Andre Rocha
email: ad.rocha@campus.fct.unl.ptldr

Luis Ribeiro

José Barata

A Multi Agent Architecture to Support Self-organizing Material Handling

Keywords: Multi Agent, Transport System, Material Handling, Self-Organization, Load Balancing, Architecture

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The transport system is usually composed by a network of conveyor belts, AGVs or both. These shop floor components have the responsibility to route material across the system to the different resources.

The load balancing optimization problem, as approached traditionally, normally disregards the role of these elements and perceives the transport system as a more or less passive entity. From an Assembly Line Balancing (ALB) perspective [START_REF] Boysen | Assembly line balancing: Which model to use when?[END_REF] several approaches have been traditionally considered. The Simple Assembly Line Balancing Problem was defined to cover the different manufacturers' goals, with four different variants. The SALBP-E aims to maximize the efficiency of the production line, SALBP-1 has as main goal the minimization of the number of stations needed in the system. If the goal is to minimize the execution time, knowing the number of stations in the system, the SALBP-2 formulation is considered. The SALBP-F problem formulation attempts to balance the optimization between number of stations and the desired time of execution [START_REF] Boysen | Assembly line balancing: Which model to use when?[END_REF].

The formulation of these problems only produce results, directly applicable to real systems, in very specific cases.

The ALB approach is therefore very dependent on the system characteristics and on the goals of a specific manufacturer and assembly installation. There is a gap between the academic models and simplifications, and the real scenario.

To cope with the pressing requirements, several approaches have been proposed. These new approaches have resulted in new production paradigms namely: Bionic Manufacturing Systems [START_REF] Ueda | A concept for bionic manufacturing systems based on DNA-type information[END_REF], Holonic Manufacturing Systems [START_REF] Van Brussel | Reference architecture for holonic manufacturing systems: PROSA[END_REF][START_REF] Babiceanu | Development and applications of holonic manufacturing systems: a survey[END_REF], Reconfigurable Manufacturing Systems [START_REF] Koren | Reconfigurable manufacturing systems[END_REF] and Evolvable Assembly Systems [START_REF] Onori | An architecture development approach for evolvable assembly systems[END_REF]. The present approach is proposed under the scope of Evolvable Assembly Systems (EAS) and is an improvement of the IADE architecture [START_REF] Ribeiro | Self-organization in automation-the IDEAS pre-demonstrator[END_REF][START_REF] Rocha | An agent based architecture for material handling systems[END_REF][START_REF] Ribeiro | A product handling technical architecture for multiagent-based mechatronic systems[END_REF] that integrates control and reconfiguration with material handling aspects. Like IADE, the proposed architecture supports a transport system constituted by conveyors and implements the necessary mechanisms that allow, at each moment, the transport system elements to calculate optimal routes and knowing status of the entire transport network. The transport elements restricts their interaction and information exchange to a local scope and rely in self-organization to support the dynamics of the transport system. The main improvements over the IADE architecture is related to the interactions between the agents that have been streamlined to improved performance, plug-ability and, as importantly, the ability to formalize and guide the structure and collective behavior of the transport system.

Relation With Collective Awareness Systems

Manufacturing is, worldwide, a strategic sector from a socio-economic perspective. Hence there is a growing body of technologies that serve the purpose of integrating production plants with higher level logistic and management tools.

Although already significantly automated the existing technologies are not yet able to promote to the desired extent multilevel collective awareness in manufacturing.

Improving the self-configuration abilities of current shop-floors is decisive for the production sector and enables new ways of enterprise organization. By making systems more intelligent and aware, making their users aware on why the system is evolving, or proposing to evolve, in a certain direction and enabling the users to understand the impact of designing and managing a system under certain conditions contributes to the creation of more sustainable production practices while open up the door to tackled emerging business opportunities.

Awareness implies instant access to information but not only any assess. It implies an intelligent infrastructure that is able to understand and triggers different actions from its components and users so that the overall system converges to some target functioning points.

The proposed work focus on creating collective awareness at factory level, in particular in the transport elements which are envisioned as a fundamental pillar of the overall logistics, and promoting the generation of knowledge that can be used at other levels to promote collective awareness in manufacturing.

In particular this knowledge can be used to upgrade/update the system increasing its performance and efficiency and hence its sustainability.

Related Work

Several approaches and concepts have emerged to circumvent the limitations of traditional ALB. The Automated Material Handling Systems (AMHS) [START_REF] Haneyah | Generic planning and control of automated material handling systems: Practical requirements versus existing theory[END_REF][START_REF] Haneyah | A generic material flow control model applied in two industrial sectors[END_REF] are very used in industry. The AMHS allow an automated routing of materials between stations and along different routes. However, these systems are difficult to modify. Their reduced flexibility, with respect to dynamic route management makes their usage restricted to a few systems and application scenarios. In order to develop approaches capable to tackling these problems, many researchers are focused in the use of multi agent-based architectures [START_REF] Shen | Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey[END_REF]. Multi agent-based design although not solving complex problems by default may, in certain cases, simplify some modeling aspects of the problems and enable a self-organizing approach that, not providing optimal solutions, still offers an efficient solution in a suitable time frame. In [START_REF] Shen | Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey[END_REF][START_REF] Guo | An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing[END_REF] a multi agent approach is proposed to eliminate the combinatorial explosion associated with traditional scheduling. The test discussed in [START_REF] Shen | Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey[END_REF] suggests that a centralized Holonic approach attains scheduling solutions close to the optimal solution in the tested scenarios. A similar approach is found in [START_REF] Turgay | Agent-based FMS control[END_REF][START_REF] Rubrico | Online rescheduling of multiple picking agents for warehouse management[END_REF] in the context of FMS. More bio-inspired approaches based on ant foraging and stigmergy to explore in a distributed way alterative paths in an assembly system. are reported in [START_REF] Peeters | Pheromone based emergent shop floor control system for flexible flow shops[END_REF] were some conceptual principles that can drive such a system are presented and justified while in [START_REF] Sallez | A stigmergic approach for dynamic routing of active products in FMS[END_REF] an architecture is proposed to use faster than real time simulation to provide continuous adjustments to the physical system. These approaches are not designed to explore and learn from the structure an organization of the transport system. In this context, the long term purpose of the research detailed in this paper is to investigate:

• What is the impact of network topology and distinct self-organization metric in the overall performance of the system? The work hereby detailed is the first step towards this ambitious goal and relates with the definition and justification of the reference agent based transport architecture.

Agent Architecture

Notion of Skill

The agents in the architecture later detailed expresses their abilities as skills [START_REF] Barata | Coalition based approach for shopfloor agility[END_REF]. In this context, each skill has an interface that contains all necessary information for this execution. This information contains among other things the skill type. The type is important for the execution process and can assume one of two values: Atomic Skill or Complex Skill. Atomic Skills are responsible for the low level execution (hardware level i.e. I/O management) associating the skill data to a specific controller. The Complex Skill is responsible for executing processes of high complexity, these skills are constituted by a work-flow. This workflow is constituted by Atomic Skills or Complex Skills.

Generic Agents

The proposed architecture similarly to the IADE architecture is composed by five type of agents, although two of the agents are different as detailed in Table1. Table 1 describes the functional differences between the agents from the two architectures. Abstracts the entry point of the system. This agent is used to enable products to enter the system.

It is responsible by the product entry, like in IADE architecture. The SoA is now perceived by the system, as a node (HA) and has the ability to route any product.

Sink (SiA)

Abstracts the products' exit points in the system.

Similarly to the SoA besides taking products from the system also behaves as an HA.

Agent IADE architecture New architecture

Transport Entity (TEA) / Conveyor (CA)

(TEA) This agent is able to abstract a conveyor belt defined between two HAs. This entity controls the execution of all the stations plugged and associated with a conveyor and all queues between these stations. This agent is also responsible for managing the plug and unplug of stations to and from the docking points inside the conveyor (CA) It is responsible to control the product transition between two other entities in the transportation system (i.e. HA, SoA, SiA, DPA). This new agent doesn't contain any docking points, so it doesn't manage these operations as in the IADE stack.

Docking Point (DPA)

This entity abstracts the points in the system where it is possible plug stations. These points in the previous architecture were controlled by the TEA. During execution, this entity controls the product execution in this point of the system. In other situations has the same behavior as HA.

Yellow Pages (YPA)

It provides an improved yellow pages service. All agents in the platform can use the service to find other agents or skills, without a complete specification.

Conceptual Assessment of the Architectural Changes

The main architectural differences are related to the entities that abstracts conveyor belts. The Conveyor Agent (CA), which is a new entity, is less complex than the previous case. The original specification, detailed in [START_REF] Rocha | An agent based architecture for material handling systems[END_REF] was strongly inspired by the typical mechanical design of conveyors and stations' docking points. In this context, each conveyor contains several points where a station can be plugged. Each one of these docking points has a pre-stopper that prevents a pallet from entering the station when another pallet is already there and an additional stopper that fixes a pallet in place when is operated by the station. This creates several queues that precede each station inside each conveyor. In the IADE stack the user is allowed to add a docking point to an existing conveyor and the TEA autonomously resizes the queues while reindexing the station's positions. Furthermore the TEA has to manage all the traffic inside all these queues. This also renders the interactions between the routing devices and the TEAs more complex since the routing devices have to index all the stations associated to a conveyor replicating already existing information.

There is an improvement in information management and performance. It may appear that this change prevents the dynamic addition of docking points however, from a mechanical point of view the introduction of a new docking point normally entails down time. In this context the tradeoff is that when a new point is introduced in the IADE architecture the user has to stop the conveyor and connect the docking point and then restart the conveyor reconfiguring it according with the new stations; in the current version the current TEA/CA is dissolved and two new CAs are created before and after the docking point. The overall reconfiguration effort is comparable with the adequate support tools. There is also a better isolation of the agents functions. The CA is mainly responsible for calculating its traversing cost. The docking point manages the stations and the skills therein and the HA is solely responsible for the computation of the best routes.

The new architecture also facilitates the modeling of the system as a network whereby transport entities, which do not have to be restricted to conveyors, behave as links and all the other entities as nodes. This new view if fundamental to study the impact of network topology in the performance of the system as it enables the extraction of configuration patterns that can improve or degrade performance. For instance is a transport system built after a small world model more efficient that the traditional line? Is it more robust? How is if affect by different metrics of transport cost?

The data model, discussed in the next section has also been improved to facilitate scalability and the specialization of new classes that can be seamlessly incorporated in the present architecture. In the IADE architecture a flat data model is followed and specialization of classes is not allowed.

Implementation

A simplified representation of the new data model is presented in Figure 1. All the classes are derived from the transport entity concept. A transport entity is something that is able to store products and can receive or dispatch them. A conveyor is, in this context a transport entity that can compute its traversing cost using a specific metric. Each conveyor has its specific metric with a formula to calculate its traversing cost and a threshold value that defines when this cost should be updated in the neighbor HAs resend the cost update.

A conveyor is a link in the transport network as would be an AGV (not considered in this initial version of the stack).

The network's nodes are treated as routing entities. These connect several conveyors and can compute the optimal routes according to the current system status. This computation uses the dijkstra algorithm which is computed in steps inside the agent scheduler to ensure a non blocking performance by the JADE based agents. Some of these nodes are specialized on skill management. For instance docking points are able to handle stations and their skills, This mediates the execution of skills and, plug and unplug actions. The source agent processes the state update from the docking point agents and uses this information for route the products when these enter in the system.

A sink is a specific node where it is possible remove the products when they end their execution. From a technical point of view the implementation uses the Java Agent Development Environment (JADE). Although JADE is cannot fulfill hard real time constraints its performance [START_REF] Ribeiro | A study of JADE's messaging RTT performance using distinct message exchange patterns[END_REF] is still acceptable to handle the dynamics of most transport systems.

Conclusion and Further Work

In the IADE architecture it was possible to prove that a self-organized response can be used to control a transport system in a robust manner and with acceptable performance. The architecture presented in this work improves in IADE conceptually and from a performance point of view as the role of the agents has been streamlined and the overall number of interactions was reduced. The main architectural optimization is the addition of DPA. This new agent manages the points of the system where it is possible plug and unplug stations. The CA, previously the TEA, consequently was simplified, and this simplification reduces the complexity and the computation. This reduction increases the performance of the entire system. The stabilization of the architecture and the test currently ongoing are however only the starting point of a far more ambitious work that related with the development of selforganization metrics to regulate the adaptive response of agent-based transport systems.

Fig. 1 .

 1 Fig. 1. Class Diagram of new architecture

Table 1 .

 1 Differences between IADE architecture and the new architecture

	Agent IADE architecture	New architecture
	Handover	(HA)	This entity is responsible to route the products. When a product arrives, this agent consults his routing table and checks what is the destination of the product. It checks, with this information, the next hop (Handover) and which Conveyor Agent or Transport Entity Agent can transport the product to the next hop.
	Source	(SoA)


Nelson Rodrigues

Eugénio Oliveira

Paulo Leitão
email: pleitao@ipb.pt

Self-organization Combining Incentives and Risk Management for a Dynamic Service-oriented Multi-agent System

Keywords: Multi-agent systems, Self-organization, Service-orientation, Risk Management

Companies are nowadays placed in very complex and dynamic environments, making their competitiveness mandatory. This competitiveness can also be achieved through the reconfiguration of systems' network. In this paper, high level of self-organization of service-oriented multi-agent systems is explored, aiming to achieve more trustworthy and automatic reconfiguration processes, in dynamic and open environments. The correct self-organization model directly impacts the success of agents' behaviours to actively change or create new appropriate services dynamically. This paper advocates on the influence, in a distributed and cooperative way, of risk management, similarities and incentives to work together in order to speed up the selforganization agent's network. By leveraging the SOA and MAS benefits it is possible to reduce time, effort and money. Based on identified benefits and needs, several research leads for my thesis plan are here proposed towards the realization of self-organization capabilities aiming to accomplish more trustworthy and automatic reconfiguration processes, in volatile and open environments.

Introduction

Innovative solutions and technologies were created to solve the companies' problems that are playing in very dynamic and competitive environments, subject to the markets pressures demanding highly customized and quality products at reduced price. Companies, to increase their competitiveness, need to implement more flexible and responsiveness systems [START_REF] Colombo | Service-oriented architectures for collaborative automation[END_REF] and increase their participation in cooperative networks, which request different solutions. Currently, decentralized approaches are being pointed to address this challenge [START_REF] Leitão | Agent-based distributed manufacturing control: A state-of-the-art survey[END_REF], based on the distribution of control functions over a network of decision-making entities. Multi-agent systems (MAS) and Service Oriented Architectures (SOA) are two examples of such approaches, based on the same principles of distribution, which can be combined to extract benefits from the two worlds: the intelligence and autonomy provided by MAS solutions and the interoperability offered by SOA solutions [START_REF] Huhns | Agents as Web services[END_REF]. The addition of dynamic and automatic reconfiguration processes of a distributed system [START_REF] Maximilien | A Framework and Ontology for Dynamic Web Services Selection[END_REF], e.g. through adapting or creating the services offered by the several intelligent entities to the new requirements, implies a deeply intelligent orchestration of services. The aim of this paper is to describe an innovative self-organization approach based on incentives mechanisms and risk management in service-oriented multi-agent systems, aiming to achieve more trustworthy and automatic reconfiguration processes, in volatile and open environments.

This paper is organized as follows. Section 2 describes the similarity of the work with the collective awareness systems. Section 3 presents the literature review and Section 4 briefly presents the paper's contribution as well as the formalization of the proposed model. Section 5 overviews the critical analysis of this proposal and states forward the research directions. Finally, Section 6 wraps up the paper with the conclusions.

Relationship to Collective Awareness Systems

This contribution is related to collective awareness systems, since it uses a MAS approach to distribute the knowledge and decisions points, being necessary to perform the management of the agent's colligations that are self-organized and to select the best service. This research needs to be driven to the collective awareness system, since the agents behaves as a global brain to offer better services; local knowledge about the environment allows the agents to take local decisions, which has a huge global impact. Additionally, the management becomes critical since too many agents communicating can imply the congestion of the agent's network.

Literature Review

This section reviews the current state of the art in several related fields, providing the initial context around service-oriented domains, and how agents and risk management are being used. To determine the value of risk management, it is considered trust and reputation values to measure threats and predict uncertainty. In a quick perspective, agents are seen as intelligent entities that offer support for robustness, flexibility and adaptability, in case of management of services [START_REF] Huhns | Agents as Web services[END_REF]. For a better autonomy process, they follow decentralized approaches [START_REF] Khan | Enabling Negotiation Between Agents and Semantic Web Services[END_REF] that require the cooperation between the agents. In some situations, this cooperation can take a form of competition, being sometimes necessary to request advices or make alliances, which require the consideration of the level of the risk management (based on trust and reputation).

Fig. 1 summarises the important works focusing individually each one of the referred domains. Once analysing Fig. 1 it is obvious the reasons that motivated this study. Some of these approaches are able to support SOA [START_REF] Lai | Incentives for Cooperation in Peer-to-Peer Networks[END_REF], [START_REF] Ye | Self-organization in an agent network: A mechanism and a potential application[END_REF], [START_REF] Nguyen | Combining Trust and Self-Organization for Robust Maintaining of Information Coherence in Disturbed MAS[END_REF], however they do not introduce the self-organization paradigm. On other hand, the other works lack on components that covers this work's interests (self-organization phenomena achieved by using incentives and risk management metrics supported by multiagents). Whenever a network of agents follows an overall goal, and this network does not encourage the cooperation, incentive mechanisms can improve this cooperation [START_REF] Centeno | Mecanismos Incentivos para la Regulación de Sistemas MultiAgente Abiertos basados en Organizacione[END_REF]. Additionally, is also considered works that look into the self-organization phenomena, meaning approaches that evolve by small cooperation for a greater goal. Essentially there is lack of proposals with self-organization that have in common multi-agent systems and risk management models, which means, an opportunity to research in self-organized systems.

Research Contribution and Innovation

Complex systems may benefit from self-* characteristics since the agents should be able to change their own behaviours facing the overall system. The individual agents use an intelligent behaviour to actively dynamically change or create new services, without a request but instead in a response to a condition change. As previously stated, the literature review demonstrates some efforts in decentralized approaches to solve the dynamic and competitive environments. However, the existing works lack in some important points, as described previously. This work aims to propose selforganization and incentive mechanisms that can offer a dynamic and automatic self-* properties (e.g., reconfiguration, adaptation) based on agent networks, where each agent combines the learning and risk management about the interaction and cooperation to evolve and offer better services. The system, as a whole, must also find opportunities to evolve by analysing their dynamic environment, the agents should be able to self-configure and self-organize, allowing increase of the system performance on-the-fly. Additionally, this approach pretends to decompose the risk management in trust and reputation models that allow the agents to work with another level of confidence when faced by unexpected scenarios, being capable of evolving in its configuration to a state where there is a better state in order to offer new/modified services with offer highly customized and trustworthy services with better quality.

Self-organization Mechanism

The addressed research challenge is to move the system from weak connections to strongest ones, as illustrated in Fig. 2 (note that the strength of the connections represents the degree of similarities with the neighbours). By applying selforganization methods, the network self-organize, passing from one specific network topology to another one more consistent and structured. Reminding that the domain is dynamic, therefore it is covered the Open MAS (OMAS), where each agent belong to a specific open society over the network. A self-organized network is essential to create a consistent network, nonetheless some confidence is needed when the agents are being created and organized among themselves. Having this in mind, it is proposed a self-organization model based on incentives, risk management and homophily [START_REF] Del Val | Decentralized semantic service discovery based on homophily for self-adaptive service-oriented MAS[END_REF], where individuals share common characteristics (e.g. beliefs, values and education). Incentives, risk management and homophily enables to create a network based on the similarity and confidence of the agent's neighbours but also on the learning of individual players. In this approach, the self-organization behaviour takes into account incentive mechanisms that are used to stimulate this cooperation. A representative part of the proposed approach is shown in Fig. 2. After a self-organization phenomenon has occurred in a distributed and decentralized system, robustness has increased, allowing surviving without some entities or with incomplete information. Self-organization is structured (Def. 1), which represents the function to achieve or not society and the connections. The model of agents also represents the strength of connections, which can evolve to create a proper network towards the goal, depending on the network topology. In order to increase the cooperation, incentive mechanisms will allow the formation of more stable colligations. In practice, the incentives are given from a normative environment, meaning that there is no supervision entity obligating to accept these incentives.

Definition 2. Mechanism of incentive (In) based on the Centeno's work [START_REF] Centeno | Mecanismos Incentivos para la Regulación de Sistemas MultiAgente Abiertos basados en Organizacione[END_REF] is represented as a tuple ( , , , ) [0,1] In St Oenv Soc fIn →

, where the agent has incentives to create coalitions of the environment and can accept or ignore the incentive depending on the impact it will have on their utility.

• m 1 {St ,...,St } St =
represents the internal states of an agent;

• : [0,1] fIn St Oenv Soc × × →
represents the incentives mechanism function; Taking into consideration that incentive mechanisms should be tuned for specific topologies, it is realized that to establish weak connections it is required greater incentives, and stronger connections do not require higher values. The selforganisation methodology has a direct relation with the strength of the connections, which means that stronger connections allow fastest and stronger reorganization. Working in an OMAS has a certain amount of risk, and a stronger and reliable connection structure must be created. Gathering the concepts of trust and reputation models it will be possible to create a risk management model. represents a set of recommendations, given by third-party recommender Tp of a trustor agent Xtr; Following the principle that the level of risk depends on trust and reputation of certain agents and certain goals at given times for example, the risk shifts if there is no trust/reputation or only for certain goals. The risk level suffers alteration according to time, if the interaction occurs in the last minute or a week ago the risk level is not the same. Social structures allow to create better organizations without central decision points. One of the social structures characteristics is the homophily, based on the similarity degree of other entities [START_REF] Del Val | Decentralized semantic service discovery based on homophily for self-adaptive service-oriented MAS[END_REF] • : [0,1] fSim Sv Sv Ro × × → represents the degree function; It is expected that after a self-organization procedure, the system continues to have weak connections, and others became stronger (see Fig. 2). This cooperation among the entities automatically increases the probability of achieving objectives more effectively as predict future events.

Agent's Internal Model

The OMAS structure has several agents with several behaviours, outstanding to the complexity, an autonomic process should be developed for the agents behaviours. Inheriting these characteristics from the computer science, an autonomic system should remain self-configured, self-optimized, self-healed and self-protected. The agents developed should be intelligent enough to trigger the self-healing and selfprotection behaviours, this means, learning when non-intentional accidents could occur, or have the intelligence to detect, diagnose and repair such accidents.

This insight fulfils very well the system architecture in two separated layers: in one layer it is represented layer, namely the agents, and in the second layer, each agent is responsible to offer the services. Definition

' Ag St Oenv K ft hUT Sv St → 5. The agent's internal model is specified as ( , , , , , )
where St' represents the next state, K signifies the knowledge of agent of its set of direct neighbours inside the society, ft is the transition function :

' ft Oenv St St × →
, and hUt represents the utility function of each agent for a specific state and knowledge : hUt St Oenv K R × × → (remember that not all topologies are motivated to reach its maximum utility [START_REF] Barabási | Emergence of scaling in random networks[END_REF]);

The agent tends to maximize their own utility, although this local maximization might sometimes not be the best for the global system. Without the supervision, the agents need to collaborate to achieve the global maximum. At this point, and since the objective is to provide services more trustworthy, the agents intend to increase their risk management accuracy Just like in the self-organization, the agents will try to compose the best service in on-going loops. From a strategic perspective, the monitoring of services for planning, e.g. functionality and availability, is controlled. Other perspective is the focus on the operational task, which explore algorithms to compose services, to achieve the goals. Summing up, the system tends to evolve and organize itself taking into account statistical fluctuations and internal agent's history. Self-(behavioural and structural) organization are applied, by changing the agent's behaviours and network structure.

Discussion of Results and Critical View

The insights of this work are to create a vision and to formalize concepts necessary to future lines of research. Aiming to verify the potentiality of this approach, two case studies are explored. The first in one business-to-business (B2B) and Virtual Enterprises (VE) domain, by exploring the benefits of using decentralized entities based on models of trust and reputation, by taking advantage of a framework that already provides a trust negotiation based on the normative environment called ANTE (Agreement Negotiation in Normative and Trust-enabled Environments) [START_REF] Cardoso | ANTE: Agreement Negotiation in Normative and Trust-Enabled Environments[END_REF]. The second case study is related to the Smart Grids domain, aiming to achieve a consistent network based on some properties, such as reliability. The network can be smart enough to recognize when each entity is starting to fail or even reduce its performance and react appropriately. To validate the proposed approach more serious tests must be performed after this first attempt to identify the research opportunities. Beyond the chosen domain it is necessary to create the environment propitious to the evolution of the system and characterization of the entities feedback in order to be able to use previous equations in their overall, this mode is possible to show the advantages of this approach, which are beyond human capabilities to solve such complex.

Conclusions and Future Work

This work intends to propose one contribution focusing the dynamic offer, highly customized and trustworthy, of services with better configurability and responsiveness. An innovative self-organization approach for service management in service-oriented multi-agent systems is proposed, describing a reconfigurable architecture that considers risk management, incentives and homophily visions, to achieve self-* characteristics. In such self-organized systems several research questions may arise, namely how and when to evolve, what is the degree of the similarities to take into account in the trust model and finally, how all these combined pieces can offer a better configuration that modifies or creates more trustworthy services. To validate the hypothesis, two use cases focusing distributed environments will be considered, namely B2B and Smart Grids. The preliminary achievements show that the self-organization component can enhance services' selection by increasing the degree of connections, increasing the trustworthy. Future work will try to answer some remaining open questions, such as, "how to handle the correct management of the network's features", which directly impacts the success of the service governance.

Fig. 1 .

 1 Fig. 1. Summary of the literature review.

Fig. 2 .

 2 Fig. 2. Self-organization network topology (note that each edge has a preferred binding, which corresponds to a stronger connection of each entity).

Definition 1 . 1 [

 11 Being the self-organization (SelfO) working in an OMAS with the structure, finite, society of agents Ag ;• Oenv characterises the environment accessible by the agents, where reside different societies of agents;• Nt Soc Soc ⊆ ×, is a set of connections where (Ag , Ag ) Oenv i j ∈ represents a direct relationship; • Rm represents the risk management function of a specific Nt ; • Simi represents the similarity degree function of the assigned Nt ; • In means the incentives function of the assigned Nt ; threshold ε (to be parameterized), ,..,ε,.. ] which corresponds to organize or not; • ' Soc and ' Nt represents the next finite society and their connections, after selforganization, respectively.

  l is the system interactions generated. Fig.3depicts the behaviour of decision support and composition, namely, what the agent performs after reasoning about the available services as well as the current network neighbour, to cooperate and owning the information that exists in the network configuration to compose trustworthy services.

Fig. 3 .

 3 Fig. 3. Dynamic and automatic reconfiguration process engine of each agent.

  , which allows to create stronger connections. composed by the service name Sn, the description Sd, the configuration input Isv, output Osv, preconditions Psv and effects Esv; • Ont represents a set of concepts 1

	Definition 4. Similarity is defined as a tuple	( , , Simi Ro Sv Ont fSim → , ) [0,1]	where:
	•	Ro	=	1 {Ro ,...,Ro } λ	represents a set of roles of each agent Ag;
	• Sv is a set of service descriptions defined by another structure
		( , , , Sv Sn Sd Isv Osv Psv Esv , {c ,...,c } , , ) µ and properties 0 {p ,...,p } ν . This variable
		is important to evaluate the similarities;


Da Robson

Mauricio F Silva
email: rmsilva@uesc.br

Fabrício Blos
email: blosmauf@usp.br

Diolino J Junqueira

Paulo E Santos Filho

Miyagi

Robson Marinho Da Silva

Mauricio F Blos

Fabrício Junqueira

Diolino J Santos Filho
email: diolinos@usp.br

Paulo E Miyagi
email: pemiyagi@usp.br

A Service-Oriented and Holonic Control Architecture to the Reconfiguration of Dispersed Manufacturing Systems

Keywords: reconfiguration, holonic control system, service-oriented architecture, dispersed manufacturing system

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Manufacturing systems have evolved from mass production, lean manufacturing and flexible manufacturing to the reconfiguration of dispersed manufacturing systems (RDMS) which is conceived to be "adjustable" into pursuance of the businesses processes. Reconfiguration is associated with changes (i) in addition, modification and removal of some part of a workflow; (ii) in processing time, number and availability of existing resources; and (iii) in development of mechanisms for fault treatment [START_REF] Hsieh | A self-adaptation scheme for workflow management in multi-agent systems[END_REF].

A workflow management framework must be able to deal with the changes mentioned. The holonic control system (HCS) technique [START_REF] Brennan | An agent-based approach to reconfiguration of realtime distributed control systems[END_REF], [START_REF] Chirn | A holonic component-based approach to reconfigurable manufacturing control architecture[END_REF], [START_REF] Van Brussel | Reference architecture for holonic manufacturing systems: PROSA[END_REF] allows the integration of heterogeneous environments. Furthermore, the service-oriented architecture (SOA) [START_REF] Mendes | Service-oriented agents for collaborative industrial automation and production systems[END_REF], [START_REF] Nagorny | A service-and multi-agent-oriented manufacturing automation architecture: An IEC 62264 level 2 compliant implementation[END_REF], [START_REF] Morariu | Customer order management in service-oriented holonic manufacturing[END_REF] has been used to undertake inter-enterprise collaboration.

Hence, this paper introduces a service-oriented and holonic control architecture (SOHCA) and its method to design RDMS. By exploring the potential of techniques which ensure the integration of design, the method uses the Petri net markup language (PNML) [START_REF] Billington | The Petri net markup language: concepts, technology, and tools[END_REF] and production flow schema (PFS) [START_REF] Hasegawa | On resource arc for Petri net modelling of complex resource sharing system[END_REF] techniques.

Relationship to Collective Awareness Systems

Collective awareness systems (CAS) allow its members to work together to improve self-organization and adaptation capacity. This is possible by integration of valueadded activities, information, resources and knowledge between heterogeneous businesses workflow. RDMS is also related to the effective degree of collaboration, because its subsystems need to be aware of each other to attain the global objective [START_REF] Leitão | Adacor: A holonic architecture for agile and adaptive manufacturing control[END_REF].

We exploit HCS and SOA techniques to automatically allow the RDMS and provide a middleware for integration between the factory control and shop floor control layers in these dispersed systems. SOHCA and its design method enable designers to develop collaboration and sequence diagrams, which assimilate the implementation of CAS characteristics, regarding workarounds to: (i) develop supervisory control system with interoperability and portability; (ii) propose strategies to recover the functionality of the system or maintain operations so that parts affected by the fault are disabled without affecting other parts (also called degeneration ), and (iii) allow designers to create workflow for adding or modifying the productive processes, regarding the reuse of models to avoid repetition and overlapping tasks.

State of the Art and Related Works

There are already many suggestions applying the concept of holons or agents with different scope, focus and methodology [START_REF] Hsieh | A self-adaptation scheme for workflow management in multi-agent systems[END_REF], [START_REF] Brennan | An agent-based approach to reconfiguration of realtime distributed control systems[END_REF], [START_REF] Chirn | A holonic component-based approach to reconfigurable manufacturing control architecture[END_REF], [START_REF] Van Brussel | Reference architecture for holonic manufacturing systems: PROSA[END_REF], [START_REF] Leitão | Adacor: A holonic architecture for agile and adaptive manufacturing control[END_REF]. Mendes et al. [START_REF] Mendes | Service-oriented agents for collaborative industrial automation and production systems[END_REF] explain how to combine the service-oriented agents in industrial automation, sharing resources in the form of services by sending requests between agents. Nagorny et al. [START_REF] Nagorny | A service-and multi-agent-oriented manufacturing automation architecture: An IEC 62264 level 2 compliant implementation[END_REF] proposed the use of hardware and software accessed remotely, and where operational activities are described as services. Morariu et al. [START_REF] Morariu | Customer order management in service-oriented holonic manufacturing[END_REF] present the design and implementation of the customer order management module which integrates HCS with SOA enabled shop floor devices using industry standards. Furthermore, control architecture should be based on standards ontologies, as proposed in ANSI/ISA 88/95 [START_REF] Instrumentation | Enterprise-control system integration: Part 1: models and terminology[END_REF] and ADACOR [START_REF] Leitão | Adacor: A holonic architecture for agile and adaptive manufacturing control[END_REF]. In previous studies [START_REF] Da Silva | Design of active holonic fault-tolerant control systems[END_REF], [START_REF] Silva | A procedure for modeling of holonic control systems for intelligent building (HCS-IB)[END_REF], [START_REF] Silva | Modeling of active holonic control systems for intelligent buildings[END_REF], we applied the HCS with fault tolerance mechanisms for intelligent building, while in [START_REF] Silva | Design of reconfigurable and collaborative control system for productive systems[END_REF] our methodology was applied for manufacturing system (MS) and for the description of the control mode switching between two operational modes.

We observe that: (i) a suitable ontology can provide a semantic model for interaction in RDMS; (ii) a trading mechanism is more favorable than a requestresponse communication format between holons; (iii) patterns emerge without an environment that facilitates the development of new models; (iv) there are few proposals regarding strategies for the system reconfiguration; (v) another challenge is how to compose holarchies that accomplish a productive task with constraints to consider the lead time; and (vi) there are still a small number of applications combining HCS and SOA concepts, or more specifically with RDMS.

SOHCA and Proposed Design Method

The proposed method to design RDMS is structured in stages (Fig. 1): analysis of requirements; modeling; analysis of models; implementation; operation; and integration. The integration stage ensures a closed-loop for re-design and reengineering.

In the analysis of requirements stage specifications are defined, such as: aim, control devices, orders, tasks, operations, interactions between parts of the system, and strategies for the cases of fault treatment and degeneration. Based on this survey, the holons are identified. Figure 2 illustrates the SOHCA holons. The product holon (PrH) manages the requests of products, searches for the corresponding holons, creates a work order (wo) comprising the product type and the amount. The set of PrHs composes the production plan, which represents the union, transformation and operation of the intermediated products to get the final products, i.e., the required input types and output types between PrHs. The task holon (TH) manages the recipe for business processes, such as, manufacturing, work order, operation and resource; and the reconfiguration strategies. The OpH represents the resources, such as humans or equipment (control objects). The supervisor holon (SuH) contains all the knowledge to coordinate the services of all holons.

At the modeling stage, the PFS and PN models represent the processes. To model the workflow of a holon, a place (terms of PN are highlighted) represents a state in the workflow while a transition represents an event or operation that conducts the flow from one state to another. The synchronization of PN models is made by enabler and inhibitor arcs, as well as by auxiliary places. To process an order, each holon is modeled as an atomic service that can be combined with atomic services by other holons. OpHs models include the control object which must regard fault states of these objects. The PrH, TH and SuH specify a set of operations, precedence constraints of the operations and control supervisory. PN models are used to represent the holons workflow and activities. The calculations of timing constraints are made using the temporized transitions in PN models.

In the analysis of models stage structure and dynamic behavior of the models are simulated and verified. The qualitative analysis is based on structural analysis using editors of PN models. Quantitative analysis is performed through the simulation of temporized transitions. At this stage, scenarios are identified with models built for each one. The models must meet the restrictions and achieve the objectives outlined in the hypothesis. It is also possible to review the control system models identifying the places and transitions that must be considered in each service.

The implementation and operation stages move toward the implementation and real-time monitoring system for supervision and control aspects. For low-level control applications, the code generation is made following the IEC60848 GRAFCET language. The code generation of a high-level language is made in Java using JADE and its extensions [START_REF] Silva | Design of reconfigurable and collaborative control system for productive systems[END_REF]. The real-time monitoring system is accomplished by synchronizing the operation of the PN models with sensor signals that represent the devices state. Communication channels are specified in the SOHCA ontology, a Java method is called and it creates the channels with the specified IP addresses and ports, while other holons associate a behavior to the specified port for each communication channel.

Application Example

Here, a benchmarking dispersed manufacturing system (BDMS), illustrated in Fig. 3, is used to describe the application of the proposed method. For each web service (WS) the devices, their control functions, commands and signals of actuation and detection are identified. The identification is made according to the specification DIN/ISO 1219-2:1996-11 and the codes recommended in specification IEC 61346-2:2000-12. For example, in the nomenclature 1S2: 1 =circuit number, S =device code, and 2 =device number.

The temporized transitions are used for calculating the holarchies formation. For example, let ሾܪ ሿ be the production sequence n formed by PrHs to obtain a final product. Let us consider the following sequences (the caption of Fig. 3

Conclusions and Further Work

This paper presented service-oriented holonic control architecture (SOHCA), and its method that describes the required data and techniques to design supervisory control system to the reconfiguration of dispersed manufacturing systems (RDMS). Different scenarios were elaborated for running an application example of a benchmarking dispersed manufacturing system. The scenarios met the restrictions and achieved the objectives outlined in the hypothesis. SOHCA responded in a faster and in a collaborative manner and showed to be useful to protect the system when hardware problems occur, implemented different thresholds of production and demonstrated operational advantages, such as better and more efficient use of manufacturing resources, production speed and ability to deliver products faster. The qualitative analysis was based on structural analysis using PN editors and quantitative analysis was performed through the simulation with temporized transitions. It was also possible to review the control system models identifying the places and transitions that must be considered in each service. The reconfiguration was not only applied to solve fault occurrence. It also was applied to improve the system performance by increasing the production gain or the number of final products. SOHCA is generic and developed based on SOA and HCS techniques; it can be tailored for specific manufacturing applications. A larger project is being developed which involves in addition to modeling, simulation and validation of PN models, ontology description, tools for designer, and other case studies.

Fig. 1 .

 1 Fig. 1. Schema to design the reconfiguration of dispersed manufacturing systems.

Fig. 2 .

 2 Fig. 2. SOHCA. Holarchies are formed (orchestration) to represent the control levels. The system is represented by the composition of these holarchies (choreography). Orchestration and choreography are terms related to SOA technique.

  lists the abbreviations used): ܪ[ ଵ ሿ: ሾܾܾܿሿ → ሾܾܾܿ ܽሿ → ሾܾܾܿ ܽ ݏሿ → ሾܾܾܿ ܽ ݏ ܿሿ and ሾܪ ଶ ሿ: ሾܾܿݎሿ → ሾܾܿݎ ܽሿ → ሾܾܿݎ ܽ ݏሿ → ሾܾܿݎ ܽ ݏ ܿሿ. time set by ܪݎܲ െ ሾܾܾܿ ܽ ݏ ܿሿ is ݐ ሾܾܾܿ ܽ ݏ ܿሿ. This imposes a timing constraint on PrHെሾܾܿ ܽ ݏሿ in ܪ[ ଵ ሿ sequence. Let ݐ ሾܾܾܿ ܽ ݏ ሿ be the latest time that PrHെሾ ܾܾܿ ܽ ݏሿ must complete all its operations. Let ݐ ሾݐ ሿ be the time in which transition ݊ must complete its operations. To meet the due date, the constraint of ݐ ሾܾܾܿ ܽ ݏሿ ݐ ሾܾܾܿ ܽ ݏ ܿሿ ݐ ሺݐ ଵ ݐ ଶ ⋯ ݐ ሻ must be satisfied by PrHെሾܾܾܿ ܽ ݏሿ and so on for other holons, i.e., ݐ ሾܾܾܿ ܽ ݏሿ must be less than or equal to ݐ ሾܾܾܿ ܽ ݏ ܿሿ minus the sum of the times of transitions of the PrHെሾܾܾܿ ܽ ݏ ܿሿ. A variable named cost ݊ܪ is associated to PrH to formation of holarchies. Let ܥ ே be the cost of a production sequence ܰ of a ܪ or a PrH. Let ܿ be the cost of a transition ݊ of PN model. For composing holarchies, SOHCA compares the cost of ܥ ሾுଵሿ ൌ ܥ ሾሿ ܥ ሾାሿ ܥ ሾାା௦ሿ ܥ ሾାା௦ାሿ and ܥ ሾுଶሿ ൌ ܥ ሾሿ ܥ ሾାሿ ܥ ሾାା௦ሿ ܥ ሾାା௦ାሿ to decide what the better sequence for production is at a certain time.

Figure 4

 4 has PN and PFS models for some holons and their workflow.

  Figure 5 presents the production plan, and reconfiguration examples.

  Figure 6a shows the PFS and PN models, and the programmable logic controller (PLC) I/O addresses list of some detectors and sensors of the D-WS. The mapping between the PFS and the JAVA implementation is depicted in Fig. 6b. Scenarios were simulated to validate the advantages of the reconfiguration and degeneration mechanisms. For example, by controlling the production speed through the pneumatic pressure and by disabling the swivel arm (represented by OpH, [sa D WS ]) then its function was assumed through by another OpH, [R-WS].

Fig. 3 .

 3 Fig. 3. BDMS composed of workstations (WSs): distributing (D-WS), testing (Te-WS), transporting (Tr-WS), handling (H-WS), assembling (A-WS) and robot (R-WS). The production plan is joining work pieces (wps): a cylinder body (black [bcb], red [rcb] or aluminum [acb]), a piston (black [bp] or aluminum [ap]), a spring [s] and a cover [co].

Fig. 4 .

 4 Fig. 4. Choreography of THs-[Business Processes], an orchestration of OpH and TH, and the detailing of SuH-[CO_SO] for the message exchange between the holons.

Fig. 5 .

 5 Fig. 5. PrH [production plan], fault treatment and degeneration workflow.

Fig. 6 .

 6 Fig.6. Examples of implementation stage.

Acknowledgments. The authors would like to thank the partial financial support from the agencies: CNPq, CAPES, FAPESP, and from the universities: UESC and USP.


Fábio Januário
email: f.januario@campus.fct.unl.pt

Alberto Cardoso
email: alberto@dei.uc.pt

Paulo Gil

A General Distributed Architecture for Resilient Monitoring over Heterogeneous Networks

Keywords: Resilient systems, distributed computing, embedded systems, multi-agents, wireless sensor and actuator network, heterogeneous networks

The growing developments on networked devices, with different communication structures and capabilities made possible the emergence of new architectures for monitoring systems. In the case of heterogeneous distributed environments, where knowledge, processing devices, sensors and actuators are distributed throughout the network, the design of such systems are challenging in terms of integration and, markedly, in security and state-awareness of the overall system. This work proposes a general distributed architecture, with supporting methods, for building a resilient monitoring system that can adaptively accommodate both cyber and physical anomalies. Its implementation relies on multi-agent systems within a distributed middleware.

Introduction

Modern computing networks that enable distributed computing consist of a wide range of heterogeneous devices, with various levels of resources, which are interconnected using differing networking technologies [START_REF] Tynan | Multi-agent System Architectures for Wireless Sensor Networks[END_REF]. These networks may be implemented in industrial environments for connecting large number of subsystems, of different natures, including interactions with humans and supervision platforms. Distributed heterogeneous environments present inherently some challenges and vulnerabilities. In the context of monitoring systems for fault and failure detection there is the need of efficient information processing and correct assessment of the systems' behaviour. Such endeavours require the development of dedicated methods to identify and recover from faults and failures, or in order to mitigate their impact on the overall system. Regarding the vulnerabilities of these networked systems, the cyber-intrusion is of major importance, as malignant actors can mask the system's degradation or provide fake data to higher management levels, with respect to the current system's status [START_REF] Rieger | Resilient control system execution agent (ReCoSEA)[END_REF].

In this work, a resilient system is regarded as a system that maintains state awareness and an acceptable level of operational normalcy in response to disturbances, including threats of an unexpected and malicious nature [3][4]. In the case of heterogeneous monitoring systems, this resilience provides the system with security and trust mechanism, thus fixing, mitigating or coping with the aforementioned vulnerabilities.

The implementation of resilient enforcement mechanisms can be accomplished by incorporating dedicated algorithms and heuristics on a distributed architecture based on multi-agents and "chunks" of middleware deployed remotely. One of the most prevalent alternatives in the context of distributed architecture design is the multiagent system (MAS) paradigm. A distributed agent-based architecture provides flexibility to move functions to where actions or measures are needed, thus obtaining improved responses at execution time, autonomy, services continuity and superior levels of scalability [START_REF] Alonso | Implementing a hardware-embedded reactive agents platform based on a service-oriented architecture over heterogeneous wireless sensor networks[END_REF].

Middleware architectures are becoming increasingly important as networks, services and applications become more complex. These architectures can deal with coordination, cooperation and interoperability of distributed components by bridging the gap between applications and their underlying low level software and hardware infrastructures. Moreover, they provide tools and methods that can help hiding the complexity and heterogeneity of hardware and network platforms. Also, middleware supports programmer's applications in several ways, such as providing appropriate system abstractions and reusability of code services, while helping in the network infrastructure management [START_REF] Soldatos | Agent based middleware infrastructure for autonomous context-aware ubiquitous computing services[END_REF].

The present work proposes a general architecture for resilient monitoring over heterogeneous networks making use of multi-agents embedded on a distributed middleware framework, where each agent is tailored for executing specific and coordinated tasks, namely for detecting and recovering from physical and cyber malfunctions.

Relationship to Collective Awareness Systems

According to the European Commission, collective awareness systems are information and communication technologies (ICT) systems that leverage the emerging "network effect", by combining open online social media, distributed knowledge creation, and data from real environments, in order to create awareness of problems and possible solutions requesting collective efforts, while enabling new forms of innovation [START_REF]Digital Agenda for Europe: Collective Awareness Platforms for Sustainability and Social Innovation[END_REF].

These systems can be adopted in various scenarios, like in social environments, enterprises collaboration, industrial processes and decision methods. In these contexts, a network infrastructure allowing communication and perception is considered necessary to assess the context aware, by making use of a collaborative approach.

The electric power production and distribution is a critical system where monitoring is of major important to prevent or accommodate systems' malfunctions. This is one striking example where resilient systems can contribute to the integrity of the overall system, by preventing cyber and physical attacks to infrastructures. Another field with high security requirements, is that of governmental communications, where intrusion threats in their communication networks are taken very seriously. Finally with the automation of many industrial processes it is necessary to develop monitoring systems in order to ensure the correctness of operation.

To provide security and dependability mechanisms to this kind of distributed systems, new set of heuristics and algorithms have to be developed, which opens a window of opportunity for new contributions to the field. In this context, the present work intends to give a contribution to the development of collective awareness systems, by proposing a general distributed architecture for enhancing the overall resilience of monitoring systems over heterogeneous networks.

Resilient Systems

The research area of resilient systems is a relatively new topic that involves systems design taking into account issues, such as cyber security, physical security, process efficiency and stability, process compliancy and state awareness.

In [START_REF] Zhu | A dynamic game-theoretic approach to resilient control system design for cascading failures[END_REF] a hybrid system model is used to address physical layer control design and cyber level security policy making for cyber-physical systems that are subject to cascading effects from cyber attacks and physical disturbances. In [START_REF] Zhu | Robust and resilient control design for cyber-physical systems with an application to power systems[END_REF], the authors propose a hybrid theoretical framework to analyse and design, in a quantitative and holistic way, robust and resilient control systems that can be subject to different types of disturbances, at different layers of the system. The work in [START_REF] Ji | Resilient control for wireless networked control systems[END_REF] presents an intelligent resilient control algorithm for a wireless network control system based on the quantification of the concept of resiliency in terms of the quality of control. The authors developed an intelligent resilient control algorithm that maintains operational normalcy in face of wireless interference incidents, such as radio frequency jamming and signal blocking, while [START_REF] Garcia | A resilient condition assessment monitoring system[END_REF] proposes a resilient condition assessment monitoring system, able to dynamically adapt and reconfigure, depending on the assessed conditions.

In literature one can find several works making use of MAS to implement intelligence mechanisms. In [START_REF] Rieger | Resilient control systems: A multi-agent dynamic systems perspective[END_REF], it is presented an overview on how to achieve critical infrastructure resilience through advanced control engineering. The authors developed a hierarchical multi-agent dynamic system framework to model distributed control system dynamics and enforce resilience. In [START_REF] Rieger | Resilient control system execution agent (ReCoSEA)[END_REF], a generalized design methodology was suggested for analysing and accommodating anomalies in cyber physical systems. By using computational intelligence techniques it is provided a design method to integrate cyber and physical data and, thus ensuring the appropriate response to both benign and malicious actions. In [START_REF] Januário | Resilient Supervision System over WSAN: A Distributed Multi-Agent Architecture[END_REF], a distributed multi-agent architecture is presented to implement a resilient supervision system over wireless sensor and actuator network. This system implements resilience enforced mechanisms, by incorporating dedicated algorithms and heuristics in an architecture based on agents, so as to guarantee the state awareness and an acceptable level of operational normalcy, in response to disturbances.

Taking into account previous constrains on resilient systems, this work intends to extend resilience policies to heterogeneous and distributed environments. With the modern computing networks infrastructures and distributed resources these systems become an important research area to provide security and dependability. Aiming at this purpose, the present work proposes a general resilient architecture based on the MAS paradigm.

Architecture Overview

The proposed architecture is based on a MAS embedded on a distributed middleware platform, for which issues, such as those of communication, network topologies, routing protocols and integration are assumed to be dealt with elsewhere. Fig. 1 presents a distributed environment comprising three main components, namely applications, middlewares and processes/devices or nodes. The processes/devices or nodes consist of physical components and the necessary hardware and software infrastructures. These components can be represented by processing units that process data stemming from the network, or from databases that store and provide information and knowledge regarding the network, gateways interfacing with another network that uses different protocols, and sensor and actuator networks (wireless or not) to interact with the environment, by sensing and actuating on physical infrastructures. Fig. 1 shows a wireless sensor and actuator network (WSAN), where the sink node and the dispatcher are responsible for transferring the data from the WSAN into the middleware and for coordinating the underlying communications.

The application layer provides users with a number of applications allowing, in a transparent way, the interaction with networks, devices and plants in the perimeter of the networks. The monitoring application enables to follow-up the system status, while the control application manages, commands or manipulates the behaviour of the system, using sensors and actuators. The configuration application enables to setting up the system's parameters and available devices. Finally, the MAS manager configures and changes the multi-agent system's attributes.

The middleware layer allows the interaction between the application layer and the plant/devices. The middleware is in this architecture deployed, in a decentralized fashion, on several components in order to bring about the functionalities to the location where they are called out. In heterogeneous networks this topology has the advantage of each "chunk" of middleware could be implemented taking into account the associated hardware or software constrains shown up in the process/device layer.

Fig. 2 presents the main components of the proposed middleware. The integration layer facilitates data exchange between the process/device infrastructures and applications. Data from sensor and distributed devices, as well as additional diagnosis information is fed into the middleware, analysed and forwarded to the corresponding destination. Further, actuation commands and data is passed down from the middleware to actuators or devices, while the monitoring layer evaluates the performance of the middleware at runtime by applying data quality metrics. This element ensures that processing delays are within required bounds. The configuration layer enables the definition of commands to configure the data uploaded and downloaded from the devices, as well as agents in the security and dependability modules. Dependability. For enabling the monitoring system to meet dependability targets it is necessary to incorporate fault tolerant and self-healing mechanisms into the resilient design. These mechanisms will ensure end-to-end performance communication in environments where growth, scalability, closed loop stability and performance, are important features.

Middleware

Security. When sensitive data received from sensors or servers and data sent to actuators or other devices are transmitted through uncontrolled environments, security is inescapably compromised. If not protected, transmitted data may be accessed, corrupted, or even destroyed, by unauthorized users. Consequently, security mechanisms should be developed and implemented in order to be able to adapt to changes in the application environment, to system requirements and system resources.

Multi-Agent System

Agents can be regarded as computing entities in a given environment, presenting a certain degree of autonomy, and possessing the ability to feel and act in order to accomplish a given mission. Some of their inherent features include the following [START_REF] Paolucci | Agent-Based Manufacturing and Control Systems: New Agile Manufacturing Solutions for Achieving Peak Performance[END_REF]:

Autonomy: agents are independent entities, able to accomplish a given task, without any programming or direct intervention; Reactivity: capability of perceiving their environment and respond quickly and effectively to changes; Pro-activity: ability to take initiative goals and behave in order to meet them; Cooperation: agents have the ability to interact and communicate with one another for the sake of their own teleonomy; Intelligence: in order to evaluate and take over a task, in an autonomous way, an agent should incorporate intelligent techniques; Mobility: agents have the ability to move its code from a node to another one in a system, offering mobility properties in distributed computational devices.

Multi-agent systems offer the means to design and implement complex distributed systems. This paradigm extends previous approaches and methodologies, namely object-oriented or distributed computing. The proposed architecture takes into account the use of MAS (Error! Reference source not found.), where a master agent is responsible for the management routines related to other dependent local agents, and for coordinating some tasks, such as configuration requests.

Each module (dependability or security) has a master agent that is responsible for all dependent agents, as well as the communication with other agents. Local agents are devoted to monitoring the state of the system with specific methods and algorithms. In the case of heterogeneous distributed systems, subsystems do not possess the same characteristics and vulnerabilities, so these agents have to be configured to provide the necessary functionalities to each subsystem. In the case of a detected event, the agent has the ability to act accordingly, while guaranteeing the maintenance of the system until the problem is completely solved. In this architecture, mobile agents have an important role, as their use allows reducing the network traffic. One of the applications for mobile agents deals with communications between middlewares, namely for configurations of distributed agents, with commands sent by the user. With these features MAS allows the implementation of different techniques and show to be a good way so as to provide resilience to heterogeneous networks.

Conclusion and Future Work

This paper addressed the problem of resilient monitoring over heterogeneous networks. A general architecture based on multi-agent systems embedded on a distributed middleware framework. The resilience is achieved by combining two main features/resources: i) dependability with the implementation of a fault detection and identification system, aiming at maintaining the system in safe operation state; ii) security envelope by developing measures to protect the system from cyber and physical attacks.

Finally, it should be mentioned that the present work is still under progress. Further developments will include the exhaustive research in security and dependability areas in order to provide metrics and methods to improve the framework responsiveness.

Fig. 1 .

 1 Fig. 1. General distributed architecture.

Fig. 2 .

 2 Fig. 2. Resilient middleware based in MAS.

Fig. 3 .

 3 Fig. 3. Multi-agent system architecture.

Acknowledgments. Januário, F. acknowledge Fundacão para a Ciência e Tecnologia (FCT), Portugal for the Ph.D. Grant SFRH/BD/85586/2012. This work has been partially supported by iCIS-Intelligent Computing in the Internet of Services, Project CENTRO-07-ST24-FEDER-002003.