ∀𝑗∈{𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 𝑜𝑓 𝑖} + 𝛽 + γ) (30)
𝐷 𝑟𝑥 (𝑖) = 𝑑 𝑠𝑦𝑛 + ∑ (𝐷 𝑡𝑥 (𝑗)

∀𝑗∈{𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛 𝑜𝑓 𝑖} + 𝛽 + γ) (31)
Unlike the single chain structure, the gathering matrix in tree (or multi-chain) structure is more complex. For the example of the Figure 57, the node 3 can receive messages from the node 2 and node 4. The gathering matrix G of the 5-node tree can be seen as the fusion of the two small chains via the node 3, where G1 and G2 are the gathering matrix for the chain from node 1 to node 3, and node 5 to node 3 respectively.

Figure 57 Tree as a composition of chains

With the gathering matrix as in ( 32), the transmitted matrix can then be computed as in (33), and the received message matrix can be obtained with the equation in (28). As consequence, active duration for all nodes can then be calculated with the equation in ( 29). For a N-node tree where the node i is the root node, we can then generate the gathering matrix as in (34) which includes the matrices of its two parts.

𝐺 = [ λ 11 ⋯ 0 0 0 0 ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ λ 11 * … . * λ 𝑖-1𝑖-2 ⋯ ⋱ 0 0 0 λ 11 * λ 21 * … . * λ 𝑖𝑖-1 ⋯ λ 𝑖𝑖 λ 𝑖𝑖-1 λ 𝑖𝑖 ⋯ λ 𝑖𝑖+1 * … . * λ 𝑁-1𝑁 λ 𝑁𝑁 0 ⋯ 0 0 ⋱ ⋮ ⋮ ⋯ 0 0 0 λ 𝑁-1𝑁 λ 𝑁𝑁 0 ⋯ 0 0 0 λ 𝑁𝑁 ] (34)

Model for other structures

Independent chain networks can be grouped to form a tree structure or a cluster structure. An example is shown in Figure 58. There are two CM elements: a data collection tree structure is used as in element 1 (the red block) or a cluster structure in element 2 (the blue block). In a case of cooperation between the two elements, the node 9 which is the cluster leader links the two parts via the node 4 (the link in orange). The node 4 then transmits data to the BS via the node 9 instead of transmitting directly to the BS. Thus, nodes in each CM element send their information to the next until the cluster leader via their own topology structure.

Figure 58 Data collection in different structures

With the Algorithm 1, the matrix for the element 1 in red and element 2 in blue can be generated as in ( 35) and ( 36) respectively. The gathering matrix for this network can be seen as the composition of the two parts as illustrated in (37), all messages from the node 4 are aggregated at the node 9 with the aggregation coefficient 𝜆 94 (in orange color). Therefore, the gathering element 𝐺 91 of the new gathering matrix in (37) can be computed with the gathering element 𝐺 41 of 𝐺 1 in (35) and the responding aggregation coefficient 𝜆 94 , same for other messages from the nodes of element 1. We can conclude that gathering matrix for a composed network can be seen as the composition of the independent small matrices. In this example, the node 9 links the two structures without changing the structures. In other cases, there may be modifications in the communication structure and in the routing of data.

𝐺 Based on gathering matrix G, we can then apply the equations ( 24) and ( 25) to calculate the size of transmitted and received messages. Messages sizes can then be used to compute active durations in order to estimate their consumed and residual energy with the equation (29).

Lifetime computation examples are given in Chapter 4 and Chapter 5.

Conclusion

In this chapter, a simple energy model for communicating node is presented, followed by the explanation of the data collection process in chain-based structure. The energy consumption models for data collection with/without aggregation in a chain structure are detailed. The accuracy of this position-based analytical model in chain is verified by a physical platform. The experimental results are presented in Chapter 5 (section 5.1). In our proposal, the energy spent by the radio interface of a node only depends on five parameters (consumption is uniform all along the chain in case of collection with aggregation) and also depends on the position of the node in the chain (in case of collection without aggregation). The general formulation of the energy consumed by a node in a chain is defined as follows:

Econsumed = f (α, β, γ, 𝑃 𝑠 , 𝑃 𝑎 ) With:
-𝑃 𝑠 , the power consumed by radio in sleep mode;

-𝑃 𝑎 , the power consumed by radio in active mode; -(α, β, γ), the delay parameters, , expressing the active duration of radio module. Due to the limit of computing and storage at nodes, long-term service requirements bring challenges to the design of WSN in a resource constrained environment. Using a multi-agent system to build a digital twin can provide an optimal solution with a global view to extend network lifetime. This chapter begins with the recursive architecture for communicating material in section 4.1, followed by the proposed agent model in section 4.2. The composition/decomposition mechanisms are detailed in section 4.3. Interaction between agents for lifetime estimation is given in section 4.4. In some phases of the lifecycle, communicating concretes may cooperate with each other, different cooperation methods are then discussed in section 4.5. Besides, the proposed energy estimation model (proposed in chapter 3) is used for energy consumption estimation of a single concrete and an aggregated concrete in section 4.6.

A conclusion is given in section 4.7.

The proposed CM recursive architecture

As introduced in chapter 1, the real (physical) node coupled with its agent (digital) node is an elementary material holon as shown in Figure 60. Due to the special composition ability of CM, these elementary holons can be grouped as a composed material holon via the link 3. The node agents can communicate with the composed agent via the link 1. Besides, agents can discuss with those on the same level by exchanging messages via the interaction link 2. To organize the communication between agents on the same level or different levels, we would like to apply the MAS-R recursive architecture to our work as mentioned in chapter 2.

Figure 60 Composed CM agent

The CM recursive architecture for the two communicating elements example (tree structure in element 1 and cluster structure in element 2) in previous chapter is shown in Figure 61. There are four CNs (node 1 to 4) in the first element, and five CNs in the second (node 5 to 9). Each node is represented by an elementary agent (level 0). For an agent CMi j , i and j represent the abstraction level and the index of the CM in the corresponding level respectively. Elementary agents (from CM0 1 to CM0 9 ) are aggregated into two composed CM agents (CM1 1 and CM1 2 ) on level 1. In the case of aggregation, those two level 1 composed agents are gathered into the agent CM2 1 at the level 2. The latter (and hierarchically higher) agent has the global view of all CM agents. These links for building aggregated agent and communication between agents in the same level and different levels can be expressed by the operators PA, PO and PI in MAS-R:

• The PA operator (Process Agent) is responsible for building aggregated agents or decomposing aggregated agents (the composition and decomposition processes will be detailed in section 4.3).

• The PI operator (Process Interaction) is defined for transforming interactions between different levels, for example, the interaction between agents CM0 1 and CM1 1 can be transformed into an interaction between agents CM1 1 and CM2 1 (an example will be given in section 4.4).

• the PO operator (Process Organization) is defined for transforming relations between agents of a level to the upper level (an example will be presented in section 4.5).

Figure 61 CM recursive architecture

Adapted MAS-R agent model

Based on the agent model in MAS-R model [START_REF] Hoang | Un modèle multi-agent récursif générique pour simplifier la supervision de systèmes complexes artificiels décentralisés[END_REF], an adapted agent model, dedicated to the CM application, is proposed. The adapted CM agent still has five parts as shown in Figure 62: the knowledge part (K) holding all data, the capabilities and recursive states of agent; the composition part (C) and the decomposition part (D) containing composition function to construct a higher level and decomposition function to destroy abstraction level; the observer part (O) monitoring the recursion states of agent in K and call composition or decomposition functions if necessary; the recursive interaction part (RI) recording the interaction functions for agents located at the same level and the interaction between different levels. Compared to the original agent model, the main difference is in the K part which includes two different types of data: product-related data and data for energy computation. In more details, the parameters of product-related data are presented as follows:

• Node ID: each communicating node have a unique identification in the embedded WSN of communicating concrete.

• Current energy Eres: this information is different along the abstraction level, it includes remaining energy information of communicating node in level 0. Otherwise, it includes energy information of all members in a vector.

• Communicating element ID: it is the identification of the communicating concrete. Each communicating concrete have a unique identification.

• Agent ID: it records the identification of an agent in the digital part where each communicating agent are represented by a node agent (elementary agent), the ID of an agent being unique in each abstraction level.

• Abstraction level: it is the agent level in the recursive architecture

• Parent agent's ID: recording the ID of composed agent which it belongs to.

To efficiently manage energy consumption of the embedded WSN, it is necessary to add all energy-related information to the K part of linked agents:

• Communication relationship matrix P (corresponding to the physical network):

recording communicating relationship of a node in the data gathering structure (its children and parent nodes).

C D K O RI

• Cooperative agent: the agents which can cooperate with, for example, the elementary node agent of the nodes in its communicating range. Those cooperative agents are recorded in the cooperative agent list (CAL).

• Aggregation coefficients matrix λ: including the aggregation coefficients selected by the users.

• Gathering matrix G: including the gathering matrix which depends on the communication relationship matrix P and the aggregation coefficients.

• Transmitted matrix Mtx and received matrix Mrx: including the size of transmitted messages and received messages respectively.

• Energy consumption per node E : it records the analytical energy consumption result of node in level 0, or energy consumption of the whole network in upper levels.

In different phases of CM product lifecycle, aggregated agents can cooperate with others to complete special tasks or be decomposed into independent CM products. At the beginning of data collection, each physical node has a unique ID in the network, their physical information is stored in dedicated elementary agents. The knowledge (part K) of agents on different abstraction levels are different. As an example, the knowledge of agents (CM0 4 , CM1 1 , CM2 1 ) for product-related information and energy consumption in three different levels is shown in Table V. a and Table V. b respectively. Only elementary agents contain the physical node ID.

In this architecture, each agent has its unique identification CMi j which includes its abstract level i and its identification ID j on this level. The communicating element ID is included for all agents. Besides, the agents in communication range are recorded in the CAL.

The relationship matrices P for agents located at different levels has different forms: relationship matrix of an elementary agent is a vector which includes its children ID. For example, P0 4 of the agent CM0 4 is shown in (38) where the node 4 is the parent for the node 2 and node 3. The higher the abstraction level, the higher the completeness of the relationship matrix as the agent CM1 1 has the global view of its members in (39). An agent in highest level has the global view of the whole network as the CM2 1 in (40). Based on relationship and aggregation coefficient matrices, an aggregated agent (agent with an abstract level greater than or equal to 1) can compute the gathering matrix G and energy consumption E and estimate network lifetime.

4 [100%] 1 4 0 CM1 1 CM0 5 5 [100%] 2 5 0 CM1 2 CM1 1 0 𝐸 𝑟𝑒𝑠 1 1 1 1 1 CM2 1 CM1 2 0 𝐸 𝑟𝑒𝑠 1 2 2 2 1 CM2 1 CM2 1 0 𝐸 𝑟𝑒𝑠 2 1 1-2 1 2 0 Table V. b. Knowledge part K concerning energy consumption in different abstraction levels Agents Relationship Matrix P CAL Aggregation coefficient Matrix 𝜆 Gathering Matrix Gi Energy consumption Ei CM0 4 𝑃 0 4 CM0 2 , CM0 3 , CM0 5 𝜆 0 4 G0 4 E0 4 CM 0 5 𝑃 0 5 CM0 6 , CM0 8 , CM0 9 λ 0 5 G 0 5 E 0 5 CM1 1 𝑃 1 1 CM1 2 λ 1 1 G1 1 E1 1 CM1 2 𝑃 1 2 CM1 1 λ 1 2 G1 2 E1 2 CM2 1 𝑃 2 1 0 λ 2 1 G2 1 E2 1 𝑃 0 4 = [0 1 1 0] (38)
P 1 1 = [ 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 ] (39)
P 2 1 = [ 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 1 1 1 ] (40)
Once all elementary agents are created, the O part in these agents will periodically analyze the relationship information stored in K (the relationship matrix P, the CAL) to verify whether the composition/decomposition conditions are met. The composition and decomposition process and conditions are explained in the following section.

Composition / decomposition mechanisms

In different phases of CM lifecycle, a product may work alone or cooperate with other CMs.

There are different composition conditions varying over the lifecycle. At the beginning (within a single concrete), the aggregation condition is triggered if a node detects another node in its communicating range based on the communication relationship matrix P. In the phase of construction or exploitation, the aggregation mechanism can be triggered when the agent of one product detects the arrival of a cooperative agent of another product, or when it receives a cooperation request from users. For example, once the elementary agents of element 1 are created as shown in Figure 63, the agent CM0 4 can invite other elementary agent in its CAL to create an aggregated agent CM1 1 .

Figure 63 Building recursive architecture with composition process

The sequence diagram for generating parent agent CM1 1 by the agent CM0 4 is illustrated in Figure 64. When the observer part of agent CM0 4 finds that there are some other cooperative agents out in its communicating range (based on its relationship matrix P), it applies the PA operator (composition process) to create the aggregated agent CM1 1 . The composition process can be explained in five steps:

1) The CM0 4 creates a parent agent CM1 1 and adds its communication relationship, aggregation coefficients (P, 𝜆) to agent CM1 1 (message 1 in Figure 64).

2) Request_compose messages are sent by CM0 4 to cooperative agents based on the CAL (message 2 and 3). The invited agents process the composition request and also invites their cooperative agents to join. Such as the invitation sent from the CM0 2 to CM0 1 (message 4 in red color) which is not in the CAL of CM0 4 due to communication range (as shown in Figure 63). All invited agents then reply an accept_compose message or a refuse_compose message directly to the agent CM0 4 (message 5, 6 and 7). For example, the agents belonging to another group may refuse the composition request. In the case of agreement, their communication relationship and aggregation coefficients information (P, 𝜆) are included in the accept_compose message.

3) The agent CM0 4 adds the relationship matrix and energy information of the accepted agents to its parent node (message 8).

4) The agent CM0 4 then sends update_compose messages to all child nodes. Once received the update_compose message, the agents update their own CAL to ensure they have the same parent agent.

Figure 64 The sequence diagram of composition

Once the aggregated agent CM1 1 is created and the structure is stable, this aggregated agent has the global view of the whole network with a complete relationship matrix P and coefficient matrix 𝜆. This agent can then compute its gathering matrix G based on the matrix P and 𝜆, and then construct the analytical energy model E which can be used to estimate lifetime of this element. Besides, the aggregated agent will also run PA operator to create an aggregated agent in upper level if its aggregation conditions are met.

The decomposition will be triggered if the agent in the highest level received a request from users or if one child agent detects the disappearance of its communicating node in physical part.

For example, the observer of the agent CM0 2 notices that its communicating node has run out its energy, the decomposition process will be triggered as following:

1) The agent CM0 2 sends a request_reduction to its parent agent CM1 1 .

2) The agent CM1 1 processes the request and updates its relationship matrix P, update_reduction messages are then sent to all children.

3) The child agents update their own relationship matrix based on the received update_reduction message.

Besides, there are two decomposition possibilities for an aggregated CM which is composed by two aggregated CMs such as CM2 1 is composed by CM1 1 and CM1 2 in the Figure 65. The first decomposition condition (case 1 in the Figure 65) is that the aggregated agent CM2 1 may receive the decomposition order from the server (the users). There are three phases for the decomposition in the first case:

1) The agent CM2 1 broadcasts request_stop to all children agents CM1 1 and CM1 2 to stop their activities and remove their parent relationship with CM2 1 . The ACK_stop messages are sent back to the agent by its children.

2) Once receives ACK_stop messages, the agent CM2 1 kills itself.

3) The aggregated agents CM1 1 and CM1 2 then evaluate and select suitable data gathering strategies and update their relationship matrices 𝑃 1 1 and 𝑃 1 2 . These matrices are then transferred to their children to update their strategies with update_gathering messages.

4) The ACK_gathering messages are then sent back to the aggregated agents CM1 1 and CM1 2 to ensure all agents have updated the new data gathering strategies.

The second decomposition case is that a child agent sends the decomposition request (the agent CM1 1 in Figure 65). This decomposition process will occur when the agent finds communicating nodes in physical part have run out of energy. Compared with the previous case, there are one more phase: the agent CM1 1 sends a request_reduction to its parent agent CM1 1 to begin the decomposition process. Then the decomposition process continues as that in the previous case. At the end, each aggregated agents in level 1 (CM1 1 and CM1 2 ) will select their new data gathering strategies.

Interaction between agents for lifetime estimation

Once composition process and energy model construction are completed, the agent CM2 1 at the highest level has the global view of all members. Based on the data gathering matrix G and energy levels, the agent CM2 1 can estimate residual lifetime of its members. To ensure the accuracy of lifetime estimation, this agent CM2 1 can ask current energy level of child agents to estimate CM lifetime. The estimation process example is shown in Figure 66.

The agent CM2 1 first sends request_energy messages to its child agents (CM1 1 and CM1 2 ). These agents then check whether they have current energy level information. They can reply directly to their parent node if they have (such as the aggregation agent CM1 2 in this example).

Otherwise, the agent (CM1 1 ) retransfers the request message to all its child agents to get their energy information (this interaction transformation is the PI operator). Once received all replies, the composed agent (CM1 1 ) can then aggregate energy information of its members and reply an

Cooperation between two aggregated elements

In some phases such as building construction, two aggregated CM products (by example, two individual concretes in Figure 67) may cooperate with each other to improve energy performance. These elements can be implemented in a multi-agent simulator NetLogo where each communicating node is represented by an agent (the simulator NetLogo will be presented in Chapter 5).

During the aggregated agent's lifecycle, its child agents check periodically whether they have new relationship possibilities or if new element arrives. In this example in Figure 67, the agent CM0 9 detects the agent CM0 4 . It then runs the cooperation process as shown in Figure 68 to decide the cooperation way. At the beginning, the agent CM0 9 sends a warning message to parent agent CM1 2 (message 2). A request_coop message that includes energy information (E)

and relationship matrix(P) of this element is sent back to CM0 9 (message 3). The request is then transferred to the aggregated agent (CM1 1 ) via the connected agent (CM0 4 ) (message 4 and 5).

Once the evaluation of cooperation is computed, an ACK_coop message is sent back. There are

The first case is no relation. Once the elementary agents receive a refuse_coop message, they maintain the original working state as shown in Figure 69. Therefore, there are no changes to the communication relationship matrices and the aggregation matrices.

The second case is association. If the parent agent confirms that the new element is related, it then replies to all children with the association messages. The aggregated agents can then build the relationship with each other as shown in Figure 70. This relationship transformation from the lower level to the high level is the PO operator. In this case, these two parts can always know each other's status and cooperate to accomplish their common tasks. G). This new strategy will then be transferred to all elementary agents. For example, the element 2 shares the cluster leader to the element1, and only the cluster leader in element 2 will send information to the BS.

Energy consumption estimation scenarios

With the formalized recursive architecture, composition and decomposition mechanisms, some application scenarios based on the proposed analytical energy model in chapter 3 are discussed in this section. We would like to analyze energy consumption of nodes in the previous example (two independent elements). Node 4 and node 9 are designated as root node and cluster leader of their respective element. The recursive architecture for these two elements is illustrated in Figure 72 where the aggregated agents CM1 1 and CM1 2 have the global view of their members respectively. Energy consumption is discussed for data collection in independent communicating concretes and in the case of the two elements aggregation hereafter.

Data collection with lossy data aggregation

During the composition process, the relationship matrix, aggregation coefficients will be added to the created aggregated agent as mentioned above. Figure 73 illustrates the construction of relationship matrix at the aggregated agent CM1 1 . In this example, the agent CM0 4 creates the aggregated agent CM1 1 and invites cooperative agents to join, all accepted agents reply the accept_compose messages which includes the relationship matrix P. At the end, the agent CM0 4 add all received relationship matrices and its own to the aggregated agent CM1 1 which then has the global view of all members.

Figure 73 Composition process for the construction of relationship matrix

The aggregation coefficients 𝜆 are retrieved via the same process. The aggregated CM1 1 can then compute the gathering matrix G and obtain the energy consumption model E for each node.

The aggregation coefficients depend on the applicative needs. For example, with lossy data aggregation, the aggregation coefficients of a node i with n children are 1/(n+1). After the composition process is completed, the aggregation coefficient matrices 𝜆 1 1 and 𝜆 1 2 of the two elements can be presented as in ( 41) and ( 42). With aggregation coefficients and with the relationship matrices, the gathering matrices G1 1 and G1 2 can then be computed as in ( 43) and (44).

𝜆 1 1 = [ 1 0 0 0 1/2 1/2 0 0 0 0 1 0 0 1/3 1/3 1/3 ] (41) 𝜆 1 2 = [ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1/5 1/5 1/5 1/5 1/5] (42) 𝐺 1 1 = [ 1 0 0 0 1/2 1/2 0 0 0 0 1 0 1/6 1/6 1/3 1/3 ] (43) 𝐺 1 2 = [ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1/5 1/5 1/5 1/5 1/5] (44)
With the gathering matrices, the size of transferred and received messages for these two elements can be computed at the aggregated agents as follows: With these scenario parameters, the active duration of nodes can be calculated at all nodes. As the root node or cluster head always consumes more energy than other structure members, the active duration for the root node (node 4) is then analyzed in (45). A more complete energy consumption calculation for all nodes involved in these two elements is given in Chapter 5. The transmitted message size at the node 4 is 40 bytes, therefore, it sends data in a small transmission frame (transmission duration is 20 ms). For each iteration, its active duration is the sum of the time for message transmission 𝐷 𝑡𝑥 [START_REF]Cela pourrait être possible au niveau physique et au niveau digitale. Pour construire ce mécanisme, les interactions entre agents doivent être considérées[END_REF], the time for message reception 𝐷 𝑟𝑥 [START_REF]Cela pourrait être possible au niveau physique et au niveau digitale. Pour construire ce mécanisme, les interactions entre agents doivent être considérées[END_REF] and an IFS (𝛽 ). With the active duration, energy consumption of this node can then be computed. In this section, we just illustrate how to apply the analytical estimation model to compute active duration and energy consumption. In the section 5.2, energy consumption of both the chain leader and its members will be analyzed and compared.

𝑀 𝑡𝑥 1 1 = 𝐺 1 1 × 𝑆 = [ 1 0 0 0 1/2 1/2 0 0 0 0 1 0 1/6 1/6 1/3 1/3 ] [ 40

Energy consumption estimation for the composition of elements

There are three cooperation modes as mentioned in the previous section: independent, associate and aggregation. Compared with the first two modes, the last one, the aggregation of two elements, is the most complex case, where both cooperation and aggregation mechanisms are triggered. Such as the aggregation of two elements in Figure 74, the nodes in the element 1 transmit data to the BS via the chain leader of element 2 (the orange link between the node 4 and the node 9). In this example, we suppose that lossy data aggregation is used in these two elements.

There are three steps for this cooperation. First, the agent in the first element detects the arrival of the cooperative element and runs the cooperation process to decide the cooperation way (see Figure 68). Since the cooperation way is aggregation, the aggregated agent of element 2 can then apply the PA operator to build an aggregated agent in the upper level (see Figure 71). The created agent CM2 1 will have the global view of all members, the relationship matrix P2 1 as well as the aggregation coefficient matrix 𝜆 1 1 will be constructed as in Figure 73. Third, once the composition process is done, the aggregated agent CM2 1 in the level 2 then broadcast the new data collection strategies to all agents via the PI operators.

Figure 74 Aggregation of two elements

The new relationship matrix 𝑃 2 1 and coefficient matrix for the aggregated agent CM2 1 is shown as in ( 46) and ( 47). Based on those two matrices, the gathering matrix 𝐺 2 1 can be computed at the agent CM2 1 as in (48) which can be seen as the fusion matrices as the (37) in section 3.3.3.

𝑃 2 1 = [ 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 ] 36 1/36 1/18 1/18 1/6 1/6 1/6 1/6 1/6] (48) With the relationship matrix and the gathering matrix, the transmitted message size and the received message size can be computed as follows: The transmitted message size is the same for all nodes with lossy method. However, the more the children, the more the received messages. Therefore, the active duration of the leader (the node 9) can be computed as follows:

(46) 𝜆 2 1 = [ 1 0 0 0 1/2 1/2 0 0 0 0 1 0 0 1/3 1/3 1/3 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1/6 1/6 1/6 1/6 1/6 1/6] (47) G 2 1 = [ 1 0 0 0 0 0 0 0 0 1/2 1/2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1/6 1/6 1/3 1/3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1/
𝑀 𝑡𝑥 2 1 = G 2 1 × 𝑆 = [ 1 0 0 0 0 0 0 0 1/2 1/2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1/6 1/6 1/3 1/3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1/36 1/36 1/18 1/18 1/6 1/6 1/6 1/6 1/6] [
𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (9) = 𝐷 𝑟𝑥 (9) + 𝛽 + 𝐷 𝑡𝑥 (9) + 𝑑 𝑠𝑦𝑛 ( 9 + 𝛽 + γ = 𝑑 𝑠𝑦𝑛 + 𝑑 𝑡𝑥 (4) + 𝛽 + γ + 𝑑 𝑡𝑥 (5) + 𝛽 + γ + 𝑑 𝑡𝑥 (6) + 𝛽 + γ + 𝑑 𝑡𝑥 (7) + 𝛽 + γ + 𝑑 𝑡𝑥 (8) + 𝛽 + γ To address the research issues for the realization of the communicating concrete, energy saving solutions and analytical energy estimation models have been proposed in chapter 3. Dedicated agents have also been defined as well as the recursive architecture in chapter 4. As consequence, there are two main experimental steps before implementing these proposals into real concrete pieces: first, the energy evaluation of a chain structure to show that our predictive models are reliable and have a high accuracy (section 5.1); second, implementation of the proposed recursive architecture in a multi-agent simulator to verify the usefulness of proposed composition and cooperation process, and illustrate its benefits for energy estimation in the cases of cooperation (section 5.2).

Chapter 5 Experiments, developments and validation

Validation of the energy consumption model for a chain structure

In the first part of this section, the materials used for our testbed are introduced. Then, the first experimental step deals with the measurement of power consumption in active and sleep modes.

In a second step, energy consumptions with/without aggregation are compared to validate the aggregation option with a 3-node chain structure. The real energy consumptions of 6 nodes in chain are recorded and compared to verify the accuracy of our energy estimation model.

Chain-based data collection platform

The chain-based routing structure is suitable for a small network as that within one McBIM element. Before applying it to a real concrete element, a physical platform is designed to verify the accuracy of the proposed energy estimation model. However, to efficiently use sleep mode, synchronization of nodes is required.

Energy consumption of Arduino nodes with XBee shield

The power consumption of the Arduino XBee board card is measured periodically. This board card can be powered either by USB cable or external battery. Measuring power consumption with battery may be affected by its surrounding environments and the result may be instable. In order to verify the usefulness of sleep mode for data collection, a simple program is implemented on Arduino XBee card 1 for two tests. During the first test, the node 1 sends a simple letter 'A' every five seconds for twelve cycles, but always remains in active state (it only enters in idle mode after transmitting its message). In the second one, the node lets its XBee module enter into sleep mode after transmission. The energy power for these two tests is shown in Figure 78.

It is clear that the node in the first case consumes more energy. In the second one, its power is reduced when the XBee module enters into sleep mode. The total energy consumption is around 36.89 J for the first test. The duty cycle of the XBee module (defined as the ratio active time/period) is 100%. However, the energy cost is reduced to 27.74 J using sleep mode which is only 75.2% of that in the first test. The duty cycle of radio module is 18.3% with a total active time of 11.90 s (almost one-fifth of the former) and the gains of energy consumption reaches 63%. This proves that using sleep mode in WSN can significantly extend the network lifetime. The average power of a node in active state is around 600 mw, and 320 mw in sleep state where the power of Arduino alone (without the XBee shield) is 275 mw. Therefore, the power of XBee module with its shield in active / sleep mode are 325 mw and 45 mw respectively. Besides, the computing ability of nodes could also be used to reduce the data transfer. In the following, a study on chain-based data collection with or without in-network data aggregation is presented.

Data collection in 3-node chain

Before applying our approach for a long chain, a short chain structure is first considered to verify whether our synchronization approach works, and the usefulness of in-network data processing at intermediate nodes. A three-node chain is shown in Figure 79.

Figure 79 Three-node chain structure

In this structure, data collection process begins from node 1 to node 3. A card is used as sink node (or BS) to collect and upload information to PC. Four hypotheses for the data collection process are considered:

• All nodes (except the farthest node from the BS) employ the same program.

• Each node sends its data (7 bytes for the monitoring of temperature) with the same period T (5 s).

• Each node lets the XBee module enter into sleep mode after it receives an ACK message.

• The BS node is powered by the PC, other nodes are powered with external batteries.

After few synchronization tests with Arduino XBee card, the minimal duration for data transmission (𝛽) has been estimated around 20 ms. A long IFS is used to avoid the message collision (𝛼 is set to 100ms). To ensure the reception of message, node should wake early before the arrival of message from child node, the dsyn is set to 10 ms. With these parameters, the energy consumption of the whole network can be estimated thanks to our energy consumption models (Figure 80). The x-axis and y-axis represent the chain size and the network energy consumption for each period respectively. This result is a theoretical projection of the energy cost of a chain as a function of the number of nodes. For data collection in a chain structure, the predicted result is parabolic for the case without aggregation and linear with aggregation. The farther the node from the BS, the shorter its active duration. The active duration of node 3

(nearest node to the BS) is around 2.5 times higher than that of node 1. Using data aggregation avoids the hot-spot problem for chain-based structure and significantly reduces the energy consumption at node. In next section, this experiment is executed on a 6node chain structure, to validate our models on longer structures.

Data collection in 6-node chain

The previous experiments show that using sleep mode and aggregation techniques can significantly reduce energy consumption on a three-node chain-based structure. The following experiment aims at comparing the real energy consumption with our proposed energy estimation model for a longer chain structure. Therefore, a six-node chain structure is built as shown in Figure 82.

Validation of recursive architecture

In this section, the proposed recursive architecture and analytical energy model are implemented in a multi-agent platform (NetLogo) to build a virtual communicating concrete.

The NetLogo software environment is authored by Uri Wilensky. It allows to construct and explore agent-based models. This tool has been widely used in many fields of research, especially for the simulation of natural and social phenomena.

Analysis of energy consumption within a single communicating concrete

To validate the correctness of the proposed energy consumption methods for every kind of WSN structures, some chain, cluster and tree-based structures (or any combination) are tested.

An example of these three kinds of structures is shown in Figure 86. The element 1 is a 5-node chain structure where all nodes (with round shapes) send data to next until the chain leader (node 5 with a square shape). The chain leader then transmits data to the sink node (in red color).

The element 2 is a tree structure, where child nodes send data to parent node until node 4 which transmits data to the sink node. The element 3 is a cluster structure where node 5 collects information from members and then transmits information to the sink node. As mentioned in chapter 3, the proposed analytical model can be used for any type of WSN structures. To compute energy consumption in a structure, it needs to calculate the relationship matrix P, the gathering matrix G, the size of received messages 𝑀 𝑟𝑥 and transmission messages 𝑀 𝑡𝑥 as well as the active duration. Therefore, to validate the correctness of proposed method, the obtained results by our NetLogo MAS simulator will be compared with theoretical results.

Based on the network structure, the relationship matrix P for these three elements can be computed as in ( 50), ( 51) and ( 52). The gathering matrix depends on the relationship matrix and on the aggregation strategy. In order to simplify the computation, the matrices for collection with data lossless aggregation are compared in this study. The gathering matrices G for the three structures can then be computed in ( 53), ( 54) and ( 55) for lossy aggregation. Based on the matrix P and G, the size of transmitted message and received message can be computed to analyze the active duration and so the energy consumption of every node.

𝑃 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_1 = [ 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 ] (50)
𝑃 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_2 = [ 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 ] (51)
𝑃 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_3 = [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 ] (52)

𝐺 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_1 = [ 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 ] (53) 𝐺 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_2 = [ 1 0 0 0 1 1 0 0 0 0 1 0 1 1 1 1 ] ( 54
) 𝐺 𝑒𝑙𝑒𝑚𝑒𝑛𝑡_3 = [ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 ] (55)
In the simulation, each node is represented by an agent which can only know its neighbors.

Aggregated agents (in blue color) will be created when the composition process (see Figure 87) the active duration. Compared with lossless aggregation, the reduction of active duration for the leader in chain and tree for lossy data aggregation collection is obvious. As the child node number does not increase these two structures (from 2 nodes to 9 nodes), active duration of the chain leader and tree root does not increase with the increasing of network size. However, the cluster head has more and more children with the expansion of the network, explaining why the active duration of the cluster head still increases linearly for lossy data aggregation data collection.

The results of FND time with lossy aggregation for the three structures are shown in Figure 91 where each structure is composed of 9 nodes. Each node is powered with a 9 v, 565 mah battery.

The longer the active duration per cycle, the shorter the FND delay. As a cluster head has more radio activity per cycle than a chain leader or a root node in a tree, it consumes more energy than the others and reaches FND time first. As a root node in a tree has more children than a chain leader in a single chain structure, it takes more time for replying ACK and thus the tree root node has less lifetime.

Figure 91 FND time for data collection with lossy aggregation

Besides, aggregated agents in both structures can provide and manage the mean, maximal or minimal energy information which can be used to analyze the network lifetime. For example, we can compare the average residual energy of nodes for the case of first node death. The FND times of these three structures are 102,77 h, 97,26 h and 73,61 h respectively. Although the FND time in cluster and tree structure is shorter than that in chain, the average residual energy in chain, tree and cluster are 0,73%, 5,7% and 28,84% respectively. Based on the residual energy information, different reconstruction strategies can then be analyzed to adjust the network structure. Besides, the MAS simulator can also be used to predict the consumed energy for different cooperation methods between communicating elements, this usage is detailed in next section.

Cooperation of communicating concretes

Communicating elements may cooperate with each other to extend lifetime or complete some special tasks. Energy consumption for different cooperation methods (associated or aggregation)

can then be analyzed for the previous two independent communicating elements: a 4-node tree in element 1, and 5-node cluster in element 2 (see Figure 61). The digital model for those two elements is implemented as shown in Figure 92.

Figure 92 Two independent communicating concretes

The tree structure is on the left, the cluster structure is on the right side of the sink. Agents in green color (level 0) represent the communicating nodes, agents in blue color represent the aggregated agents in level 1. These two structures will both move towards the sink. The composition process will be triggered if one agent detects some agents in the other structure. The cooperation process for this example is illustrated in Figure 93. At the beginning, the agent CM0 6 detects the agent CM0 1 in another element, this agent CM0 6 then sends a warning message to its parent agent. In this case, the agent CM1 2 sends a request_coop message including its network relationship matrix P, aggregation matrix 𝜆 to the parent agent CM1 1 using the PO operator via the agents CM0 1 and CM0 6 as shown in Figure 94. Based on the received information, the agent CM1 1 will reply with an ACK_coop message which includes the cooperation method (independent, association or aggregation). Therefore, the new relationship matrix 𝑃 2 1 at the aggregated agent CM2 1 can be computed as in (56). Based on the relationship matrix, the gathering matrix 𝐺 2 1 can be calculated as in (57).

𝑃 2 1 = [ 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 ] (56)

𝐺 2 1 =
[ 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0

1 1 1 1 1 1 1 1 1 ] (57)
Based on these two matrices, the active duration of every node can be computed for both lossless and lossy data aggregation collection as in section 4.6. For each round of collection, energy consumption at nodes is illustrated in Figure 97. Energy consumption for the end nodes (node 1, [START_REF]réaliser le lien entre les matériaux communicants avec leurs agents dédiés, ainsi que la gestion des données et de l'énergie des noeuds chez ces agents? La partie physique est liée à une partie digitale représentée dans cet mémoire par un système multi-agents (SMA)[END_REF]5,6,7,8) is the same for both lossless and lossy strategies, because they don't have child node. For the others, the more child nodes, the more energy it consumes. From these results, it is obvious that node lifetime is extended with lossy data aggregation method. The lifetime gain for node 1, node 2, node 4 and node 9 are 0%, 2,24%, 8,33% and 20,49% respectively. The greater the number of children, the more obvious the extended life.

Analysis

This chapter presents experimental results for both the accuracy of the proposed energy model in physical part and the usefulness of the recursive architecture for energy estimation in digital part. In the physical part, the proposed analytical estimation models were verified and validated by a chain experiment platform. Using sleep mode combined with data aggregation greatly reduces energy consumption. According to the node position in the chain, the reduction can be quantified by the gap of active duration between the two collection modes (with or without data aggregation). For a 6-node chain, the closer to the chain leader, the more obvious the reduction in energy consumption. For one round, the active duration of chain leader for data collection without aggregation is 3,5 times longer than in the case of aggregation. The proposed analytical energy estimation model achieves a high accuracy (more than 95%) for ten iterations. The long cycles test will be performed in following works, such as one week or one month.

In To address these research issues, states of the art regarding the physical part and digital part are described in the chapter 2. In the physical part, the existing data aggregation solutions, routing protocol and time synchronization protocols are analyzed to find an efficient way for periodical data collection. Using data reduction techniques at nodes allows to reduce transmission messages and extend network lifetime. The time synchronization protocols always depend on the applied routing protocols. Each data collection structure has their special benefits, it is thus hard to ensure that a routing protocol can be suitable for all lifecycle phases. Most of works apply the distance-based energy models. However, distance seems to be unsuitable for WSNs in our case study where obtaining the node positions is unrealistic. Therefore, a general analytical energy model, using a power-based consumption approach, is needed for periodical data collection in a network of communicating concretes.

In the digital part, the existing recursive holarchies and MAS architecture are studied to derive a recursive architecture for CM applications. The needs of recursion are in different applicative areas, and most of proposed architectures are for static environments. However, communicating concrete may aggregate with others throughout its whole lifetime. A dynamic architecture is then required. Although the application for WSN in Occello is closest to our needs, more information should be added to the agent model, and the network cooperation between WSNs is not considered. Thus, a dynamic recursive architecture is needed for a CM application, as well as a formalization of the composition and cooperation processes for dynamic scenarios.

Regarding the analytical energy estimation model, the chapter 3 presents a power-based energy consumption model for communicating node. As energy consumption depends on the duration of active and sleep activities, the duration is analyzed for different cases. In the first part, an analytical energy model based on the position of nodes in chain structure has been proposed to estimate energy consumption. Data aggregation and time synchronization are both considered to reduce energy consumption at nodes. To provide approximate remaining energy of nodes for all types of structures, a relationship-based analytical energy model has then been proposed and detailed. For a given data collection structure where aggregation strategies are defined by users, the size of transmitted and received messages can be computed with the defined algorithm which can then be used to calculate energy consumption at nodes to estimate their residual lifetime. This energy consumption information of nodes in a resource constrained environment can not only provide user the remaining lifetime but also can help to select appropriate data collection strategies to meet the environment's changes. cooperation between concretes, once an agent finds another available communicating elements, the cooperation process is triggered. An aggregated agent in an upper level is created if the cooperation method is aggregation. This agent in the upper level can compute the active duration of its members, and it can provide the remaining energy information which can be used to analyze different data collection strategies to extend lifetime.

Perspectives

The perspectives in short-term is mainly in the physical part: we will improve, test and adapt energy estimation models to different aggregation routing protocols (tree-based and clusterbased or a hydrid structure). Extended experimentations could be made in order to consolidate models. Three kinds of campaigns can be envisaged: 1. Long time experimentations to verify that the models do not drift from reality with time; 2. Experimentations with a longer chain structure to experimentally confirm the gap between collection with or without aggregation; 3.

Experimentations with other wireless technologies to compare performances. In addition, the proposed energy estimation model as well as the recursive architecture will be integrated into the McBIM project, and link to a real communicating concrete. Both the composition and cooperation processes of nodes within a single concrete will be tested as well as the cooperation between several communicating concretes. Lifetime estimation in different scenarios will be analyzed and verified to illustrate its usefulness.

For long-term perspectives, we would like to combine the estimation model with decisionmaking (reconfiguration of the collection process) through centralized or distributed algorithms (or even a hybridization of the two) and to compare this approach with existing solutions. For example, we can use machine learning or other AI methods to evaluate and select the most suitable and efficient data gathering strategies with the product-related information (life phase, remaining energy and so on) to extend product lifetime. Meanwhile, we will test with other communication technologies to show the genericity of the proposed model. Besides, some other resource constrained applications may be considered for the use of the proposed holonic architecture. For example, the transportation of COVID-19 vaccine, WSN can be used to monitor temperature of the container to ensure the safety of vaccine. In a forest, monitoring temperature and humidity can be used to prevent fires. The proposed holonic architecture will be tested with other intelligent products or communicating materials to demonstrate its usefulness for the product lifecycle management.

détaillé, ainsi que le modèle de collecte de données avec ou sans agrégation (voir chapitre 3). Ces modèles prédictifs sont testés et validés par des expérimentations (voir section 5.1 du chapitre 5).

• Enfin, la thèse se termine par une conclusion générale dans laquelle les contributions de nos travaux sont résumées, et quelques perspectives sont discutées.

Figure 1

 1 Figure 1 McBIM communicating concrete ................................................................................

Figure 2

 2 Figure 2 Intelligent control system for communicating concrete...............................................

Figure 3

 3 Figure 3 Information flow of closed-loop PLM (Jun et al., 2007) .............................................

Figure 4

 4 Figure 4 Classification of intelligent products (Meyer et al., 2009) ........................................

Figure 5

 5 Figure 5 Intelligence classification with lifetime phases .........................................................

Figure 6

 6 Figure 6 Applying Intelligent product to PLM ........................................................................

Figure 7

 7 Figure 7 Architectures of information system: EPCIS, DIALOG and WWAI ........................

Figure 8

 8 Figure 8 PROSA architecture (Van Brussel et al., 1998) .........................................................

Figure 9

 9 Figure 9 Structure of an elementary material Holon ................................................................

Figure 10

 10 Figure 10 Representation of a communicating material ..........................................................

Figure 11

 11 Figure 11 Example of WSN .....................................................................................................

Figure 12

 12 Figure 12 Network architecture of communicating concrete ...................................................

Figure 13

 13 Figure 13 McBIM communicating concrete lifecycle .............................................................

Figure 14

 14 Figure 14 Intelligence levels of CM applications ....................................................................

Figure 15

 15 Figure 15 Digital part design for communicating concrete ......................................................

Figure 16

 16 Figure 16 The holonic approach for communicating concrete.................................................

Figure 17

 17 Figure 17 Architecture of sensor node .....................................................................................

Figure 18

 18 Figure 18 Classification of energy efficient mechanisms for WSN (Rault, 2017) ..................

Figure 19

 19 Figure 19 MLDA problem in WSN .........................................................................................

Figure 20

 20 Figure 20 Data aggregation example........................................................................................

Figure 22

 22 Figure 22 Tradeoff of data reduction functions........................................................................

Figure 23

 23 Figure 23 Hierarchical data collection structures in WSN .......................................................

Figure 24

 24 Figure 24 LEACH routing protocol .........................................................................................

Figure 25

 25 Figure 25 Minimum spanning tree-based routing protocol ......................................................

Figure 26

 26 Figure 26 PEGASIS chain-based protocol ...............................................................................

Figure 27

 27 Figure 27 CRBCC protocol ......................................................................................................

Figure 28

 28 Figure 28 Timeline for chain based routing protocols (Khedr et al., 2021) .............................

Figure 29

 29 Figure 29 RBS method .............................................................................................................

Figure 30

 30 Figure 30 Synchronization between two nodes in TPSN .........................................................

Figure 31

 31 Figure 31 Radio energy dissipation model ...............................................................................

Figure 32

 32 Figure 32 Event trigger mechanism of NEM ...........................................................................

  Figure 34 PROSA architecture (Van Brussel et al., 1998) .......................................................

Figure 35

 35 Figure 35 ADACOR architecture (Leitão and Restivo, 2006) .................................................

Figure 36

 36 Figure 36 Organization approach of agents: Flat (a), Hierarchical (b), Holonic (c), Coalition (d),

  ...............................................

Figure 37

 37 Figure 37 Holonic architectural model for embedded diagnosis .............................................

Figure 38

 38 Figure 38 Social-based holonic architecture (Esmaeili et al., 2017) .......................................

Figure 39

 39 Figure 39 The MAS-R architecture ..........................................................................................

Figure 41

 41 Figure 41 Holonic architecture for microgrid (Nurdin and Muchallil, 2019) ..........................

Figure 42

 42 Figure 42 different states of radio module ...............................................................................

Figure 43

 43 Figure 43 Process at the furthest node......................................................................................

Figure 44

 44 Figure 44 Process at intermediate node ....................................................................................

Figure 45

 45 Figure 45 Chronogram of radio module's activities without aggregation ...............................

Figure 46

 46 Figure 46 Chronogram of radio module's activities with aggregation ....................................

Figure 47

 47 Figure 47 Explanation of model parameters ............................................................................

Figure 48

 48 Figure 48 Estimation of active duration for chain-based approach..........................................

Figure 49

 49 Figure 49 WSN energy consumption method's overview .......................................................

Figure 50

 50 Figure 50 Aggregation coefficients at node .............................................................................

Figure 51

 51 Figure 51 Data collection example with lossy aggregation .....................................................

Figure 52

 52 Figure 52 Transmitting duration for different size of data packet ...........................................

Figure 53

 53 Figure 53 Data processing flow of the message from node j ...................................................

Figure 54

 54 Figure 54 Root node is an intermediate node during data collection .......................................

Figure 55

 55 Figure 55 Gathering two chains into a tree structure ...............................................................

Figure 56

 56 Figure 56 Periodical data collection in tree structure ...............................................................

Figure 57

 57 Figure 57 Tree as a composition of chains ...............................................................................

Figure 58

 58 Figure 58 Data collection in different structures ......................................................................

Figure 59

 59 Figure 59 Radio interface consumption model for a node (one collection cycle) ...................

Figure 60

 60 Figure 60 Composed CM agent................................................................................................

Figure 61

 61 Figure 61 CM recursive architecture ........................................................................................

Figure 62

 62 Figure 62 Agent model in MAS-R ...........................................................................................

Figure 63

 63 Figure 63 Building recursive architecture with composition process ......................................

Figure 64

 64 Figure 64 The sequence diagram of composition ....................................................................

Figure 65

 65 Figure 65 Decomposition for aggregated CM ..........................................................................

Figure 67

 67 Figure 67 Cooperation of two elements ...................................................................................

Figure 68

 68 Figure 68 Cooperation process for a new element ...................................................................

Figure 69

 69 Figure 69 The first cooperation case: independent ..................................................................

Figure 70

 70 Figure 70 The second cooperation case: association................................................................

Figure 71

 71 Figure 71 The third cooperation case: aggregation ................................................................

Figure 72

 72 Figure 72 Data collection in independent concrete ................................................................

Figure 73

 73 Figure 73 Composition process for the construction of relationship matrix ..........................

Figure 74

 74 Figure 74 Aggregation of two elements .................................................................................

Figure 75

 75 Figure 75 Chain-based data collection ...................................................................................

Figure 76

 76 Figure 76 The materials of data collection platform ..............................................................

Figure 77

 77 Figure 77 USB wattmeter .......................................................................................................

Figure 78

 78 Figure 78 Radio module sleep mode test ...............................................................................

Figure 79

 79 Figure 79 Three-node chain structure ....................................................................................

Figure 80

 80 Figure 80 Estimated consumption of the whole chain ...........................................................

Figure 81

 81 Figure 81 Active duration of nodes during data collection without aggregation ...................

Figure 82

 82 Figure 82 Six-node chain testbed ..........................................................................................

Figure 83

 83 Figure 83 Test of 6-node chain structure................................................................................

Figure 84

 84 Figure 84 NetLogo McBIM energy simulator user Interface.................................................

Figure 85

 85 Figure 85 NetLogo McBIM energy simulator 3D view .........................................................

Figure 86

 86 Figure 86 Different structures in NetLogo MAS simulator ...................................................

Figure 87

 87 Figure 87 Virtual network and multi-agent architecture in NetLogo .....................................

Figure 88

 88 Figure 88 Energy consumption information obtained by NetLogo .......................................

Figure 89

 89 Figure 89 Active duration of leader for data collection with lossless aggregation ................

Figure 90

 90 Figure 90 Active duration of leader in data collection with lossy aggregation ......................

Figure 91 FND

 91 Figure 91 FND time for data collection with lossy aggregation ............................................

Figure 92

 92 Figure 92 Two independent communicating concretes ..........................................................

Figure 93

 93 Figure 93 Cooperation between two communicating material elements ...............................

Figure 94

 94 Figure 94 Cooperation between the aggregated agents ..........................................................

Figure 95

 95 Figure 95 Composition of two communicating concrete .......................................................

Figure 96

 96 Figure 96 Reconstructed network...........................................................................................

Figure 97

 97 Figure 97 Energy consumption of nodes per round ...............................................................

Figure 98

 98 Figure 98 Node lifetime for lossless aggregation ...................................................................

Figure 99 3 . 2 3 . 3 4 . 4

 99323344 Figure 99 Node lifetime for lossy aggregation .......................................................................

  in the context of McBIM project (Material communicating with the BIM -Building Information Modelling) (ANR Project McBIM, 2017). Based on the communicating material concept, McBIM project aims to design a "communicating concrete" which is equipped with an embedded wireless sensor network (WSN). This project is funded by the ANR (French National Research Agency) and coordinated by the CRAN (Research Center for Automatic control of Nancy) since the 1st of January 2018. The consortium is composed of four partners: the CRAN, two others research units (LAAS: the laboratory for Analysis and Architecture of Systems; LIB: Computer Science Laboratory of Bourgogne) and one company (360 Smart Connect/FINAO SAS).

Chapter 3 ,

 3 related to the literature on physical part, presents a power-based energy consumption model for communicating node. Based on this model, analytical energy models are proposed to estimate energy consumption for data collection with/without aggregation in WSN. In addition, analytical energy models for lossy or lossless data aggregation collection are also discussed for any kind of WSN topology structures. Chapter 4, related to the literature on digital part, details the proposed Multi-Agent System (MAS) recursive architecture and the communicating material agent model. Besides, composition/decomposition mechanisms are discussed, information handling and sharing between agents are detailed especially for energy management as well as the use for lifetime estimation and composition/decomposition.

1. 3 . 3

 33 Overview of Wireless Sensor Networks (WSN) ......................................................... 18 1.3.4 Multi-Agent System ..................................................................................................communicating concrete design ........................................................................... 20 1.4.2 Position of communicating concrete in the CM paradigm .......................................... 22 1.5 Proposed Holonic Approach and related research issues ............................................. 23

Figure 4 .

 4 It is composed of 3 different orthogonal axes: Intelligence level, Location of intelligence, and Aggregation level of intelligence.

Figure 6

 6 Figure 6 Applying Intelligent product to PLM

  (a) Set representation (b) Graph representation

Figure 10

 10 Figure 10 Representation of a communicating material

1. 4

 4 CM application in the ANR McBIM project 1.4.1 The communicating concrete design Based on the concept of CM, the ANR McBIM (Material communicating with the BIM -Building Information Model) project aims to design a "communicating concrete" which is equipped with an embedded WSN. The network architecture of communicating concretes is shown in Figure 12. There are two types of nodes: the sensing node (SN) and the communicating node (CN). The SNs measure relevant parameters and transmit measured data to CNs. The CNs then process, store and transfer the received data to the base station.

Figure 12 Memory

 12 Figure 12 Network architecture of communicating concrete

  , decomposition has been discussed for data storage and dissemination. In the McBIM project, communicating concrete can work independently by example to report its states during the manufacturing, or group with the others as an aggregate concrete during the construction. As consequence, the aggregation level of communicating concrete in McBIM project is the intelligent container.1.5 Proposed Holonic Approach and related research issuesThe digital part example for the management of real materials is shown in Figure15. Real communicating nodes (CNs) in real concrete are represented by virtual agents in the digital concrete. There are some benefits: firstly, these agents can store monitoring and product-related information to overcome the storage limitation of nodes; Secondly, this digital concrete allows to visualize energy evolution of the embedded WSN based on the received information which can be helpful for the maintaining or the exploitation. Moreover, agents can be grouped into a concrete agent which has the global view of the embedded WSN. This concrete agent can evaluate available collection strategies and then provide energy efficient solutions. By this way, this digital concrete overcomes the limit of processing power at nodes and increases intelligence level of communicating concrete.

Figure 15

 15 Figure 15 Digital part design for communicating concrete

Contents 2 . 1

 21 Physical part: Energy saving and estimation in WSN .................................................. 28 2.1.1 In-network data reduction ........................................................................................... 30 2.1.2 Data aggregation energy-efficient routing protocols................................................... 32 2.1.3 Time synchronization .................................................................................................. 42 2.1.4 Energy consumption models for WSN ........................................................................ 45 2.1.5 Synthesis ...................................................................................................................... 47 2.2 Digital part: energy management for communicating concrete .................................. 49 2.2.1 Holonic Control Architecture (HCA) .......................................................................... 49 2.2.2 Multi-Agent System (MAS) ........................................................................................ 51 2.2.3 Recursive holarchy or MAS ........................................................................................ 53 2.3 Synthesis .........................................................................................................................

Figure 17

 17 Figure 17 Architecture of sensor node

  : Radio optimization, Data reduction, Sleep/wakeup schemes, Energyefficient routing and Battery repletion.

Figure 18

 18 Figure18Classification of energy efficient mechanisms for WSN[START_REF] Rault | Energy-Efficiency in Wireless Sensor Networks[END_REF]

Figure 19

 19 Figure 19 MLDA problem in WSN

  Figure 29 RBS method

Figure 31

 31 Figure 31 Radio energy dissipation model

Figure 32

 32 Figure 32 Event trigger mechanism of NEM

Figure 33

 33 Figure33MSP430 and CC420 current consumption information[START_REF] Dron | An emulation-based method for lifetime estimation of wireless sensor networks[END_REF]

  coordination and operational level. The supervisor holons propose optimized execution plan to the task holons and monitor the execution of operating holons. When disturbance occurs, operational holons propagate the need for reorganization via the supervisor holon. The neighbor operational holons sense the pheromone and re-organize themselves into a heterarchical structure. Holons can then interact directly with the task holons to achieve an alternative schedule plan that minimizes the impact of the unexpected disturbance.

Figure 35

 35 Figure35ADACOR architecture[START_REF] Leitão | ADACOR: A holonic architecture for agile and adaptive manufacturing control[END_REF]

2. 2 . 3

 23 Recursive holarchy or MAS[START_REF] Mortellec | Embedded holonic fault diagnosis of complex transportation systems[END_REF] presents SURFER (SURveillance active FERroviaire, translated as "active train monitoring"), an embedded holonic fault diagnosis architecture for complex transportation systems (CTS) where train, vehicles and doors are considered as intelligent products. The generic holonic architecture was presented in Figure37. All elements are represented by Holons.

Figure 37

 37 Figure 37 Holonic architectural model for embedded diagnosis

Figure 38

 38 Figure38Social-based holonic architecture[START_REF] Esmaeili | A socially-based distributed selforganizing algorithm for holonic multi-agent systems: Case study in a task environment[END_REF]

  four different characteristics are used to analyze the presented works: application domain, architecture, formalization of agents and aggregation (or composition) mechanism. Application domain presents their applied field. The architecture records whether the approach can evolve with time or not (static or dynamic). Whether they proposed an agent model or composition mechanisms are recorded in formalization of agent and aggregation mechanism respectively.

=

  𝐸 𝑎𝑐𝑡𝑖𝑣𝑒 + 𝐸 𝑆𝑙𝑒𝑒𝑝 = 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 + 𝑃 𝑆𝑙𝑒𝑒𝑝 𝐷 𝑆𝑙𝑒𝑒𝑝 where Pactive is the mean power of the three active states (Tx, Rx, Idle) and Psleep is the power spentin sleep state. Dactive and Dsleep correspond to the duration in the active and sleep states respectively, mandatory to estimate the energy cost of data collection per round. All parameters used for the analytical energy consumption model are defined in TableIII.

Figure 43

 43 Figure 43 Process at the furthest node

Figure 44

 44 Figure 44 Process at intermediate node

Figure 45

 45 Figure 45 Chronogram of radio module's activities without aggregation

Figure 47

 47 Figure 47 Explanation of model parameters

  𝟏) * 𝟐 * (𝜶 + 𝜷) + 𝟒 𝜶 + 𝟑𝜷 + 𝒅 𝒔𝒚𝒏 , 𝒘𝒉𝒆𝒏 𝟐 ≤ 𝒊 ≤ 𝒏 -𝟏 (𝒊 -𝟏) * 𝟐 * (𝜶 + 𝜷) + 𝟑𝜶 + 𝟐 𝜷 + 𝒅 𝒔𝒚𝒏 , 𝒘𝒉𝒆𝒏 𝒊 = 𝒏 (11) Where 𝛼, 𝛽 and 𝛾 values are constant. (3𝛼 + 3𝛽); (4𝛼 + 3𝛽 + 𝑑 𝑠𝑦𝑛 ) and (3𝛼 + 2 𝛽 + 𝑑 𝑠𝑦𝑛 ) can be respectively replaced by constant values k1, k2, k3. The equation can then be simplified as follows:

=

  𝑛 * 𝑇 * 𝑃 𝑠𝑙𝑒𝑒𝑝 + ∑ 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 ) 𝑛 𝑖=1

  ): 𝐸 𝑡𝑜𝑡𝑎𝑙 = 𝑛 * 𝑇 * 𝑃 𝑠𝑙𝑒𝑒𝑝 + ∑ 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 ) 𝑛 𝑖=1 (18) = 𝑛 * 𝑇 * 𝑃 𝑠𝑙𝑒𝑒𝑝 + (2α + 2𝛽 + (𝑛 -1) * k 2 ) * ( 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 )With mentioned models, we can then estimate energy consumption for data collection with or without aggregation in a chain structure. The size of chain (N), the powers of radio module (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 , 𝑃 𝑠𝑙𝑒𝑒𝑝 ) and data collection period (T) depend on used communication module and the data collection scenarios. Due to the difference of active duration, energy consumption of nodes is different. A comparative estimation result for a 20-node chain is shown in Figure48where data collections starts from the node 1 until the node 20. The parameters 𝛼, 𝛽, 𝑑 𝑠𝑦𝑛 are fixed at 20 ms, 100 ms, and 10 ms respectively. The closer to the sink node, the longer its active duration for data collection without aggregation. Unlike the linear growth in active duration for the case without aggregation, all intermediate nodes have the same active duration for data collection with aggregation. In the case of data collection without aggregation, the more nodes there are, the more obvious the hot-spot phenomenon is. Therefore, using data aggregation techniques at intermediate nodes may be useful for extending network lifetime. This analytical energy model is compared with a physical platform to verify its accuracy (tests and results are described in section 5.1).

Figure 48

 48 Figure 48 Estimation of active duration for chain-based approach

Figure 50

 50 Figure 50 Aggregation coefficients at node

Figure 53

 53 Figure 53 Data processing flow of the message from node j

  𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 + 𝐷 𝑠𝑙𝑒𝑒𝑝 (𝑖) * 𝑃 𝑠𝑙𝑒𝑒𝑝 = 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * 𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 + (𝑇 -𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖)) * 𝑃 𝑠𝑙𝑒𝑒𝑝 = 𝑃 𝑠𝑙𝑒𝑒𝑝 * 𝑇 + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (𝑖) * (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 )

Figure 55

 55 Figure 55 Gathering two chains into a tree structure

Figure 59

 59 Figure 59 gives a visual representation of this model for an individual node transmitting data during one collection cycle. 𝑃 𝑠 gives the tendency between two collection periods. 𝑃 𝑎 gives the tendency during a collection cycle. The active duration of radio depends on (α, β, γ). The values of these parameters can be adapted for any wireless network communication technology. Active duration is fixed for data collection using aggregation. In case of a collection without aggregation, the active duration exponentially increases with the position of node to the sink node (revealing the well-known hot-spot problem in chain structure).

Figure 59 4

 594 Figure 59 Radio interface consumption model for a node (one collection cycle)

Figure 62

 62 Figure 62 Agent model in MAS-R

Figure

  Figure 65 Decomposition for aggregated CM

Figure 66 CM

 66 Figure 66 CM lifetime estimation process

Figure 69

 69 Figure69The first cooperation case: independent

Figure 71

 71 Figure 71 The third cooperation case: aggregation

Figure 72

 72 Figure 72 Data collection in independent concreteThe parameter assumptions for energy consumption estimation are given in TableVI. The size of minimal data packet Smin and maximal data packet Smax are 40 bytes and 100 bytes, their transmission duration are α m𝑖𝑛 (20 ms) and α m𝑎𝑥 (50 ms) respectively. The IFS 𝛽 is 10 ms. The size of ACK packet and of a unique monitoring data packet is 40 bytes, therefore, the time for transmitting a acknowledgment message is also 20 ms. The synchronization duration dsyn is set to 10 ms for each node.

  * 𝑃 𝑠𝑙𝑒𝑒𝑝 + 𝐷 𝑎𝑐𝑡𝑖𝑣𝑒 (4) * (𝑃 𝑎𝑐𝑡𝑖𝑣𝑒 -𝑃 𝑠𝑙𝑒𝑒𝑝 )

  𝑟𝑥 (9) = 𝑑 𝑠𝑦𝑛 + ∑ 𝑑 𝑡𝑥 (𝑗) ∀𝑗,𝑃(𝑖,𝑗)=1

Contents 5 . 1

 51 Validation of the energy consumption model for a chain structure .......................... 109 5.1.1 Chain-based data collection platform ........................................................................ 110 5.1.2 Energy consumption of Arduino nodes with XBee shield ........................................ 111 5.1.3 Data collection in 3-node chain ................................................................................. 113 5.1.4 Data collection in 6-node chain ................................................................................. 115 5.2 Validation of recursive architecture ............................................................................. 117 5.2.1 Analysis of energy consumption within a single communicating concrete .............. 119 5.2.2 Using the simulator to analyze the First Node Death time........................................ 122 5.2.3 Cooperation of communicating concretes ................................................................. 125 5.3 Analysis............................................................................................................................ 130

  Figure 76. c. Each node owns an ID between 1 and N, from the furthest node to the BS. The last node is connected to a PC via a USB cable that can not only transfer data but also provide the necessary power. The other nodes are supplied via standard 9v batteries. Data collection begins from the furthest nodes until the BS. The PC shows all received messages.

Figure 75 Figure 76

 7576 Figure 75 Chain-based data collection

Figure 77

 77 Figure 77 USB wattmeter

  Figure 78 Radio module sleep mode test

Figure 80

 80 Figure 80 Estimated consumption of the whole chain

Figure 81

 81 Figure 81 Active duration of nodes during data collection without aggregation

Figure 82

 82 Figure 82 Six-node chain testbed

A

  CM energy simulator has been developed for the implementation of the proposed analytical energy models and recursive architecture, whose user interface is shown in Figure84. It allows defining experimental parameters (network structure, energy consumption of battery, power and communication parameters of communicating module and so on…); implementing different data collection strategies (gathering without data aggregation, with lossless aggregation, lossy aggregation, or some other gathering strategies); analyzing statistical results (first node death time, or active duration of nodes during different phases, etc). Based on the defined parameters and strategies, this simulator allows to visualize the digital part of the WSN in a 3D view as shown in Figure85, where two communicating elements (in green and yellow) and their respective communicating nodes are represented as agents. In addition, the energy of nodes is represented by different colors.

Figure 84

 84 Figure 84 NetLogo McBIM energy simulator user Interface

Figure 86

 86 Figure 86 Different structures in NetLogo MAS simulator

  is completed. The aggregated agents have the global view (the relationship matrix P, the gathering matrix G and energy information) of their related network. Based on the network information, these aggregated agents can then compute the size of received messages and transferred messages and active duration for all members.

Figure 87

 87 Figure 87 Virtual network and multi-agent architecture in NetLogo

Figure 88

 88 Figure 88 Energy consumption information obtained by NetLogo

Figure 89

 89 Figure 89 Active duration of leader for data collection with lossless aggregation

Figure 93

 93 Figure 93 Cooperation between two communicating material elements

Figure

  Figure 95 Composition of two communicating concrete

Figure 97

 97 Figure 97 Energy consumption of nodes per round

Figure 98

 98 Figure 98 Node lifetime for lossless aggregation

  different phases, communicating concretes may cooperate with each other to construct a new network structure. In the proposed recursive architecture, three operators (PA, PO and PI) are detailed for the composition, cooperation process and message exchanges between agents. Energy estimation models are implemented in a 3D multi-agent simulator NetLogo for analyzing energy evolution in different cooperation possibilities. Aggregated agents are created automatically when the composition conditions are met. They have the global view of their members (the relationship matrix P, the gathering matrix G and energy information), and can then compute the size of received and transferred messages as well as active duration of all members. Based on the computed energy consumption information, the aggregated agent can provide us the remaining energy information (such as the FND time) which can be used to analyze different data collection strategies to extend lifetime in a resource constrained application like the McBIM project.

  Une architecture SMA récursive est présentée (chapitre 4) pour construire la partie digitale associée au matériau communicant. L'architecture proposée est récursive et flexible ce qui permet la composition/décomposition de matériaux communicants. La partie digitale peut fournir le niveau d'énergie restant mais aussi apporter des solutions coopératives pour répondre à une grande flexibilité du béton communicant.• Un simulateur de béton communicant 3D est développé avec la plateforme NetLogo, où les noeuds communicants sont représentés par des agents. Cet outil numérique permet de visualiser le niveau énergétique du béton communicant et fournit l'information de durée de vie restante (en section 5.2 du chapitre 5).Cette thèse est organisée en cinq chapitres suivis d'une conclusion générale :Le chapitre 1 présente le contexte et les concepts du matériel communicant, ses caractéristiques et son intérêt dans le cadre du projet McBIM. Cela commence par le contexte de la gestion du cycle de vie des produits. Le produit intelligent et le paradigme holonique sont ensuite décrits. Pour surmonter les limitations du produit intelligent, le concept de matériau communicant est proposé. Afin de gérer efficacement et de prolonger le cycle de vie du béton communicant, les solutions en RCSF sont discutées, tandis qu'une architecture holonique est proposée pour la gestion des données et de l'énergie. Enfin, les enjeux de recherche de cette thèse sont détaillés. Le chapitre 2 présente un état de l'art tant sur la partie physique que sur la partie digitale. Pour la partie physique, des solutions bibliographiques pour le traitement des données en réseau dans les RCSF sont présentées : fonctions d'agrégation de données, protocoles de routage efficaces, protocoles de synchronisation et modèles de consommation d'énergie. Pour la partie digitale, l'architecture holonique traditionnelle dédiée au produit intelligent est présentée, ainsi que les approches multi-agents pour la gestion de l'information. De plus, pour répondre aux exigences de composition/décomposition de l'application CM, des solutions récursives sont examinées et discutées. Le chapitre 3, lié à la littérature sur la partie physique, présente un modèle de consommation d'énergie basé puissance pour le noeud communicant. En se basant sur cette représentation, des modèles énergétiques analytiques sont proposés pour estimer la consommation d'énergie pour la collecte de données avec/sans agrégation dans un RCSF. En outre, les modèles d'énergie analytiques pour la collecte d'agrégation de données avec ou sans perte sont également abordés pour tout type de structure de topologie RCSF. Le chapitre 4, relatif à la littérature sur la partie digitale, détaille l'architecture récursive du système multi-agents (SMA) proposée et le modèle d'agent du matériau communicant. En outre, les mécanismes de composition/décomposition sont discutés, la gestion et le partage d'informations entre les agents sont détaillés en particulier pour la gestion de l'énergie ainsi que l'utilisation de cette approche pour estimer la durée de vie d'éléments et la composition/décomposition.Le chapitre 5 présente les résultats applicatifs et démonstratifs tant pour la partie physique (RCSF) que digitale (SMA). Les modèles énergétiques analytiques pour la collecte de données avec/sans agrégation sont validés avec une plateforme Arduino-XBee (dans la section 5.1). Sur la partie digitale, un modèle 3D basé sur des agents est développé sur la plate-forme Netlogo.Ce modèle peut être utilisé pour visualiser le niveau d'énergie du noeud de communication, analyser et fournir une solution optimale pour prolonger la durée de vie de la partie physique.

Chapter 1 Communicating Material: concept, context and positioning Contents 1.1 Product Lifecycle Management

  Definition of intelligent product .................................................................................. 10 1.2.2 Holonic paradigm ........................................................................................................ 14

........................................................................................ 9 1.2 Intelligent Product ............................................................................................................ 10 1.2.1 1.3 A new type of intelligent product: the communicating material ................................. 15 1.3.1 Definition of the communicating material .................................................................. 15 1.3.2 Holonic architecture for communicating material....................................................... 17

Table I .

 I Summary of data aggregation protocols

	Protocol	Structure-	Energy	Maintaining	Resilience to	Scalability
	name	based	efficiency	Cost	link failure
	LEACH	Cluster	--	+	+	--
	HEED	Cluster	+	-	+	+
	DWEHC	Cluster	++	-	++	+
	EADAT	Tree	-	--	-	+
	PEDAP	Tree	++	+	-	+
	TAG	Tree	+	++	-	+
	PEGASIS	Chain	-	--	-	--
	CRBCC	Chain	+	+	+	-
	BCBRP	Chain	-	--	++	--

Table II .

 II Summary table of recursive MAS

	Reference	Application	Architecture	Formalization	Aggregation
		domain		of agent	mechanism
	(Le Mortellec et al., 2013)	CTS	Static	No	No
	(Abdoos et al., 2013)	Traffic	Static	No	No
	(Sallez et al., 2015)	Logistics	Dynamic	No	Yes
	(Esmaeili et al., 2017)	LSMAS	Static	Yes	No
	(Occello et al., 2019)	WSN	Dynamic	Yes	Yes
	(Khelifa and Laouar, 2020)	UPP	Static	No	Yes
	(Nurdin and Muchallil, 2019)	Microgrid	Static	No	No

3.3 Analytical model for data aggregation in WSN

  Analytical model for data collection without aggregation .......................................... 68 3.2.2 Analytical model for Data collection with aggregation .............................................. 71

	3.1 Energy model of a communicating node
	During data collection, communication takes a large proportion of energy consumption. The
	radio communication modules are responsible for sending and receiving data. As mentioned in
	Chapter 2, distance-based energy model cannot be applied for communicating concrete
	applications. Therefore, a power-based energy model is used, where four different states are
	considered for the radio communication module:
	• Transmit (Tx): transmitting message to other nodes

Contents 3.1 Energy model of a communicating node ........................................................................ 62 3.2 Data collection in a chain communication structure ..................................................... 64 3.2.1 ............................................................. 73 3.3.1 Energy consumption estimation method in WSN ....................................................... 73 3.3.2 Model for tree structure ............................................................................................... 79 3.3.3 Model for other structures ........................................................................................... 82 3.4 Conclusion ......................................................................................................................... 83 This chapter presents the proposed analytical energy consumption models for periodical data collection in WSN. It begins with the energy model of a communicating node in section 3.1. Data collection process is detailed in section 3.2. The position-based analytical energy models for data collection without data aggregation in a chain structure are presented in section 3.2.1 and with aggregation in section 3.2.2. Based on the chain structure model, a generic energy consumption model for every kind of WSN structure is given in section 3.3. Then, a conclusion is presented in section 3.4. • Receive (Rx): receiving message from other nodes • Idle: active but not receiving or transmitting data • Sleep: inactive with a low energy cost Radio modules can shift into different states with some delays as depicted in Figure

Table III .

 III Proposed model's symbols

	Notation	Unit	Definition
	𝑬 𝒊	J	Communication energy consumption of node i
	𝑬 𝒕𝒐𝒕𝒂𝒍	J	Total energy consumption of network
	𝑷 𝒂𝒄𝒕𝒊𝒗𝒆	W/s	Power of active state
	𝑷 𝑺𝒍𝒆𝒆𝒑	W/s	Power of sleep state
	𝑫 𝒂𝒄𝒕𝒊𝒗𝒆 (𝒊)	s	Active duration of node i
	𝑫 𝒔𝒍𝒆𝒆𝒑 (i)	s	Sleep duration of node I
	𝑫 𝒕𝒙 (𝒊)	s	Duration for message transmission of node i
	𝑫 𝒓𝒙 (𝒊)	s	Duration for message reception of node i
	𝑺 𝒎𝒊𝒏	byte	Minimal size of transmission data packet
	𝑺 𝒎𝒂𝒙	byte	Maximal size of transmission data packet
	𝜶	s	Transmission duration
	𝜶 𝒎𝒊𝒏	s	Transmission duration for data packet of minimal size
	𝜶 𝒎𝒊𝒏	s	Transmission duration for data packet of maximal size
	𝜷	s	Inter Frame Space time
	𝜸	s	Time for ACK message
	𝒅 𝒔𝒚𝒏	s	Synchronization duration
	𝑻	s	Data collection period
	N		Chain size (Number of nodes in a chain)

  𝜆 42 𝜆 21 𝜆 11 𝜆 94 𝜆 42 𝜆 22 𝜆 94 𝜆 43 𝜆 33 𝜆 94 𝜆 44 𝜆 95 𝜆 55 𝜆 96 𝜆 66 𝜆 97 𝜆 77 𝜆 98 𝜆 88 𝜆 99 ]

						λ 11	0	0	0
				1 = [	λ 21 λ 11 0	λ 22 0	0 λ 33	0 0	]	(35)
					λ 42 λ 21 λ 11 λ 42 λ 22 λ 43 λ 33 λ 44
						λ 55	0	0	0	0
						0	λ 66	0	0	0
				𝐺 2 =		0	0	λ 77	0	0	(36)
						0	0	0	λ 88	0
				[	λ 95 λ 55 λ 96 λ 66 λ 97 λ 77 λ 98 λ 88 λ 99 ]
			𝜆 11	0		0	0	0	0	0	0	0
			𝜆 21 𝜆 11	𝜆 22		0	0	0	0	0	0	0
			0	0		𝜆 33	0	0	0	0	0	0
			𝜆 42 𝜆 21 𝜆 11	𝜆 42 𝜆 22		𝜆 43 𝜆 33	𝜆 44	0	0	0	0	0
	𝐺 =	[	0 0 0 0 𝜆 94 (37) 0 0 0 𝜆 55 0 0 0 0 0 0 0 0 𝜆 66 0 0 0 0 0 0 0 0 𝜆 77 0 0 0 0 0 0 0 0 𝜆 88 0

Table IV .

 IV a. Knowledge part K concerning product-related information

	Agents	Node ID	Current Energy Eres	Element ID	Agent ID	Abstraction Level	Parent Agent
	CM0 4

Table VI .

 VI Parameter assumptions

	Parameters	Value	Unit
	𝜶 𝐦𝒊𝒏	20	ms
	𝜶 𝐦𝒂𝒙	50	ms
	𝜷	10	ms
	𝜸	20	ms
	Smin	40	byte
	Smax	100	byte
	dsyn	10	ms

  The comparison of the estimation given by our energy model with the real test values leads to prediction errors for node 1 to node 3 of 1.98%, 4.48% and 5.93% respectively (errors computed with (49)). The average prediction error is 4.13%.The active durations for data collection with aggregation from node 1 to node 3 are 245 ms, 391 ms and 380 ms respectively, where the estimated values are 240 ms, 390 ms and 390 ms. In this test, the active durations for intermediate nodes (nodes 2 and 3) are almost the same. As a conclusion, the hot-spot problem is avoided by using data aggregation. We also observe that the furthest node works less time than the others. Because it does not have a child, no ACK reply is required. For this experiment, the errors between the real test and our energy estimation model are 2.04%, 0.26%, 2.63% from node 1 to node 3 respectively. The average error is 1.64%.

	𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑒𝑟𝑟𝑜𝑟 =	𝑎𝑏𝑠(𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡-𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑐𝑜𝑠𝑡) 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡	(49)

Our estimation model is adapted for both tests.

Duty cycle plays an important role for energy consumption at nodes. The average duty cycles from node 1 to 3 are respectively 7.08%, 12.04%, 14.16% in case of data collection without aggregation. These values are reduced to 4.90%, 7.82% and 7.60 % with data aggregation.

Table VII .

 VII The average estimation errors from node 1 to node 6 are respectively:

	4.85%, 3.33%, 6.18%, 8.53%, 6.15%, 0.94%. Therefore, the average prediction error for the
	whole WSN is around 5%.
					Comparison of active duration
		1600
		1400					1350	1370
								1357,273
	Average active duration (ms)	400 600 800 1000 1200	360	630 609,697	870 819,394	1110 1022,727	1271,818
		343,333 200
		0
			1	2	3	4	5	6
					Node ID
					Estimated value	Real value
				Figure 83 Test of 6-node chain structure

Table VII .

 VII Estimation errors of activity durations in six-node chain structureBesides, the average error for data collection with aggregation is also similar. Both tests prove the correctness of our energy estimation model for a chain-based structure. This model can give fast and relatively accurate energy cost results and can help to know the embedded WSN lifetime. In the following section, this analytical energy model and the proposed recursive architecture will be implemented in a multi-agent simulator to verify its usefulness and analyze the lifetime evaluation of nodes.

	N_ID	Estimated value	Measured value	Absolute error	Percentage prediction
		(ms)	(ms)	(ms)	error
	1	360	343.333	16.667	4.85%
	2	630	609.697	20.303	3.33%
	3	870	819.394	50.606	6.18%
	4	1110	1022.727	87.273	8.53%
	5	1350	1271.818	78.182	6.15%
	6	1370	1357.273	12.727	0.94%

Acknowledgements

First of all, I would like to express my sincere gratitude to my supervisor Prof. William DERIGENT for his support and encouragement throughout my PHD study. His passion and

LIST OF ACRONYMS

ACK recursive way. For example, the area territory holons can be divided into areas in the middle level and sub-areas in the low level. The authors use multi-objective ACO (Ant colony) algorithm to optimize the urban planning from the bottom level to the top level (from the subarea to the Area Territory). Besides, a H-MACO (Holonic multi-objective ant colony optimization) algorithm is proposed to improve the global urban planning with the global objectives. This method can achieve an efficient solution for large scale applications where the network is static. Nevertheless, the reconstruction of network is not discussed.

Figure 40 Three-level holonic architecture for urban planning [START_REF] Khelifa | A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm[END_REF] Nurdin and Muchallil propose a Rol-Interaction-Organization (RIO) model based holonic multi-agent system for the control system in microgrid [START_REF] Nurdin | Holonic Multi-Agent System for Microgrid Hierarchical Control[END_REF]. The holonic architecture is present in Figure 41. There are four types of roles in this structure: the part, the head, multi-part and the StandAlone. A part holon cannot directly communicate with holons of other groups; A head of a group is responsible for collecting and disseminating information to the group members; a multi-part holon is the one in two different groups; the StandAlone is the holon which can communicate with the holon outside the groups. Although the authors present the satisfaction module to define the role of agents, the selection of head in a group and the interactions between holons are not specified.

If all nodes have the same material characteristics, tendencies (slopes induced by Ps and Pa) should be the same for all nodes involved in a chain. The duration is the only variable changing and impacting the energy consumption of each node. Our models allow quantifying the potential energy gain involved by using (or not using) aggregation for chain data collection. Because these parameters do not evolve in time and if, during application, the required collection frequency does not change too, the total energy spent by a node can be directly obtained with the number of collection cycle. In this approach, the consumed energy at node depends on device characteristics (Pa and Ps), on the communication structure (chain) and on the collection and network communication protocols (activity duration).

In addition, a relationship based analytical consumption model is proposed for all types of WSN structures. The relationship matrix P depends on the used routing or network structuring protocol. The gathering matrix G can be calculated with P and with aggregation coefficients 𝜆 at nodes. The size of received message 𝑀 𝑟𝑥 and transmitted message 𝑀 𝑡𝑥 can be computed by the matrix P and G. Messages sizes are used to calculate duration in active state to estimate residual energy for each node.

In conclusion, these simple and effective models can be used online by nodes for remaining energy self-assessment. Distributed routing algorithms can use the prediction consumption to determine or reconfigure the communication structure. Models can also be used by an external software control system to have a global and centralized vision of the spent energy of all nodes of the network. The cooperation between elements is analyzed via a multi-agent simulator (see section 5.2). To understand how the simulator was developed, next chapter (Chapter 4) presents and describes our second proposal: a recursive multi-agent architecture, and how it can be used to compose or decompose communicating materials along their lifecycles and/or to estimate residual energy of WSN nodes. Compared with the independent case, leader (node 9) consumes more energy for data collection in this example of cooperation. However, the energy consumption of the node 4 decreases, because it does not need to directly transmit data to the BS. In this case, the CM agent in the highest level has the global view of the communication relationship and energy information of all members. This agent can evaluate and select suitable data collection solution to limit and balance the energy spent by the real Communicating Nodes and so extend CM lifetime.

Conclusion

In for transforming relationship of agents from low level to upper level. The application for energy consumption estimation with proposed analytical models is also discussed in the case of data collection within single element and cooperation between elements in section 4.6.

In the proposed recursive architecture, the agent in the highest level has the global view of all members (the communication relationship, gathering matrix, energy level and so on). This agent can adjust data collection strategies to meet environment changes (such as the change of product lifecycle phase). In the following chapter, the accuracy of the analytical energy consumption model is analyzed with data collection test in a physical platform (section 5.1).

The proposed recursive architecture and composition mechanisms will be implemented and verified via a multi-agent simulator (see section 5.2).

General conclusion and future works

This thesis presents the communicating material paradigm as well as a holonic architecture for data and energy management. The study case is the McBIM project where concretes are equipped with embedded WSN. In the proposed architecture, the communicating material is represented by a virtual agent in digital part, which can store product-related information and provide optimal solutions to meet environment changes and extend the lifetime. As the communicating concrete should work ten or twenty years, energy management is relevant for this resource constrained application. As consequence, this work proposes analytical energy estimation models for periodical data collection with WSN to provide a rapid and approximated energy information. Besides, communicating concrete may cooperate with each other to build larger network and save energy. As a result, a recursive MAS architecture is proposed to efficiently manage informational part (the agents in digital part). As the analytical energy model is implemented in the digital part, energy efficient strategies may be provided with a global view and an optimal solution to extend product lifetime.

The chapter 1 reviews the use of intelligent product in product lifecycle management and In the second part of the chapter 5, the proposed recursive architecture, composition and cooperation processes are implemented in the 3D multi-agent simulator NetLogo. An aggregated agent is created once the composition process is completed in the simulation. The aggregated agent in the highest level has the global view of all members (their communication relationship P, their gathering matrix G and their energy information, etc…). In the case of