MSD Mass Spring Damper PID Proportional Integral Derivative

Keywords: Wireless Sensors Network's lifetime, energy saving mechanisms, energy harvesting, clustering protocols, game theory routing protocol, Nash equilibrium, autonomous piezoelectric energy harvesting management and control Wireless sensors network

During recent years, wireless technologies have been increasingly used in different fields. Wireless sensor network consists of three main components: a large number of nodes or connected objects, gateways and softwares. Wireless Sensor Network (WSN) workload depends on the WSN lifetime. Wireless sensor nodes are deployed in large number to detect information from their environment, communicate autonomously by wireless links to let the information messages reach the Base Station. Nevertheless, sensors suffer generally from limited resources as limited memory, limited computations and especially in terms of energy. Therefore, there is a need to develop energy-efficient mechanisms solutions to extend the network lifetime.

In this thesis, we propose new strategies to increase the WSN lifetime. These strategies are based on energy management and energy harvesting in the wireless sensors' batteries.

After an introduction of the wireless sensor network architectures and applications, we analyse the advantage of using WSN in corresponding areas. Then, some main research results of energy saving and node deployment are provided. Afterwards, we propose two new approaches: The first one is a hierarchical clustering protocol based on election and location. The second approach is based on a game theory routing protocol. The second part of work concerns harvesting environmental energy control to recharge wireless sensors' batteries. Our results show that the wireless sensors lifetime can be extended using the proposed strategies. All obtained results are supported by numerical experiments and extensive simulations. Finally, we conclude this thesis by providing insights for research directions about energy conservation and harvesting in WSN.

P a g e 6 | 126

Résumé

Les réseaux sans fil et les réseaux des objets connectés sont de plus en plus utilisées dans différents domaines. Un réseau de capteurs sans fil est constitué de trois composants principaux : un grand nombre de noeuds, des passerelles et des logiciels.

La charge de travail d'un réseau dépend de la durée de vie de ses noeuds. Ces derniers sont déployés en grand nombre pour détecter les informations de leur environnement et communiquer de manière autonome via des liaisons sans fil pour permettre aux messages d'informations d'atteindre une station de base. Cependant, les capteurs souffrent généralement de ressources réduites telles qu'une mémoire limitée, des capacités de calculs simplifiées et en particulier une ressource finie en termes d'énergie.

Par conséquent, il est nécessaire de développer des approches pour prolonger la durée de vie du réseau.

Dans cette thèse, nous proposons de nouvelles stratégies originales et performantes pour prolonger la durée de vie des réseaux des capteurs sans fil ou des réseaux des objets connectés. Ces stratégies reposent sur la gestion ou la récupération d'énergie depuis l'environnement ou la récupération d'énergie dans les batteries des capteurs sans fil.

Les contributions de la thèse sont les suivantes : 1) Une approche distribuée basée sur l'élection et la localisation des noeuds pour augmenter la durée de vie du réseau de stockage est proposée ;

2) Une approche adaptative basée sur la théorie des jeux de décision permettant aux capteurs de prendre part ou non aux communications en vue des décisions en vue de prolonger la durée de vie du RCSF est proposée ;

3) Une approche autonome de contrôle permettant de compenser l'énergie consommée à l'aide d'un système piezo autonome de récupération d'énergie est proposée.

Après une introduction aux architectures de réseaux des capteurs sans fil, nous présentons un état d'art sur les applications en analysant les avantages apportés par RCSFs dans ces domaines. Ensuite, des résultats de recherche principaux sur la conservation de l'énergie et le déploiement des noeuds sont fournis. Ensuite, nous

P a g e 7 | 126

proposons deux nouvelles approches : la première est un protocole de classification hiérarchique basé sur l'élection et la localisation des capteurs sans fil. La seconde approche concerne un protocole de routage basé sur la théorie des jeux dans l'algorithme de classification hiérarchique proposé. Nos résultats montrent que la durée de vie des capteurs sans fil peut être prolongée à l'aide des stratégies proposées. Tous les résultats obtenus sont étayés par des expériences numériques et des simulations approfondies. Dans la deuxième partie de ce travail, nous nous sommes intéressés à la récupération de l'énergie environnementale pour recharger les piles rechargeables des capteurs sans fil. Nous proposons une approche permettant de récupérer l'énergie produite par vibration et contrôler les batteries rechargeables des capteurs sans fil. Les résultats montrent que l'énergie consommée durant les activités des capteurs peut être compensée et la durée de vie des capteurs sans fil est étendue. Enfin, nous concluons cette thèse en présentant les futures recherches sur la conservation et la récupération de l'énergie dans RCSF.

P a g e 12 | 126

List of figures

Introduction

This chapter constitutes a general introduction of the thesis report by presenting an overview of the problem statement, the contributions discussed and thesis outline in addition to organization of the manuscript.

Research domain

General context : Wireless Sensor Networks lifetime Specific context : Adaptive energy harvesting approaches to extend the Wireless Sensor Network lifetime.

Problem statement

Recent progresses in wireless communications have allowed the development of wireless sensor networks (WSN) which are one of the most promising technologies. Wireless sensor network consists of a set of small, low-cost sensors powered with a battery devices whose sensing capabilities enable them to detect information from their environment. Sensor nodes can be deployed in a large number in a physical area to cooperate with each other and to form a wireless sensor network through wireless links. This attracts tremendous attention for various domain applications where remote monitoring abilities are required. Sensor nodes are expected to operate autonomously in a region of interest for long period of time. The WSN lifetime depends on limited power in sensors' batteries. However, replacing exhausted batteries may be very high costing in terms of time and budget and may be even impossible in a harsh or hostile environment. Severe energy, computation, storage constraints and the size of sensor nodes present new challenges in the development and applications of WSN. Especially, the design of power efficient in wireless sensor networks is, however, a very challenging issue. The communication

P a g e 17 | 126

among sensor nodes in WSN is one of the most dominant factor that consumes the energy of the source nodes. Therefore, the development of energy-aware solutions for wireless sensors communications is necessary to increase the lifetime of wireless sensor networks.

Contributions and thesis outline

In this thesis, we propose new solutions to improve the wireless sensor network lifetime and performance. The main contributions of this thesis are as:

1) Distributed energy aware protocols to increase WSN lifetime;

2) Game theory adaptive decisions approaches applied in Wireless Sensors to making decisions to extend the WSN lifetime;

3) Rewarding energy consumed by an autonomous energy harvesting system with Proportional Integral Derivative control.

The first part of this thesis consists of a literature review of WSN architectures and a study of the different WSN applications. Then, we present the different energy sources and a comprehensive review of existing energy harvesting mechanisms designed for wireless sensor networks. After that, two contributions, which can be applied in the communication between wireless sensors to extend the network lifetime, are proposed. The first one exploits the fact that one of the solutions to save energy in WSN is to divide the networks into clusters depending on low energy and location of sensor nodes.

In this context, we propose a new energy-efficient hierarchical clustering scheme that update the election of cluster heads and the formation of clusters in setup phase to extend the network lifetime. Compared to the famous clustering protocol LEACH (Low Energy Adaptive Clustering Hierarchy), our proposed distributed clustering protocol P a g e 18 | 126 achieves better lifetime while guaranteeing more information messages reach the Base Station (BS). In our second contribution, we propose a new solution for sending messages between sensors in our proposed clustering scheme. Once deployed, after the formation of clusters in setup phase, the messages are compressed in the steady phase. Then, we propose a non-cooperative game theory based algorithm to control the energy consumed by the sensors in the network. This algorithm is called the Profitable Energy Market Game (PEMG) wherein each player has to decide if he wants to participate or to stay out of the market at each round. The market defines trading rules according to a strategy. In this work, the strategy has two actions: to enter the game or to stay out the game. Each player (i.e. sensor) calculates a payoff that can affect or be affected by the payoffs of other players (i.e. its neighbours). The payoff is a function of the sensor's residual energy. More precisely, the payoffs depend on the players' strategies that stay in the sleeping mode to charge their batteries or enter the game to transmit the sensing data messages. for each cluster, each sensor and its neighbours play this game theory to send messages. Our solution effectively reduces the number of dead sensors, prolong the network lifetime and achieves better performance in term of information arriving to the BS. The second part of this thesis is dedicated to recharge sensors' batteries by harvesting energy to increase the network lifetime. We introduce a controller to tune the energy harvesting and a controller to manage the energy in the sensor according to its activities needs. This new self-organization power system insures the prolongation of the WSN lifetime by rewarding the energy consumed and balancing the rest of energy in the sensors' batteries.

Organization of the manuscript

The rest of this thesis is organized as follows. In Chapter 2, we present the different architectures of WSN and we describe their applications. Then, in Chapter 3, we give details of energy consumption in WSN and we give a review of energy harvesting sources. In addition, we provide a comprehensive review on existing

P a g e 19 | 126

energy saving mechanisms for wireless sensor networks. In Chapter 4, we present our hierarchical clustering approach based on low energy and location and we compare its performance against existing approach. Afterwards, in Chapter 5, we present our approach based on Game theory in our clustering hierarchy. In order to assess the efficiency of our proposed approach, we evaluate it by comparing our results with first proposed approach and other existing approach in terms of energy consumption, Network lifetime and packets information reached the sink. In Chapter 6, we propose a self-organized manager of energy to reward the energy consumed during the sensors' activities in the rechargeable batteries by harvesting energy from the sensors' environment. The results approve that the proposed self-organized manager of energy recompense the energy dissipated during the sensor's activities and give it more power to stay in life and achieve next activities. Finally, in Chapter 7, we conclude this thesis by providing a summary of our contributions, and discussing prospects for the future work.

P a g e 20 | 126

: Overview of thesis organization Introduction

A sensor node consists of four essential components : the sensing unit, the processing unit, the communication unit, and the power supply unit. The sensing unit includes one or several sensors equipped with analog-to-digital converters (ADCs) for data information collection. The ADCs convert the analog signals based on data information collection into digital units, which are then transmitted to the processing unit. The processing unit usually includes a microprocessor or a microcontroller (MCU) and a storage memory, which cooperate to process the sensed data from local environment. The communication unit consists of a radio model to perform data information transmission and reception over channels. The power supply unit includes one or more batteries to drive all components of the sensor node system with power. In addition, a sensor node can be equipped with other optional units depending on specific application requirements.

All the sensor node's units need to be fabricated in small modules with low energy consumption and low assembly cost. Figure 2.1 describes the structure of a typical wireless sensor node and interconnections between its components.

P a g e 23 | 126

WSNs have interesting advantages that can not only reduce the cost and delay in development, but can also be applied to any environment, especially those that are not easily accessible by human. In this chapter, we introduce the different existing wireless sensor networks architectures and applications.

The rest of this chapter is organised as follows. In the next section, we provide the structure of typical sensor node and the fundamental concepts on wireless sensor networks architectures. Then, in Section 2.3, we present the main categories of applications we have identified and their respective requirements. Finally, Section 2.4 we summarize this chapter.

WSN architectures

Wireless sensor network widely consists of a large number of low cost sensor nodes deployed in an area of interest, and one or more sinks are located close to or inside the sensing area. WSNs can be classified into three types: homogenous, heterogeneous and hybrid sensors. For homogenous network, the sensor nodes have the same computational power and storage capacity. The heterogeneous network consists of sinks, normal sensor nodes and sophisticated sensor nodes with advanced abilities in terms of embedded processing, communicating capabilities as compared to normal sensor nodes.

To transmit data information to the base station or sink, each sensor node can use single hop long distance transmission. Nevertheless, long distance transmission is very costly in terms of energy consumption. Furthermore, the energy consumed to transmit data messages dominates the total available energy and grows exponentially with the increase of distance and reduces the network lifetime. In order to save energy and extend the operational network lifetime, the amount of transmission distance and traffic needs to be reduced. For this reason, researchers propose the multihop communication. In multihop communication, sensors transmit sensed data information toward the sink via one or more intermediate sensor nodes, which can reduce the energy consumed during communication activity. Meanwhile,

P a g e 24 | 126

the sink can also work as a gateway to send relevant data information via internet to the users of this data information.

The architecture of a multihop short distance communication for wireless network can be organized into two categories : flat and hierarchical architectures.

Flat architecture

In a flat network, all the sensors are peers and have the same role in performing a sensing task. It is very difficult to assign a global identifier for each node due to the large number of sensor nodes in the network. Therefore, data gathering is usually accomplish using data-centric routing protocol. The sink plays the role of the coordinator that manage the transmission of data information. The sink transmits a message to all the sensors and only sensor nodes that have the data matching the message received will respond to the sink. The communication between each sensor node and the sink is based on multihop path and each sensor used its peer nodes as relays. An approach to use cameras exploiting wireless sensor networks as surveillance systems to collect visual data from cameras is proposed. It is a controlled sink mobility algorithm that uses the RL of sensor nodes to calculate the position of a single sink in a WSN.

P a g e 27 | 126

Due to the huge increasing of number of habitants in cities, security is one of the most significant need for smart cities from the citizens' point of view. The entire smart city has to be continuously monitored and observed to discover criminal acts, thefts and so on. Therefore, smart surveillance systems based on wireless sensor networks that have the ability not just to detect information but also have the ability of intelligent processing and alarm in case of any event of interest occurs.

With the rising of vehicular traffic around the world in urban areas, new traffic management solutions become a need to manage the traffic flows. It is visibly in our daily life through traffic snarl and increasing number of accidents. Therefore, Wireless sensor networks based intelligent transportation systems are deployed. This technology enables a new broad range of smart city applications around urban sensing including traffic safety, traffic jam control, road state monitoring, vehicular warning services, speed control and parking management.

Environmental monitoring

The development of environmental monitoring system has rapidly grown in agricultural monitoring, indoor monitoring, greenhouse monitoring, climate monitoring and forest monitoring. Environmental monitoring applications are the first ones that have been advanced for wireless sensor networks.

For security indoor environment, wireless sensors for safety in living monitoring have become one of the main preferences for people. Implemented wireless sensor network for security system using Bluetooth technology consists of relay control nodes and a control system that can be placed in an area in a building. The sensor and relay nodes can detect the events that happened and report collected information to the control nodes. Then, control nodes send detected information to alert the local security control system. In addition, wireless home security system is also designed to detect any intruder in houses and buildings. It uses motion sensor as the sensor node. When the motion sensor detects an intruder, it sends a report to the end node.

The end node is linked with an interface between user and system. A new architecture

P a g e 28 | 126

of WSN with UC Berkeley motes sensors nodes was proposed for real world habitat monitoring.

For greenhouse monitoring, wireless sensors network is used to measure and monitor environmental parameters including temperature, humidity, light. The system collects, sends and controls the parameters information automatically and it is proven that the performance of the system is efficient as the user can collect high precision data of the environment without any disturbance. The implementation of greenhouse environment monitoring based on ZigBee wireless sensor network in [START_REF] Shi | Game Theory for Wireless Sensor Networks: A Survey[END_REF] use analysis and experimental test methods to ensure the system efficiency. It collects the humidity, temperature and carbon dioxide concentration, which are the parameters of greenhouse environmental parameters, and demonstrate the nodes and network coordinator communications perform network stabilization, and compliance between theoretical data and real situations.

In forest, Wildfire monitoring with WSN are used to predict future forest incidents, to tracking a fire in temporal evolution. WSN measure the amount of CO and H2 in air, relative humidity, pressure, light intensity, and other environmental parameters. A combination of developed ZigBee-WSN-based multi-sensor system (smoke sensor, CO sensor, temperature sensor, relative humidity sensor) and an Artificial Neural Network have been developed to identify the different fire conditions in real time.

Healthcare applications

WSNs can be used to track patients' healthcare purposes. Indeed, in healthcare applications, sensors are used to monitor patients' body temperature, brain activity, muscle motions, to capture data information during daily life or extended stays in hospital or to track the health of patients at home by using embedded technology. In addition, WSN allows access to patient information everywhere and on real-time.

A hospital care base on wireless sensor networks have the access to detect the patient's data at any point in time without the need for doctors or nurses to be present physically in the patient's room to examine the readings.. The network nodes can be P a g e 29 | 126 interfaced to different vital sign sensors like electrocardiograms, blood pressure, and heart rate and so on. The sensor node can initiate a reading, collecting data and forwarded the data to the base station. They are connected to a graphical user interface to store and display data on the base station server. A technological platform PlaIMoS that includes WSN to transmit data about blood oxygen, respiration rate, and galvanic resistance via a serial link was created to monitor cardiovascular and respiratory variables.

Home healthcare can replace the social and financial burdens especially for an aging population.

WSNs can be installed in a patient's home environment to achieve the home healthcare by acquiring physiological and behavioral data information from patients for diagnosis, monitoring, or chronic disease management in real time.

Military applications

WSN is introduced in defense applications and it can play a big role for different military purposes such as detection and tracking of enemy movements, force protection, and urban warfare.

WSNs can be useful for defense purposes. Those types of applications are very important in military perspectives. The deployment of sensors in low-flying airplanes or drones that can collect information from the surrounding area of war zone when the airplanes or drones land and approach from the earth. After detecting data information, wireless sensors send data to a powerful base station located inside the plane. Data are gathered and analysed by the BS and determine strategic information such as type and number of tanks in the battlefield, number of militaries, elevation of ground, and types of hiding spaces such as bunkers. These information messages are very important to be ready to defend against a threat or a danger.

In addition, Wireless sensors are not expensive. Therefore, the damage of some nodes by hostile actions in the battlefields may not affect a military operation. The structures of robustness, selforganizing and fault tolerance make sensor networks P a g e 30 | 126 suitable for military use. The butterflied surveillance system is developed to monitor the enemy arms as missiles and aircrafts in which the mobile WSNs are linked to the cloud computing. This mobile WSN performs the parameter estimation for effectively pointing out the enemy movements. In addition, Sensors can be also used to detect biological and chemical attacks. Trace detection sensors can collect information and alert soldiers about bombs, explosive materials and so on. For this reason, this technology is very important to previse attacks and save the life of people and save buildings from damage in the urban warfare.

Industrial control

WSNs have a huge application in industrial fields. It can be deployed in industry to monitor and control manufacturing processes, and equipment.

Industrial automation systems have become more and more common in industries and factories and they play an essential role to provide the dynamic link between control systems and the physical world. Due to the employment of a huge variety of wireless industrial sensor networks with their geographical distribution over factory or industry, the capability to communicate and control data information have become more sophisticated and easy ranging from low-level to high-level control. For example to ensure the efficient operation for pipes and systems, heat tracing wireless sensor network is used to keep pipes within a particular temperature range. WSNs can also be deployed to monitor the execution of process periods in industrial deployments by providing the operator with means to adapt process parameters on demand.

In addition, WSNs can be applied in the Machine surveillance and maintenance field.

Wireless sensors can be deployed for condition-based maintenance of machines exploiting the ability of local data processing and providing monitoring data in real time in order to enhance the utilization and lifetime of the monitored equipment.

P a g e 31 | 126

Conclusion

In this chapter, we have introduced the Wireless Sensor Networks, where we have presented the different architectures. In addition, we have presented the applications of WSNs in various domains, which is why it is important at this time to implement WSNs. In next chapter, we introduce the energy consumed during communication between sensors in wireless networks and we present different methods to harvest energy to recompose the energy consumed in WSNs. -------------------------------------------------------------------------------34 Networks -------------------------34 ----------------------------------------------------------36 ----------------------------- ---------------------------------- ------------------------------------------------------------------- -----------------------------------41 3.4. Energy harvesting saving methods- ---------------------------------------------- ----------------------------------------------------------------- ---------------------------------------------------- ---------------------------------------------------------- ------------------------------------------------------- -----------------------------46 3.4.2. Data reduction- -------------------------------------------------------------------- ------------------------------------------------------46 P a g e 33 | 126 ------------------------------------------------------- --------------------------------------------------------- ----------------------------------------------48 --------------------------------------------48 3.6. Conclusion --------------------------------------------------------------------------------- summarizes this chapter with a brief discussion of future works.

P a g e 32 | 126

Energy consumption in Wireless Sensor

Energy harvesting sources-

Light power--------------------------------------------------------------------------37

Kinetic power ---------------------------------------------------------------------37

Electromagnetic energy harvesting------------------------------38

Electrostatic energy harvesting-------------------------------------38

Magnetostrictive energy harvesting-

Data aggregation-

Recharging sensors' battery in WSN-

Energy consumption in Wireless Sensor Networks

WSNs have primary goal of transmitting data information from sensor nodes to the sink node effectively. The energy consumed in WSNs is due to different activities namely sensing data, data processing, data communication, and microcontroller (MCU) switching.

Sensing energy consumption

The sensing energy cost depends on the type of sensors. For example, the temperature sensors consumed less important energy than gas sensors. The sensor

P a g e 35 | 126

node can contain diverse sensors, and each one has its individual energy consumption attributes. Generally, the sensing energy consumption for a 𝑆𝑖can be expressed as follows:

𝐸 𝑠 = 𝐿(𝑆 𝑖 ) × 𝑉 𝑑𝑐 × 𝐼 𝑠𝑒𝑛𝑠 × 𝑇 𝑠𝑒𝑛𝑠 (1)
where 𝐼 𝑠𝑒𝑛𝑠 is the needed amount of current, and 𝑇𝑠𝑒𝑛𝑠 is the duration to detect and collect 𝐿(𝑆𝑖) bits data information.

Processing energy consumption

The sensor consumes energy to read the data message and to write it in its memory. The processing energy consumption could be calculated by:

𝐸 𝑝 = 𝐿(𝑆 𝑖 ) × 𝑉 𝑑𝑐 8 × (𝐼 𝑊𝑟𝑖𝑡𝑒 × 𝑇 𝑊𝑟𝑖𝑡𝑒 + 𝐼 𝑅𝑒𝑎𝑑 × 𝑇 𝑅𝑒𝑎𝑑 ) (2)
where 𝐼𝑊𝑟𝑖𝑡𝑒 and 𝐼𝑅𝑒𝑎𝑑are the necessary amount current to write and read one byte data.

𝑇𝑊𝑟𝑖𝑡𝑒 and 𝑇𝑅𝑒𝑎𝑑 are the necessary duration to treat the 𝐿(𝑆𝑖)data information.

Communicating energy consumption

The energy consumed to transmit and receive (𝑆𝑖)is computed following the firstorder wireless communication model for the radio hardware illustrated in figure 3.2. Transmitter expends energy to run the radio electronics and the power amplifier. The necessary energy required to transmit (𝑆𝑖) bits data message is:

𝐸 𝑇 𝑖 = { 𝐿(𝑆 𝑖 ) × 𝐸 𝑒𝑙𝑒𝑐 + 𝐿(𝑆 𝑖 ) × 𝐸 𝑓𝑠 × 𝑑 2 𝑤ℎ𝑒𝑛 𝑑 < 𝑑 0 𝐿(𝑆 𝑖 ) × 𝐸 𝑒𝑙𝑒𝑐 + 𝐿(𝑆 𝑖 ) × 𝐸 𝑚𝑝 × 𝑑 4 𝑤ℎ𝑒𝑛 𝑑 > 𝑑 0 [START_REF] Xuxun | A Survey on Clustering Routing Protocols in Wireless Sensor Networks[END_REF] where 𝐸𝑒𝑙𝑒𝑐 represents the energy consumed to transmit or receive 1 bit message, the constants 𝐸𝑓𝑠 and 𝐸𝑚𝑝 depend on the transmitter amplifier model. 𝐸𝑓𝑠 is for the free space model, 𝐸𝑚𝑝is for multipath model, 𝑑 is the transmitter receiver distance and 𝑑0 is a threshold distance calculated as follows :

𝑑 0 = √ 𝐸 𝑓𝑠 𝐸 𝑚𝑝 ⁄ ( 4
)
and the energy consumed by the radio to receive 𝐿(𝑆𝑖) bits data information is defined by:

𝐸 𝑅 𝑖 = 𝐿(𝑆 𝑖 ) × 𝐸 𝑒𝑙𝑒𝑐 (5)

Switching the microcontroller (MCU) mode energy consumption

The sensor wastes energy by switching between the MCU modes. In this paper, we just take in consideration the active mode and the sleeping mode. The energy cost for the computational MCU mode can be expressed as :

𝐸 𝑆𝑤𝑖𝑡𝑐ℎ-𝑅𝑎𝑑𝑖𝑜 = 𝑉 𝑑𝑐 2 × (𝐼 𝑠𝑡 𝑗 -𝐼 𝑠𝑡 𝑖 ) × 𝑇 𝑠𝑡 𝑖,𝑗 (6)

Energy harvesting sources

With the extensive improvements in the domain of wireless sensor networks, some applications necessitate a long lifetime for the sensor nodes. An alternative type of power source to rechargeable batteries can be considered. This process helps in providing unlimited energy for the lifespan of the electronic device. Therefore, the

P a g e 37 | 126

process of extracting energy from the ambient environment and converting it into consumable electrical energy is identified as energy harvesting or power scavenging.

Due to the fact that the WSN lifetime varies proportionally with the residual energy in sensors' battery, researchers have hardly worked in the generation of electrical power by scavenging ambient energy from the environment. There are different types of energy harvesting and researchers have proposed generators of Energy Harvesting technologies. The electrical energy required to run different sensors' devices can be obtained by tapping the environmental light, kinetic, thermal, electromagnetic and human body energy harvesting. Small devices like wireless sensors can capture this energy and stock it in their rechargeable batteries to ensure the continuity of sensors' activities.

Light power

Radiant energy is harvested from the sun and from radio frequency waves, for both indoor and outdoor systems. In recent years, energy harvesting from radio frequency and sunlight has been sightseen widely in an attempt to utilize this source of energy to power wireless sensor networks for environmental monitoring applications. Light energy can be converted into electrical energy using photovoltaic cells (PV). The type of energy exploited is usually light energy obtained from sunlight or artificial lights. With respect to solar energy harvesters, Maximum Power Point Tracking (MPPT) is designed to maximize the amount of power harvested from solar cells.

Kinetic Power

Ambient mechanical power can be obtained from different sources such as wind, continual vibrations from industrial machines, and ocean waves. This energy can be converted into electrical energy and can be considered as sufficient solutions for low duty-cycled communications. Mechanical energy can be converted to electrical energy using various mechanisms: Electromagnetic energy harvesting,

P a g e 38 | 126

electrostatic energy harvesting, magnetostrictive energy harvesting and piezoelectric transduction.

Electromagnetic energy harvesting

EMEH includes collecting radio frequency (RF) signals emitted from base stations, network routers, smartphones, and any other sources by using large aperture power receiving antennae, and converting the attained waves into utilizable dc power. Their performance strongly depends on the RF-to-dc conversion efficiency and the amount of power received by the antennae. Although this method is a reliable solution unaffected by the environmental variables, providing relatively low power densities, necessitating close deployment to the network transmitters, and requiring additive components, such as filters and voltage multipliers can be counted as its main shortcomings. Moreover, in case the nodes are sparsely deployed, available energy to be harvested may be too low, which might limit the use of EM EH. Due to the abundance of EM propagation in urban areas, RF EH is mostly preferred to operate IoT-assisted Smart City services.

Electrostatic energy harvesting

This type of harvesting is based on the changing capacitance of vibrationdependent varactors i.e. variable capacitor. Vibrations separate the plates of an originally charged varactor, and mechanical power is converted into electrical power.

Electrostatic transducers are mechanical devices that can produce electricity by using manual power. There are three types of electrostatic generator which are : in-plane overlap, in-plane gap closing and out-of-plane gap closing.

Magnetostrictive energy harvesting

Magnetostrictive harvesters electrical energy from vibration sources in two steps:

First step: Mechanical energy is transferred to magnetic energy via the magnetomechanical coupling of magnetostrictive materials;

P a g e 39 | 126

Second step: Magnetic energy is converted to electrical energy via the electromagnetic coupling on electrical circuits.

Magnetic field (M-field) EH is based on coupling the field flow around the ac current carrying conductors that is clamped by current transformers. This technique is able to provide an adequate rate of continuous power so long as current flow in the line is sufficient. As the amount of current on power distribution level is considered, M-field EH stands as the best candidate for the energization of high power requiring

IoT networks. However, gathering energy from a high current carrying asset in close proximity to the harvester in a safe way is still a challenging issue. To mitigate the safety concerns, M-field-based methods need to be equipped with advanced protection and control mechanisms. This issue compels their utilization in terms of circuit complexity and implementation flexibility.

Piezoelectric energy harvesting

Piezoelectric materials can convert mechanical energy from vibrations or force into electrical energy. These materials have the ability to generate electrical charge when a mechanical load is applied on them. The property of piezoelectric materials is used by the researchers to develop different piezoelectric harvesters in order to power wireless sensors to achieve their various applications.

Technique type Advantages Disadvantages

Electromagnetic

Thermal Power

The phenomena of creating electric potential with a temperature difference and vice-versa can be termed as thermoelectricity. Here, the thermal energy is scavenged to obtain electrical energy to power the electronic devices. Thermoelectric devices are primarily used in space and terrestrial applications. Thermoelectric generation (TEG), is based on converting temperature gradients into electrical power with respect to the seeback effect occurred in semiconductor junctions. TEG is an innate power provision technique for smart grid communications, in which temperature

P a g e 41 | 126

swings between the power line and the environment is used to extract energy. In small scale, Peltier/thermoelectric coolers and thermocouples are widely used for building delay tolerant wireless indoor networks. Although harnessing power from temperature gradients sounds promising, there is a fundamental limit, namely Carnot cycle, to the maximum efficiency at which energy can be harvested from a temperature difference.

Human body-based energy harvesting

Human body based on WSN have received a lot of attention. In these networks, sensor nodes are deployed on or inside of the human body to monitor health evaluation and bodily parameters continuously. Due to their positioning in humans, the nodes need to be operational for long periods of times and preferably for the lifetime of the humans being monitored. Thus, energy can be harvested from humans in different ways, such as through locomotion or changes in finger position, and blood flow and temperature gradients between the warmer body core and colder areas beneath. Human body-based energy harvesting can be widely considered as activity based harvesters and natural physiological parameters based harvesters.

Energy harvesting saving methods

Energy efficiency is of primary important factor affecting the individual node and the entire network operational lifetime.

Energy harvesting is an emerging and reasonably mature technology to overcome the limited lifetime of battery-operated wearable devices and allows continuous recharging of the energy storage during use. The sources Energy harvesting has a big future to power autonomous sensors and insure the continuity of their activities.

For this reason, researchers proposed different types of batteries to store energy harvesting as rechargeable batteries, fiber supercapacitors made of Nanowire-Fiber hybrid structures for wearable/flexible energy storage, Above-IC Microbattery, Hybrid

P a g e 42 | 126

Nanogenerator. We review the major existing approaches proposed to tackle the energy consumption problem of battery-powered motes. Clustering protocols have been proposed to improve energy efficiency. They can limit energy consumption via different incomes:

1) they reduce the communication range inside the cluster which requires less transmission power;

2) they limit the number of message transmissions thanks to fusion performed by the CH;

3) they reduce energy-intensive operations such as coordination and aggregation to the cluster head; 4) they enable to power-off some nodes inside the cluster while the CH takes forwarding responsibilities; 5) they balance energy consumption among nodes via CH rotation.

In addition to energy-efficiency, cluster architectures also improve network scalability by maintaining a hierarchical network. heuristics to route a packet toward the destination area. The important idea is to limit the number of interests in directed diffusion by only considering a certain area rather than sending the interests to the whole network. GEAR can save more energy than directed diffusion. Each node in GEAR keeps an estimated cost and a learning cost of reaching the destination through its neighbors. The estimated cost is a combination of residual energy in sensor battery and distance to destination. The learned cost is a refinement of the estimated cost that accounts for routing around holes in the network. A hole occurs when a node does not have any closer neighbor to the target region than itself. If there are no holes, the estimated cost is equal to the learned cost.

To conserve more energy, some location-based schemes enable nodes to go to sleeping mode if there is no activity. More energy savings can be obtained by having as many sleeping nodes in the network as possible.

Control of radio model transmission

Extending the WSN lifetime can be achieved by reducing the transmission power of sensors. The radio component is the focal module that causes the consumption of energy in the battery of sensor nodes. To decrease energy waste due to wireless communications, optimizations of radio parameters such as coding and modulation schemes, power transmission and antenna are proposed.

Data reduction based

This category aims to reduce the amount of data messages to be delivered to the BS.

This category convers the limitation of unnecessary samples and the limitation of sensing tasks because both data transmission and acquisition consume energy. We can classify data reduction techniques in four types.

Data compression

This type of protocols encodes information in such a way that the number of bits needed to represent the initial message is reduced. It is energy-efficient because it P a g e 47 | 126 reduces transmission times as the packet size is smaller. However, existing compression algorithms are not applicable to sensor nodes because of their resource limitations. Therefore, specific techniques have been developed to adapt to the computational and power capabilities of wireless motes. Data compression methods contains distributed and local data compression approaches.

Adaptive sampling data

The sensing task can be energy-consuming and may generate unneeded samples which affects communication resources and processing costs. Adaptive sampling techniques adjust the sampling rate at each sensor ode while ensuring that application requirements are met in terms of coverage or data information precision.

In human activity recognition applications, adjust the acquisition frequency to the user activity is proposed that may not be necessary to sample at the same rate when the user is sitting or running.

Recharging sensors' battery in WSN

In the last few years, researchers have proposed different methods to increase the WSN lifetime via energy saving approaches as presented in section III. The main goal of these methods is saving energy, during the communication between sensors, without compensation for the energy dissipated via scavenging ambient energy. In this section, we provide recharging batteries based on energy transferred techniques.



Energy Harvesting: In human body networks, wireless sensor nodes are deployed on or inside of the human body to monitor health evaluation and bodily parameters continuously. Due to their positioning in humans, the nodes need to be operational for long periods of times and preferably for the lifetime of the humans being monitored. Thus, energy can be harvested from humans in different ways, such as through locomotion, blood flow as kinetic power by piezoelectric technique [START_REF] Moridi | Development of wireless sensor networks for underground communication and monitoring systems (the cases of underground mine environments)[END_REF] and temperature gradients between the warmer body core and colder areas beneath by thermoelectric generation technique [START_REF] Aziz | A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Network[END_REF]. Human body-based energy harvesting can be widely considered as activity-based harvesters and natural physiological parameters based harvesters. It is essential to note that sensor nodes are considered as energy constrained even during energy harvesting process, so they still require adopting energy conserving approaches.

P a g e 50 | 126

 Wireless energy charging: The widely development of wireless energy transfer is anticipated to increase the sustainability of WHBSNs. Therefore, wireless energy transfer can be perpetually operational, because these techniques can be utilized to transmit power between wearable devices and can power implantable sensor nodes without any information exchange between sensor nodes and the Base Station (BS).

Wireless energy charging in WHBSNs can generally be obtained by electromagnetic (EM) radiation and magnetic resonant coupling methods. However, magnetic resonant coupling emerges to be the most promising technique to address energy requirement of WBSNs, because of its high efficiency at extended distance. One of the actual challenges raised by wireless energy charging technologies is energy cooperation, because it is very vital to transfer energy to neighboring nodes. In future, wireless sensor nodes are intended to have the capacity to harvest energy from the environment and to be efficient in multi-hop energy transferring to other sensor nodes that cannot harvest energy from their environment, to build an autonomous network.

Conclusion

The availability of different sensor devices allows a variety of applications to emerge. However, the problem of energy limits the activities of sensors. In this ------------------------------------------------------------------------------ ----------------------------------------------------------------------------- ----------------------------------------------------- ---------------------------------------------------- --------------------------------------------------------------------------------

Simulation results-----------------------------------------------------------------------61

Conclusion-

Related Work

Clustering has proven to be an effective approach of broadcasting for organizing the network and prolonging its lifetime. Selection of cluster heads (CHs) play an important role in energy consumption management. We can classify clustering protocols into two categories: centralized and decentralized clustering algorithms.

Centralized clustering techniques

In centralized clustering techniques, the BS is the organizer to form clusters. At the start of each round, sensors nodes have to transmit their location information and energy status to the BS. The BS will collect all information from all the sensors nodes in the network, select Cluster Heads (CH), and form clusters. This type of clustering is not a very suitable way to do clustering for a large number of sensors or large network wide. sensor nodes to expend energy uniformly. Each sensor will pick a random number between 0 and 1. If this number is less than a threshold that will be defined the sensor node becomes a CH for the current round. The threshold is set as follows:

𝑇(𝑛) = { 𝑝 1 -𝑝 (𝑟 × 𝑚𝑜𝑑 1 𝑝 ) , 𝑓𝑜𝑟 𝑛 𝜖 𝐺 0, 𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (7)
where 𝑝 is the cluster head probability in the network, 𝑟 is the current round of election and 𝐺 is the set of nodes that were not cluster head in the last round. The behavior of (𝑛) is illustrated in Fig. 4.1: ) rounds, all nodes have a probability of 𝑝 to be a CH once again, over and over again.

P a g e 55 | 126

In this protocol, the selection of CHs is the main task. However, if a CH is out of energy, all the nodes in its cluster will not be able to communicate with the BS. Each node has a chance to be selected as a cluster head according to equation (1) but no optimization in terms of energy consumption is considered. The method to optimize the CH probability applied is as follows:

P a g e 56 | 126

𝑝 𝑜𝑝𝑡 = { 𝐷 𝑀𝑎𝑥 𝐷 𝑡𝑜𝐵𝑆 × √ 𝑛 2𝜋 𝑛 𝑓𝑜𝑟 𝐷 𝑡𝑜𝐵𝑆 ≤ 𝑑 0 𝐷 𝑀𝑎𝑥 𝐷 𝑡𝑜𝐵𝑆 × 𝑑 0 × √ 𝑛 2𝜋 𝑛 𝑓𝑜𝑟 𝐷 𝑡𝑜𝐵𝑆 > 𝑑 0 (8)
where 𝑝 𝑜𝑝𝑡 is the expected optimal CH probability, 𝑛 is the initial number of sensors nodes, 𝐷 𝑡𝑜𝐵𝑆 is the distance between each sensor node and the BS, 𝐷 𝑀𝑎𝑥 is the maximal distance between all sensor nodes, and 𝑑 0 is a threshold distance defined by:

𝑑 0 = √ 𝐸 𝑓𝑠 𝐸 𝑚𝑝 ( 9
)
where 𝐸 𝑓𝑠 and 𝐸 𝑚𝑝 depend on the transmitter amplifier model; 𝐸 𝑓𝑠 is for the free space model and 𝐸 𝑚𝑝 is for multipath model. According to the formula mentioned in [START_REF] Fu | An Energy Balanced Algorithm of LEACH Protocol in WSN[END_REF], the optimal probability for nodes to become cluster heads chosen in this article is 5%. It is worth to note that LEACH protocol do not provide an efficient energy optimization solution. It does not have a strategy to specify CHs positioning and distribution. The random selection of CHs may cause higher communication overhead for the nodes when they communicate with their corresponding CHs and between CHs and the BS.

In the steady phase, clusters are formed and CHs are selected. CHs collect all data information from nodes in their cluster, compress it and transmit it to the BS. Other nodes cannot communicate directly with the BS, they transmit their data information to their CHs. In next rounds, the CHs will be re-elected and clusters will be formed again.

P a g e 57 | 126

Low energy and location based clustering protocol in WSN

A new approach based on LEACH protocol with a new strategy based on location and residual energy of a sensor node to select the CHs is prposed. In our work, we consider that the sensors are heterogeneous, not mobile and randomly distributed in the network. In our designed WSN, we use the following uniform random distribution algorithm to deploy the nodes in the sensing area.

𝑆(𝑖, 𝑋) = { 1 𝑊𝑖𝑑𝑡ℎ , 𝑓𝑜𝑟 0 ≤ 𝑋 ≤ 𝑊𝑖𝑑𝑡ℎ 0, 𝑓𝑜𝑟 0 ≥ 𝑋 𝑜𝑟 𝑋 ≥ 𝑊𝑖𝑑𝑡ℎ (10) 𝑆(𝑖, 𝑌) = { 1 𝐿𝑒𝑛𝑔𝑡ℎ , 𝑓𝑜𝑟 0 ≤ 𝑌 ≤ 𝐿𝑒𝑛𝑔𝑡ℎ 0, 𝑓𝑜𝑟 0 ≥ 𝑌 𝑜𝑟 𝑌 ≥ 𝐿𝑒𝑛𝑔𝑡ℎ (11)
where 𝑆(𝑖, 𝑋) and 𝑆(𝑖, 𝑌) are the probability density of the coordinates 𝑥 and 𝑦 for sensors nodes in the network area. The sink or the BS is unique, fixed at the center of the network and has a continual energy.

Each node has an initial energy:

𝐸 0 (𝑖) = 𝛼 × 𝐸 0 [START_REF] Xiangning | Improvement on LEACH Protocol of Wireless Sensor Network[END_REF] where 𝐸0 (𝑖) is the initial energy of sensor 𝑆(𝑖), 𝛼 is a random value between 0 and 1, and 𝐸 0 is an initial energy value (see table 4.1). The protocol is divided into two phases. The first one consists of selecting CHs and the second one concerns the formation of clusters and the transmission of data information to the sink (i.e., BS).

Each cluster has a unique CH that can be changed after each round epochs. Selection CHs is very important to maximize the lifetime of the network. CHs have an important role that they are the sensors that collect all data messages from all their dependent nodes to compress them and send them to the BS in the second step.

P a g e 58 | 126

Their role needs more energy than other sensors that they just send or receive messages from their sensors neighbors and will not communicate with the BS. If all CHs are dead, the network is dead even if sensors have energy to communicate and if just one CH is dead and it is not replaced by another sensor node, the BS cannot receive any information from all the nodes in its cluster. The selection of CHs affects the network lifetime and the packets messages received by the BS. For these

P a g e 59 | 126

reasons, we propose an approach to the selection of CHs. It is different from LEACH that cannot ensure real load balancing in the case of heterogeneous sensor nodes with different amounts of initial energy. CHs are selected periodically in terms of probabilities without any energy considerations, sensor nodes that have a lower initial energy will be selected as CHs for the same number of rounds as other sensor nodes and will die prematurely. We select CHs without using randomized rotation of the cluster heads. For the setup phase, our approach has been performed based on a hybrid combination of two steps to select periodically CHs and form clusters. The first step depends on the node's residual energy, and the other one depends on the sensor node location.

First step to select CHs based on distance

The distance plays an important role in the CHs selection. A large number of CHs in a network exhausts the energy rapidly and reduces the network lifetime. For a round, if the current sensor is far from all current CHs, this sensor node cannot send any message to any CHs, it will not be in any cluster group and the BS cannot receive any information from CHs about this node. For this issue, we consider that if the distance between current sensor and all other CHs is higher than a distance D which is the minimum distance between the CHs, sensor node could become a CH.

Second step to select CHs based on energy consumption

The second step is about the current sensors' energy in a current round. For LEACH, the nodes with low remnant energy have the same priority to be a CH as the node with high remnant energy. For an iteration, if the residual energy of current sensor is less than the sufficient energy to transmit a data information to the BS, it stills can be selected as a CH despite the fact that it cannot play its role. In addition, replacing a dead sensor by the selection of another CH next round, conducts the network to a waste of energy and a short lifetime of the network. Therefore, we take into consideration the residual energy for each sensor node. If the residual energy for a

P a g e 60 | 126

sensor node is higher than the sufficient energy to transmit a number of data to the BS, it could be selected as a CH.

𝐸(𝑖) > 𝛽 × 𝐸 𝑡𝑜𝑆𝑖𝑛𝑘 (13)

Where (𝑖) is the current energy of the sensor (𝑖), 𝛽 is a coefficient that we will explain it and 𝐸𝑡𝑜𝑆𝑖𝑛𝑘 is the sensor energy sufficient to transmit a data information to the BS.

To determine 𝛽𝑜𝑝𝑡, we suppose that the ideal network is when the initial energy for each sensor is sufficient to let the sensor in life and in work for a number maximum fixed of rounds. For a current round, if the current energy of the sensor is sufficient to send a number of data messages to the BS for the rest of the life of the network, the current sensor is selected to be a CH as follows:

𝛽 𝑜𝑝𝑡 = (𝑟 𝑚𝑎𝑥 -𝑟) (𝑟 𝑚𝑎𝑥 × 𝐸 𝑡𝑜𝑆𝑖𝑛𝑘 𝐸 0 (𝑖) ) ( 14
)
where 𝑟 𝑚𝑎𝑥 is the maximal number of rounds or the lifetime of the network and 𝑟 is the current round. Our approach considers the both parameters distance and residual energy. In a current round, the distance between current sensor node and other CHs should be higher than 𝐷 and its residual energy should be higher than its proportional initial energy. If the two conditions are satisfied, the sensor node can be a CH. In the second-step, sensors transmit and receive messages according to the first-order wireless communication model for the radio hardware energy dissipation. Transmitter dissipates energy to run the radio electronics and power amplifier. Energy required to transmit 𝐿 bits data message is: 4 , 𝑖𝑓 𝑑 > 𝑑 0 [START_REF] Ying | Energy Adaptive Cluster-Head Selection For Wireless Sensor Networks[END_REF] where 𝐸𝑇 (𝑖) is the sensor energy consumption to transmit 𝐿 bits message, 𝐸𝑒𝑙𝑒𝑐 represents the energy consumed to transmit or receive 1 bit message, 𝑑 is the

𝐸 𝑇 (𝑖) = { 𝐿 × 𝐸 𝑒𝑙𝑒𝑐 + 𝐿 × 𝐸 𝑓𝑠 × 𝑑 2 , 𝑖𝑓 𝑑 < 𝑑 0 𝐿 × 𝐸 𝑒𝑙𝑒𝑐 + 𝐿 × 𝐸 𝑚𝑝 × 𝑑

P a g e 61 | 126

distance between the transmitter and receiver, the threshold distance 𝑑 0 . The energy required by radio to receive 𝐿 bits data information is defined as:

𝐸 𝑅 (𝑖) = 𝐿 × 𝐸 𝑒𝑙𝑒𝑐 [START_REF] Fu | An Energy Balanced Algorithm of LEACH Protocol in WSN[END_REF] where 𝐸 𝑅 (𝑖) is the sensor energy consumption to receive a 𝐿 bits data message. The radio parameters used during simulation are mentioned in table 4.1. Our approach, CHs selection and clusters formation depend on the distance between sensor and other CHs selected in the previous round and by considering the current sensor energy. After the selection of CHs, these CHs inform all other nodes that they are elected as heads. Then each node non-CH will decide to be in the cluster of the CH that is less furthest from its location.

Simulation results

For performance evaluation, we have implemented LEACH and our improved protocol. We run the simulations to compare the performance of these protocols on the basis of energy consumption, packet received by the BS and the lifetime of the network.

In our experiments, we used 100 sensors nodes heterogeneous with different initial energy. There are two types of sensors nodes; nodes with high energy and nodes with less energy. These sensors nodes are randomly distributed in 100×100 network.

The BS is located at the middle of the graph. Each data message was 4000 bits long.

The basic parameters used in the simulation are listed in Table 4.1. The energy consumption for all the network is calculated as follows:

𝐸 𝑐 (𝑟) = ∑ 𝐸 0 𝐸(𝑖, 𝑟) 𝑛 𝑖=1 [START_REF] Han | A Comparative Study of Routing Protocols of Heterogeneous Wireless Sensor Networks[END_REF] where 𝐸 𝑐 (𝑟) is the energy consumption of the network in round 𝑟 and it is equal of the difference between the sum of initial energy of each sensor node and the sum of the residual energy of each sensor node in current round. Fig. 4.4 shows that LEACH protocol consumes more energy in comparison with our adaptive protocol. The difference of energy consumption between these protocols is caused by the selection strategy of cluster head.

P a g e 64 | 126

Conclusion

A new adaptive approach for clustering in WSN is proposed. CHs selection is based on sensor node location and its current residual energy. This approach is efficient in terms of energy dissipation, the number of packets received by the BS and the network lifetime. The simulation results show that our approach prolongs the network lifetime about four times better than LEACH protocol and the number of packets received by the BS is more important than in LEACH protocol. In next chapter, we propose an extension for this protocol to prolong the network lifetime, limit the dead nodes and to ameliorate the data packet transmission performance.

P a g e 68 | 126

Chapter 5

Energy saving with a Game Theory based approach in WSN Contents | Chapter 5 --------------------------------------------------------------------------------69 ------------------------------------------------------------------------------69 -------------------------70 -------------------------------------------------------------------------------

Introduction

Related work-

Cooperative game theory based approach -

Non-cooperative game theory approach -----------------------------70

Game theory based distributed clustering approach to maximize WSN lifetime---------------------------------------------------------------------------------------------71

Performance evaluation---------------------------------------------------------------84

Conclusion-

Related work

The Game Theory (GT) is extensively applied in economics to maximize the outcomes by using the mathematical models such as the strategic game theory for the differential information economy which players suggest net trades and prices [START_REF] Cai | Cluster synchronization for directed heterogeneous dynamical networks via decentralized adaptive intermittent pinning control[END_REF].

In the recent years, GT is increasingly applied in WSN for different objectives, such as communication security, energy efficiency, control power transmission, data collection and pursuit evasion [START_REF] Mehta | Comparative Study of BCDCP protocols in Wireless sensor network[END_REF] [10] [11] [12]. In this section, we review the GT used to enhance the energy conservation and extend the network lifetime. The GT can be classified in two top main categories: cooperative and non-cooperative games.

P a g e 70 | 126

Cooperative game theory based approach

To decrease the energy consumed in the network, some sensor nodes cooperate to form coalitions. The coalitional game is considered as one of the most significant type of cooperative game theory. A power control game theoretic model is proposed to optimize the trade-off between energy consumption, and data packets transmission performance. It takes in consideration the individual utility of each sensor player. A novel approach is proposed to identify the overlapping community form in social networks. This approach is based on the shapely values mechanism.

It activates with a weight function to find the stable coalitions of underlying community form of the network. The shapely values and the weight function are updated by the community detection algorithm using the local information. Another type of cooperative game is the bargaining game theory. To achieve the two opposite objectives, which are prolonging the WSN lifetime and maintaining the quality of the sensors' activities in parallel, a Kalai-Smordinsky Bargaining Solution is used to find the best distribution among coalition members.

Non-cooperative game theory based approach

For the non-cooperative game theory, sensor nodes react selfishly to preserve their residual energy by refusing to receipt a data information and forward it in multihop network. The optimal responses for energy efficient non-cooperative game theoretic are obtained when each sensor player improves its strategy to maximize its utility, given the strategies of other sensors players. A non-cooperative game theory model is proposed to control the transmit power levels and the Nash Equilibrium solution exists and attained according to the channel condition and power level. In addition, a non-cooperative game theory is used in the election of the CHs for the clustering model in the literature. In this game model, the sensor node decides to declare itself as a CH or not by calculating the optimal probability in the mixed strategy that depends on the maximizing of its payoff.

P a g e 71 | 126

In addition to the non-cooperative and cooperative game theories, the repeated game theory is involved with a class of active games, in which a game is played for several times and the players have the ability to spot the result of the preceding game before attending the upcoming repetition, a control scheme based on reinforcement learning and game theory is proposed as a routing game model to provide a packetforwarding mechanism for underwater wireless sensors network and reduces the energy consumption.

Game Theory based distributed clustering approach to maximize WSN lifetime

The energy cost for a sensor depends on the energy consumed to achieve its activities. In this section, we present the different factors that play a main role in the consumption of energy. To determinate the residual energy of a node, it is required to find the total energy consumption of a node in the operating of one data packet information. The notations utilized for the factors causing energy consumption by a sensor node are described in Table 5.1. The applications of the sensor node are limited by the availability of the power stored in its battery. If the sensor node expends all its energy, it is considered as dead.

Moreover, it disturbs the dispatching of the information data to reach the sink. In view of the fact that the replacing of the sensor's battery by a new one and the redeployment of the sensors are very costly, it is not appropriate to change the sensor's battery. To overcome these problems, the sensors nodes can use energy harvesting supplies to recharge their batteries. However, the utilization of renewable energy depends on the network environmental conditions as solar, wind, hydrogen, and hybrid sources [START_REF] Aziz | A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Network[END_REF]. In this article, we considered that the sensor's battery can be recharged from the environment (see Fig. 2).

P a g e 73 | 126

A. Set-up phase

It concerns the formation of the clusters and their heads for each round using sensor location and individual energy consumption. Two CHs cannot be in the same cluster.

For this reason, the distance between CHs should be bigger than a threshold distance. The remaining energy level in each sensor node plays an important role in increasing the lifetime of the network. CHs can ensure the link between sensors and the Base Station (BS). For a round, if a CHs is dead, the communications between the sensor nodes in its cluster and the BS are interrupted and no data information from this cluster can reach the BS. A sensor node that has a residual energy bigger than a threshold energy could become a CH for the actual round.

The proposed set-up phase is illustrated by a flowchart scheme in figure fig. 5.3.

P a g e 75 | 126

P a g e 76 | 126

For each round, the selection of the CHs is based on the location and residual energy.

After, each non-CH sensor decides to belong the cluster that corresponds to the minimum distance between its location and the CH location.

B. Steady phase

It corresponds to the data processing, transmitting and receiving between the sensors in the same cluster. This phase is divided in two stages: Data information aggregation and entry market game theory for the communication between neighbors' nodes in the same cluster.

 Data information aggregation

To save the maximum amount of energy consuming during sensors communications and to increase the limited available space in the memory, the data messages are compressed before their registration in the sensor's memory.

If we compress a message of 𝐿(𝑆 𝑖 ) bits to a message of 𝐿(𝑆 𝑖 )/𝑎, the saving energy obtained by compressing the data information can be expressed as follows:

𝐸 𝑠𝑎𝑣𝑖𝑛𝑔 𝑖 = [1 -1 𝑎 ⁄ ]. [𝐸 𝑃 + 𝐸 𝑇 + 𝐸 𝑅 ] -𝐸 𝑐𝑜𝑚𝑝𝑟𝑒𝑠𝑠 ( 18
)
where 𝐸 𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠 is the energy cost to compress 𝐿(𝑆 𝑖 ) bits data packet message.

 The Game Theory based control

At this stage, we propose a non-cooperative game theory based algorithm to control the energy consumed by the sensors in the network. This algorithm is called the Profitable Energy Market Game (PEMG) wherein each player has to decide if he wants to participate or to stay out of the market at each round. The market defines trading rules according to a strategy. In this work, the strategy has two actions: to enter the game or to stay out the game. Each player (i.e. sensor) calculates a payoff that can affect or be affected by the payoffs of other players (i.e. its neighbors). The payoff is a function of the sensor's residual energy. More precisely, the payoffs

P a g e 77 | 126

depend on the players' strategies that stay in the sleeping mode to charge their batteries or enter the game to transmit the sensing data messages.

In what follows, a PEMG is deployed within each cluster. The players in each cluster 𝑖 are 𝑆 𝑖 (𝑗) where 𝑗 = {1,2 ⋯ 𝑁 𝑖 } is the current number of sensors in the cluster for the round 𝑟, 𝑚 𝑖 (𝑘) denotes the number of messages sent by a given player 𝑆 𝑖 (𝑘), 𝑀 𝑗 is the number of 𝑆 𝑖 (𝑗) neighbors and 𝑈 𝑖 (𝑗) is the individual utility function that will be presented later.

The player 𝑆 𝑖 (𝑗) can take one of two decisions denoted by 𝑥 𝑖 (𝑗) set to 0 or 1: Entering the game with 𝑥 𝑖 (𝑗) = 1 and participate by sending messages or staying out of the game and harvesting energy to charge its battery with 𝑥 𝑖 (𝑗) = 0. The sensor's decisions can be expressed as follows:

𝑥 𝑖 (𝑗) = { 1, 𝑆 𝑖 (𝑗) 𝑒𝑛𝑡𝑒𝑟𝑠 𝑡ℎ𝑒 𝑔𝑎𝑚𝑒 0, 𝑆 𝑖 (𝑗) 𝑠𝑡𝑎𝑦𝑠 𝑜𝑢𝑡 𝑡ℎ𝑒 𝑔𝑎𝑚𝑒 (19)
In this paper, our game model in each cluster is defined by: 𝐺 𝑖 = {𝑁 𝑖 , 𝑀 𝑗 , 𝑋 𝑖 (𝑗) 𝑗𝜖𝑁 𝑖 , 𝑈 𝑖 (𝑗) 𝑗𝜖𝑁 𝑖 } (20)

The utility function for a sensor node depends on the cost of the strategy decision taken and it can be expressed by:

𝑈 𝑖 (𝑥 𝑖 (𝑗)) = { 𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗), i𝑓 𝑥 𝑖 (𝑗) = 0 and ∃ 𝑥 𝑖 (𝑘) = 1 𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗), 𝑖𝑓 𝑥 𝑖 (𝑗) = 0 for all j ∈ 𝑀 𝑗 0, i𝑓 𝑥 𝑖 (𝑗) = 1 (21)

P a g e 78 | 126

where ≠ 𝑗 , the cost function 𝐶 𝑖 (𝑗) is the total energy consumed by 𝑆 𝑖 (𝑗) to send a message, the gain function 𝑔 𝑖 (𝑗) is its residual energy and 𝑓 𝑖 (𝑗) is the energy harvested to recharge the sensor's battery.

When a sensor player 𝑗 selects the action to enter the game to transmit messages and its neighbors sensors not then the utility is 𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗). The utility is 𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗), if the sensor player 𝑗 decides not to enter the game to harvest and charge its battery and that, one of its neighbors enters the game.

In our proposed non-cooperative market entry game, the best response dynamics for the sensors players can be acquired in the context that each sensor node updates its strategy in order to maximize its utility, given the strategy of its neighbors (i.e., a mixed strategy).

To determine a mixed strategy equilibrium, we need to consider the expected utility of each player. If a randomly node j in the cluster 𝑖 enters the market with a probability 𝑃 𝑖 (𝑗), the expected utility of the node 𝑗 can be expressed as follows:

It should be noted this expected utility of node 𝑗 reaches its maximum when the battery of the sensor is full (i.e., the residual energy 𝑔 𝑖 (𝑗)is at its maximum) and the energy consumption 𝐶 𝑖 (𝑗) is 0.

The Figure fig. 4 shows the variation of the expected utility function for a given sensor 𝑗, with the variation of the number of neighbors between 1 and 30 and the variation of the probability to enter the game, e.g., 𝑃 𝑖 (𝑗) is between 0.1 and 1. We consider that the neighbors have the same probability to enter the game 𝑃 𝑖 (𝑘) = 0.3, 𝑘 ≠ 𝑗.

P a g e 79 | 126

Assuming in the simulation that the maximum energy capacity available is 0.5 j, the result shown in figure fig. 5.4 shows that the expected utility function has a maximum which is the maximum energy in the sensor's battery. and has a maximum that corresponds to the maximum battery capacity.

Since the best response for a sensor node is when its utility reaches its maximum, we derive the expected utility function and the derivation is obtained by: P a g e 80 | 126

𝜕𝐸[𝑈 𝑖 (𝑥 𝑖 (𝑗)𝑗)] 𝜕𝑃 𝑖 (𝑗) = -(𝐶 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) + (𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) × ∏(1 -𝑃 𝑖 (𝑘)) 𝑀 𝑗 𝑘≠𝑗 (23)
Setting the derivation to zero, we get the maximum as follows:

(𝐶 𝑖 (𝑗) + 𝑓 𝑖 (𝑗))

(𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) = ∏ (1 -𝑃 𝑖 (𝑘)) 𝑀 𝑗 𝑘≠𝑗 (24)
Letting 𝛼 𝑖 (𝑗) = (𝐶 𝑖 (𝑗)+𝑓 𝑖 (𝑗))

(𝑔 𝑖 (𝑗)+𝑓 𝑖 (𝑗))

) and 𝑞 𝑖 (𝑘) = (1 -𝑃 𝑖 (𝑘)), we obtain a system of 𝑀 𝑗 equations from eq. 24 that can be written as:

{ 𝛼 𝑖 (1) = 𝑞 𝑖 (2) × 𝑞 𝑖 (3) × … × 𝑞 𝑖 (𝑀 𝑗 ) 𝛼 𝑖 (2) = 𝑞 𝑖 (1) × 𝑞 𝑖 (3) × … × 𝑞 𝑖 (𝑀 𝑗 ) ⋮ 𝛼 𝑖 (𝑀 𝑗 -1) = 𝑞 𝑖 (1) × … × 𝑞 𝑖 (𝑀 𝑗 -2) × 𝑞 𝑖 (𝑀 𝑗 ) 𝛼 𝑖 (𝑀 𝑗 ) = 𝑞 𝑖 (1) × … × 𝑞 𝑖 (𝑀 𝑗 -2) × 𝑞 𝑖 (𝑀 𝑗 -1) (25)
which can be rewritten as:

(∏(𝑞 𝑖 (𝑘)) 𝑀 𝑗 𝑗=1 ) 𝑀 𝑗 -1 = ∏(𝛼 𝑖 (𝑗))
𝑀 𝑗 𝑗=1 [START_REF] Fugarolas-Alvarez-Ude | A market game approach to differential information economies[END_REF] since 𝑞 𝑖 (𝑘) = (1 -𝑃 𝑖 (𝑘)), the eq. 26 becomes :

(∏(1 -𝑝 𝑖 (𝑘)) 𝑀 𝑗 𝑗=1 ) 𝑀 𝑗 -1 = ∏(𝛼 𝑖 (𝑗)) 𝑀 𝑗 𝑗=1 (27)

P a g e 81 | 126

The optimal probability for a given sensor node 𝑗 in the cluster 𝑖 to enter the market game can be then expressed as follows:

𝑃 𝑖 (𝑗) = 1 - √∏ (𝛼 𝑖 (𝑘)) 𝑀 𝑗 𝑘=1 𝑀 𝑗 -1 𝛼 𝑖 (𝑗) (28)
The maximum utility for a sensor player depends on its strategy and also on the combination decisions of all other neighbors players.

The utility matrix for sensor player 𝑆 𝑖 (𝑗) is shown in Table 5.1.

For the calculation of the utility matrix for each cluster game, the resulting utility coming from the combination of the actions taken by the players (to enter the market game or not to enter the market game) are taken into consideration as indicated by eq.21. If a node player 𝑗 in the cluster 𝑖 enters the market, its utility will be (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) regardless of the action of its neighbors in this cluster. If none of the nodes in the same cluster enters the market, this means that all the nodes 𝑗 and their neighbors' nodes are out of energy and cannot find any available energy sources to harvest and charge their batteries. For this reason, these sensors receive a payoff equal to 0. It is assumed that (𝐶 𝑖 (𝑗) < 𝑔 𝑖 (𝑗)), so that at least a node would enter the market if no other sensor node does. However, if one node enters the market, then each of its neighbors would prefer to be selfish and would maximize its residual energy by charging its battery.

P a g e 82 | 126

Let 𝑋 = {𝑥 𝑖 (1), … 𝑥 𝑖 (𝑀 𝑗 )} be the vector representation of the strategies played by the sensors.

The utility matrix for 𝑆 𝑖 (𝑗) can be written as follows:

𝑈 𝑖 (𝑗) = [ (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) 0 (𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) ] (29)
In a symmetrical market game, the strategy that a sensor player and its neighbors decide to enter the game market, i.e., 𝑋 = {1 … 1}, or the strategy that a sensor player and its neighbors decide to charge their battery in an idle mode, i.e., 𝑋 = {0 … 0}, are not Nash equilibria. Indeed, it is impossible for each node to find out a best response to the strategy decisions. Namely, no pure-strategy Nash Equilibrium exists in our game. However, to permit the entry market game to have symmetrical Nash equilibria, the players can adopt mixed strategies. For any node, as (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) > 0, the sensors players do not have a dominant strategy. We assumed that each sensor player is allowed to choose its strategy decisions randomly following a probability distribution. In other words, there are 𝑀 𝑗 mixed strategies Nash equilibria in the game and the best responses are obtained when the utility of a node 𝑗 to enter the market is equal to the utility of the node 𝑗 to stay out of the market and thus we can compute the equilibrium probability from the table 5.1 by:

𝑈 𝑖 (𝑥 𝑖 (𝑗) = 0) = 𝑈 𝑖 (𝑥 𝑖 (𝑗) = 1) (30)
(𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) × 𝑝 = (𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) × (1 -(1 -𝑝) 𝑀 𝑗 -1 ) (31)
Therefore, from the above eq. 23, we can calculate the equilibrium probability 𝑃 𝐸 to enter the game for a 𝑀 𝑗 Nash equilibrium with a mixed strategies as follows:

𝑃 𝐸 𝑖 (𝑗) = 1 -(1 - (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) (𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) ) 1 𝑀 𝑗 -1 (32)

P a g e 83 | 126

since we have 0 < (𝑔 𝑖 (𝑗)-𝐶 𝑖 (𝑗)) (𝑔 𝑖 (𝑗)+𝑓 𝑖 (𝑗)) < 1. Subsequently, from the eq. 32, we can notice that the probability decreases when the number of neighbors players increases. For example, in the limiting cases, while (𝑀 𝑗 -1) is varying from 1 to infinity, the probability of entering the market game will be changing from 1 to 0.

Performance evaluation

For our experiments, we used 200 sensor nodes in our network, where nodes are randomly distributed in 1000x1000 m2 area. The BS is deployed at the center of the area. For the simulations, a sensor node considers another sensor as a neighbor if the distance that separate them is lower than a threshold 𝐷. This threshold 𝐷 is the maximum radius with which a sensor can receive a fixed number of bits for a fixed power transmission. GT, according respectively to Eq.28 (optimal probability) and Eq.32 (Nash equilibrium probability). The either PEMG versions extend the lifetime of the network beyond 7000 rounds. The results show also that Popt GT consumes less energy than the PEMG with Nash probability Pnash GT. In the case of Pnash GT, the number of dead nodes is the half of the total number after 7000 rounds (i.e., 50%), while in the case of Popt GT and LELC without GT, 60% of the initial number of sensors are dead. This is mainly because of our GT based protocols provide the harvesting option to the sensors. Moreover, with Popt GT, the strategy taken by a sensor privilege the action to enter the market and thus sending messages, i.e., maximizing the strategy of communicating messages via Popt maximization. However, for Pnash GT, all the strategies taken by the sensor are equally probable. The simulation results reported in figure fig. 5.8 show that the number of packets received by the BS for PEMGT with Pnash in our clustering protocol is more important than all the other approaches and that the network is still active after 7000 rounds.

P a g e 87 | 126

However, in the case of clustering without any GT, the network lifetime is limited to 5000 rounds. In addition, the small difference in energy consumed by the network between Pnash and Popt in PEMGT is justified by the number of packets information that reach the BS and the extension of the network lifetime.

P a g e 88 | 126

Conclusion

In chapter 5, a new clustering based protocol using a non-cooperative game theory (GT) approach is proposed with the aims to prolong WSN lifetime. The GT permits to a sensor to decide between two actions: to enter the game and transmit a message or to stay out the game and harvest to charge its battery. ------------------------------------------------------------------------------ ----------------------------------------------------------------------------- --------------------- ----------------------------------------- -----------------------------------------------------------108 6.

Conclusion-------------------------------------------------------------------------------113

P a g e 91 | 126

Introduction

The

Related work

Extending WSN lifetime is one of the most critical challenges in WSNs. In the A topology control protocol based on learning automaton is proposed. The mechanism chooses the proper smallest transmission range of the node using the reinforcement signal produced by the learning automaton of neighbor sensor nodes.

P a g e 93 | 126

Consequently, the choice of transmission range affects the energy consumed by nodes and the overall network lifetime.

A hierarchical clustering protocol based on sensors location and energy consumptions using a non-cooperative game theory (GT) approach to extend the WSN lifetime is proposed. The GT permits to a sensor to decide between two actions:

to enter the game and transmit a message or to stay out the game and harvest energy to charge its battery to reach the Nash Equilibrium (NE) solution for mixed strategies.

In order to increase the energy efficiency and enhance the network life cycle, a

P a g e 95 | 126

In this work, we assume that the vibration resource exists continuously. Moreover, the piezoelectric harvesting energy system is modeled by an equivalent Mass-Spring-Damper (MSD) model (discussed in the section below Ⅳ) with a PID controller that adapts the quantity of energy harvested by the sensor node to still alive and continue its processes. The harvesting energy will be tuned to reduce the error 𝑒 between the setpoint or reference energy 𝑅𝐸.

When the error 𝑒 is bigger than a threshold 𝑇, the sensor node dissipates the majority of its residual energy and the microcontroller MCU switches to off mode and In what follows, the model is formulated mathematically in terms of energy consumption by sensor nodes. All the symbols used in this study are listed in Table 6.1. The energy error 𝑒 can be written as follows:

𝑒(𝑡) = 𝑅𝐸 -𝐸 𝑟 (𝑡) (33)
where the reference energy 𝑅𝐸 is a fixed energy harvesting to charge the sensor's battery and 𝐸 𝑟 is the current residual energy in the sensor's battery.

𝑒(𝑡) = 1 2 𝐶(𝑉 𝑟𝑒𝑓 2 -𝑉 𝑟 (𝑡) 2 ) ( 34
)
where the reference voltage 𝑉 𝑟𝑒𝑓 is the desired fixed voltage and 𝑉 𝑟 (𝑡) is the current voltage for the sensor's battery in time 𝑡.

𝐸 𝑟 (𝑡) = 𝐸 ℎ (𝑡) + 𝐸 𝑟 (𝑡 -1) -𝐸 𝑐 (𝑡)

It is required to find the total energy consumption of a node in the treatment of one data packet information. The total energy consumed by a wireless sensor node includes sensing, processing, communicating data information, switching radio model states and switching MCU modes.

P a g e 99 | 126

Piezoelectric energy harvesting model

For the piezoelectric energy harvesting model, the piezoelectric layers connected on a cantilever beam can be considered as a simple energy-harvesting device. The We can present the equivalence mechanical equation of ( 36) by the electrical equation by applying Kirchhoff's law for an RLC circuit:

𝐿𝑞̈(𝑡) + 𝑅𝑞̇(𝑡) + 1 𝐶 𝑞(𝑡) = 𝑉(𝑡) ( 38
)
Where 𝐿 is the inductance, 𝑅 is the resistance, 𝐶 is the battery's capacitance and 𝑞(𝑡)

is the quantity of electricity generated by the electromotive force voltage 𝑉(𝑡).

The electro-mechanical analogy of our system is represented as follows in Table 6.2. Applying the Laplace transform on equation [START_REF] Hoang | Applications of Repeated Games in Wireless Networks: A Survey[END_REF] with zero initial conditions:

𝑍(𝑠) = ℒ{𝑧(𝑡)} 𝑎𝑛𝑑 𝐹(𝑠) = ℒ{𝑓(𝑡)} (39)
𝑀 𝑝𝑧𝑡 𝑠 2 𝑍(𝑠) + 𝐷 𝑝𝑧𝑡 𝑠𝑍(𝑠) + 𝐾 𝑝𝑧𝑡 𝑍(𝑠) = 𝐹(𝑠)

The open-loop plant transfer function between the input vibration force; 𝐹(𝑠) ant the output displacement 𝑍(𝑠) is given by: For the closed-loop, there are three transfer functions between the setpoint and the output under the PID controller and they are given by:  The sensitivity function:

𝐸(𝑠) = 1 1 + 𝑃(𝑠)𝑈(𝑠) (42)
 The complementary sensitivity function:

𝑇(𝑠) = 𝑃(𝑠)𝑈(𝑠) 1 + 𝑃(𝑠)𝑈(𝑠) (43)
 The input sensitivity function:

𝐼(𝑠) = 𝑈(𝑠) 1 + 𝑃(𝑠)𝑈(𝑠) (45)
Proportional Integral Derivative (PID) controllers are extensively used to provide robustness and optimal performance for stable, unstable, and nonlinear processes [START_REF] Heinzelman | Energy-Efficient Communication Protocol for Wireless Microsensor Networks[END_REF]. It can be simply implementable in analog or digital form. Further, it supports tuning and online retuning based on the performance requirement of the process to be controlled. The output of a PID controller is calculated in the time domain from the response error and it can be expressed mathematically by the equation eq. 46 as follows:

𝑢(𝑡) = 𝑘 𝑑 𝑑𝑒(𝑡) 𝑑𝑡 + 𝑘 𝑝 𝑒(𝑡) + 𝑘 𝑖 ∫ 𝑒(𝑡) 𝑇 0 ( 46
)
where 𝑢(𝑡) and 𝑒(𝑡) are the control and the error signals respectively, 𝑘 𝑑 , 𝑘 𝑝 and 𝑘 𝑖 are the parameters to be tuned.

P a g e 104 | 126

Taking the Laplace transform of equation 46, the transfer function for a PID controller can be expressed as follows:

𝑈(𝑠) = 𝑘 𝑑 𝑠 2 + 𝑘 𝑝 𝑠 + 𝑘 𝑖 𝑠 (47)
In this section, tuning the energy harvested by the piezoelectric system is indispensable to control the quantity of energy harvested, the stability of the system.

Therefore, a Proportional Integral Derivative controller (PID) is adapted to the model represented above. The PID controller uses a control loop feedback mechanism to control the piezoelectric energy harvesting system. The parameters of the PID controller are the proportional gain 𝑘 𝑝 , the integral parameter 𝑘 𝑖 and the derivative parameter 𝑘 𝑑 that affect the control of the system.

The overall effects of controller parameters 𝑘 𝑝 , 𝑘 𝑖 and 𝑘 𝑑 on a closed-loop system are summarized in Table 6.3.

Improving proportional gain 𝑘 𝑝 has the effect of equivalently increasing the control signal for the same level of error. The fact that the controller will drive harder for a given level of error lets the closed-loop system react more speedily, but also to overshoot more. In addition, increasing 𝑘 𝑝 helps to decrease the steady-state error.

The addition of the integral parameter 𝑘 𝑖 will continually increase over time to drive the steady-state error to attain zero. If there is a steady error, the integral response will slowly increase the control signal to the error down. However, the integral term can make the system more sluggish since when the error signal changes sign. It can take a while for the integral windup phenomenon when integral action saturates a controller without the controller driving the error signal toward zero.

The derivative controller parameter 𝑘 𝑑 appends the ability of the control signal to become large, if the error begins sloping upward, even while the amplitude of the error is still quite small. This anticipation helps to add damping to the system, thereby P a g e 105 | 126 decreasing overshoot. However, the addition of this parameter will not affect the steady-state error.

All the values of column 1 of the table are positive. There is no sign changed. Therefore, the system controlled by pour PID controller is stable for the closed loop.

Performance Evaluation

The values of the hardware parameters used in our simulations are those of Mica2

Motes. All parameters used in our energy model are listed in Table 6.5. We also indicate the references where these values originated. Our results are based on existing device parameters. Moreover, that is why they can reflect a real energy model for a type of wireless sensors. Data aggregation energy (J) 5×10 -12 Size of the data packet (bits) 𝑁 4000

P a g e 109 | 126

Compression percentage (%) 𝛼 20

Mass 𝑀 𝑝𝑧𝑡 (g)

P a g e 110 | 126

Parameter value

The supply voltage to the sensor 𝑽 𝒅𝒄 (V) 2.7

The damping ratio of the structure 𝐷 𝑝𝑧𝑡 Therefore, there is a need to include an adaptive PID controller to the system that can enhance the stability and the performance of the system. The battery is recharged to a fixed threshold 0.3249 𝐽𝑜𝑢𝑙𝑒𝑠.

P a g e 111 | 126

The results illustrate that the system is still cyclical stable even if the energy consumption depends on different sensor's activities and it is not the same for each time period.

P a g e 112 | 126

Conclusion

An adaptive energy manager is essential to harvest energy in WSNs. Its core goal is to control the energy harvesting to maximize the lifetime of the sensor nodes and give the sensors the power to achieve their activities. In this paper, autonomous power management with a PID controller provides practical adaptation to the harvested and consumed energy for a sensor node. Moreover, an adaptive controller for the MCU modes, to extend a sensor lifetime depending on the existing vibration in the environment, is presented. Future work will cover a combination between simulation results and real experimental validation of the proposed autonomous energy management system. In addition, we will focus our work on the integration of some existing management energy efficient methods in our self-organized power manager system to save more energy during communications and to reduce the rapid consumption of residual energy in the sensor's battery and from the environmental energy resources. Applying energy efficient protocols for sensors' communications with the autonomous energy management system will increase the overall WSN lifetime.

P a g e 114 | 126

Future work

In this thesis, we put emphasis on of energy efficiency challenge in wireless sensor networks.

Regarding the first part of this thesis, as a future work, we envisage to compare the proposed clustering protocol with other protocols other than LEACH protocol and we will consider the mobility of sensors for data collection in WSN.

For the second part of this thesis, we will focus our work on the integration of some existing energy management saving methods in our self-energy management system to save more energy during communications, to find out a schedule to minimize the charging latency and to reduce the rapid consumption of residual energy in the sensor's battery and from the environmental energy resources. Ideally, we will conduct in-field experiments in order to characterize the performance of our selfenergy management system.

1. 1 .

 1 Overview of our contributions ...................................................................... 2.1. Sensor node structure ............................................................................. 2.2. Multihop flat architecture .......................................................................... 2.3. Hierarchical multihop clustering architecture ........................................... 2.4. Applications of WSNs .............................................................................. 3.1. First-order wireless communication radio model .......................................... 3.2. Different energy saving mechanisms in Wireless Sensor Networks ............ 3.3. Classification of Data Compression approaches (DC) ................................ 3.4. Classification of recharging batteries techniques for WSNs ......................... 4.1. Thresholds for 200 rounds ........................................................................... 4.2. Clustering phases ........................................................................................ 4.3. States of 100 nodes after 100 rounds .......................................................... 4.4. Energy consumption of LEACH protocol and our adaptive clustering protocol ..................................................................................................................... 4.5. Packets received by BS in LEACH protocol for 1000 rounds ....................... 4.6. Packets received by BS in our adaptive protocol for 1000 rounds ............... 4.7. Packets received by BS in our adaptive protocol for 5000 rounds ............... 5.1. Energy harvesting for WSN model ............................................................... 5.2. Set-up and steady phases ........................................................................... 5.3. Flowchart for the set-up phase .................................................................... P a g e 13 | 126 5.4. The expected utility function varies with the number of nodes neighbors and the probability 𝑃 𝑖 (𝑗) and has a maximum that corresponds to the maximum battery capacity ............................................................................................ 79 5.5. Entering game probability varies with the number of nodes neighbors for a Nash Equilibrium mixed strategy ................................................................. 83 5.6. Energy consumption by the network ............................................................ 85 5.7. Dead nodes in the network .......................................................................... 87 5.8. Number of Data Packets received by the BS. With Pnash GT, the network is still active as the packets continue to be received by BS beyond the other protocols ...................................................................................................... 88 6.1. Architecture of the global system of wireless sensor composed of two main blocks one to manage energy consumed and residual energy in the battery and another one to harvest energy from the ambient source ....................... 95 6.2. Schematic of piezoelectric harvesting structure ........................................... 99 6.3. Equivalent MSD model .............................................................................. 100 6.4. Transfer function between the input vibration force and the output displacement for an open loop without the PID controller .......................... 102 6.5. Flowchart to find the transfer function for the PID controller based on Ziegler-Nichols heuristic tuning method ................................................................. 106 6.6. Transfer function between the input vibration force and the output displacement for a closed-loop with an adaptive PID controller based on Ziegler-Nichols tuning method ................................................................... 108 6.7. Self-power manager behavior without the adaptive PID controller ............ 111 6.8. The self-power manager controller organizes its wake-up mode to do its activities and its off mode to harvest energy controlled by the adaptive PID controller depending on its residual energy ............................................... 112

Figure 1 . 1

 11 Figure 1.1: Overview of thesis organization

Figure 2 . 1 :

 21 Figure 2.1: Sensor node structure

Figure 2 .

 2 Figure 2.2.1 illustrates typically the flat network architecture.

Figure 2 . 2 :

 22 Figure 2.2: Multihop flat architecture

  Figure 2.2.2 illustrates an example of hierarchical architecture.

Figure 2 . 3 :

 23 Figure 2.3: Hierarchical mulihop clustering architecture

Figure 2 .

 2 Figure 2.3 shows a broad classification of WSNs applications in different disciplines.

Figure 2 . 4 :

 24 Figure 2.4: Applications of WSNs

- 38 3. 3 . 2 . 4 .

 38324 Piezoelectric energy harvesting-

- 39 3. 3 . 3 .

 3933 Thermal power-

- 40 3. 3 . 4 .

 4034 Human-body based energy harvesting-

- 41 3. 4 . 1 .

 4141 Topology control-

- 42 3. 4 . 1 . 1 .

 42411 Clustering protocols-

- 43 3. 4 . 1 . 2 .

 43412 Mac protocols-

- 43 3. 4 . 1 . 3 .

 43413 Routing protocols-

- 44 3. 4 . 1 . 4 .

 44414 Control of radio model transmission-

- 46 3. 4 . 2 . 1 .

 46421 Data compression -

- 47 3. 4 . 2 . 3 .

 47423 Network coding-

- 48 3. 4 . 2 . 4 .

 48424 Adaptive sampling data -

Figure 3 . 1 :

 31 Figure 3.1: First-order wireless communication radio model

  Figure fig. 3.2 presents the classification of energy efficiency saving mechanisms in WSNs.

Figure 3 . 2 :

 32 Figure 3.2: Different energy saving mechanisms in Wireless Sensor Networks

  Figure 3.3 summarized data compression approaches.

Figure 3 . 3 :

 33 Figure 3.3: Classification of Data Compression approaches (DC)

Figure fig. 3 .

 3 Figure fig. 3.4 shows the two approaches to recharge wireless human body sensors' batteries.

Figure 3 . 4 :

 34 Figure 3.4: Classification of recharging batteries techniques for WSNs

  chapter, we have presented different energy harvesting sources. In addition, we have summarized different techniques that tackle the energy efficiency challenge in WSNs and classified them in two major main classes as shown in Figure3.2. In next chapter, we present our works that proposed new protocol based on energy efficiency mechanisms.

- 52 4. 2 .

 522 Related work-

- 52 4. 2 . 1 .

 5221 Centralized clustering-

- 52 4. 2 . 2 .

 5222 Distributed clustering -

- 53 4. 3 . 54 4. 4 .

 533544 Low energy adaptive clustering protocols in WSN---------------------------Low energy and location based clustering protocol in WSN---------------57

67 P a g e 52 | 126 4 . 1 .

 6741 IntroductionWSN workload needs an unlimited lifetime energy without depending on a limited energy usage while sensor nodes are supplied by batteries or supercapacitors with limited energy. Particular algorithms must be employed so that energy consumption is reduced. In this paper, clustering protocols are investigated and a new approach based on clustering protocols is proposed to increase the lifetime of the wireless sensor network. Simulation results show that its performance is better in terms of prolonging the lifetime of the network and increasing the number of data packets received by the Base Station (BS).

For 4 . 3 .

 43 example, BCDCP (Base-Station Controlled Dynamic Clustering Protocol) is a centralized clustering protocol with a unique BS that is capable of complex computation, the CHs are selected by the BS randomly and all the routes and paths for transmission and reception of data information are selected by the BS. Each node needs to transmit data messages regarding its location and residual energy to the BSP a g e 53 | 126during the formation of clusters. Therefore, BCDCP increases the design complexity and the energy consumption of the nodes in the large-range networks. BCSP (Base station Centralized Simple Clustering Protocol) is a protocol where in the BS does not collect any information about location of the sensor nods but utilizes information about remaining energy of each sensor node and the number of CHs depending on the circumstance of the sensor network. Each node should send its current energy information along with the sensing information, increasing the overhead. The drawback of this protocol is that due to its centralized implementation, it is not so appropriate for sensor networks with a large number of nodes. In addition, without any location information, BCSP cannot guarantee a uniform distribution of CHs nodes and their clusters. 4.2.2. Distributed clustering techniques Distributed clustering techniques eliminate the need of a centralized station to create CHs and clusters. The low energy and hierarchical structure models are generally used to create clusters and select CHs in two levels. At the first level, there is a selection of CHs and at the second level, the data messages are transferred by sensor nodes to BS via CHs. BS just receives messages and does not control the creation of clusters. EEMDC (Energy Efficient Multi level and Distance aware Clustering) is that extends the WSN lifetime while providing more stability and reliability to the network. This routing protocol splits the network area into three logical layers. After the partition of the network area, the hotspot problem is fixed, the distance between the nodes and the CH and between the CH and the BS are taken into account when considering the hop-count value of the nodes. In addition, CHs are elected by acquiring the average leftover energy of the nodes, and the data messages are delivered to the BS using the shortest distance path to the BS. ICCBP (Inter Cluster Chain Based Protocol) is a new clustering algorithm that uses multi-hop and intra-cluster communication with updating CHs when the existing CHs dissipate their energy. A new structure to construct clusters and establish connections between sensors is proposed. In this protocol, the distance between CHs depends on a P a g e 54 | 126 threshold calculated by the signal message transmission to insure the connections between clusters. In addition, this protocol creates a virtual wireless sensor networks. Low energy adaptive clustering protocol in WSN LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol is a decentralized clustering protocol based on the homogeneous WSNs. Its first phase consists in grouping sensors into clusters. LEACH is a dynamic clustering method that update clusters and head clusters (CH) each round. Each round starts with a setup phase and finishes with steady state. In the setup phase, it rotates the CHs role among all

Figure 4 . 1 : 1 𝑝-

 411 Figure 4.1: Thresholds for 200 rounds

Figure 4 . 2 :

 42 Figure 4.2: Clustering phases

Figure 4 .

 4 Figure 4.3 shows the state of the network after 1000 rounds with different states of the sensors nodes.

Figure 4 . 3 :

 43 Figure 4.3: States of 100 nodes after 100 rounds

Figure. 4 . 4 :

 44 Figure. 4.4: Energy consumption for LEACH protocol and our adaptive protocol

Fig. 4 .

 4 Fig. 4.5 and 4.6 show that the number of packets received by the BS in LEACH and our protocol. Our protocol achieves higher data delivery compared to LEACH for 1000 rounds. In addition, the lifetime of the network for LEACH protocol is about 1000 rounds; the BS does not receive anymore packets as shown in fig. 4.4. Despite the fact that the network does not consume all its initial energy, the network is dead. For our protocol, the network lifetime of the network is extended beyond 5000 rounds as shown in fig. 4.6. The network lifetime is increased beyond four times better than LEACH protocol.

Figure. 4 . 5 :

 45 Figure. 4.5: Packets received by BS in LEACH Protocol for 1000 rounds

Figure. 4 . 6 :Figure. 4 . 7 :

 4647 Figure. 4.6: Packets received by BS in our adaptive protocol for 1000 rounds

- 88 P a g e 69 | 126 5 . 1 .

 8851 IntroductionOne of the most significant difficulty in Wireless Sensors Network (WSN) is the development of an effective topology control method that can support the quality of the network, respect the limited memory and at the same time increase the lifetime of the network. This chapter introduces a new approach by mixing a non-cooperative Game Theory technique with a decentralized clustering algorithm presented in chapter 4 to address the problem of maximizing the network lifetime. More precisely, this approach uses Game Theory techniques to control the activities of a sensor node and its neighbors to limit the number of the forwarding messages and to maximize the lifetime of the sensor's battery. In other words, the approach will decrease the energy consumed by the WSN by decreasing the number of forwarded packets and improve the network lifetime by harvesting energy from the environment. The simulations results show that the performances in terms of energy saving and increasing the number of data packets received by base station outperforms those with distributed based clustering algorithms without GT, such as low energy and location based clustering LELC and LEACH algorithms.

Figure. 5 . 1 .

 51 Figure. 5.1. Energy harvesting for WSN model

Figure. 5 . 2 :

 52 Figure. 5.2: Set-up and steady phases

Figure. 5 . 3 :

 53 Figure. 5.3: Flowchart for the set-up phase

  𝐸[𝑈 𝑖 (𝑥 𝑖 (𝑗))] = 𝑃 𝑖 (𝑗) × (𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)) + (1 -𝑃 𝑖 (𝑗)) × (𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)) × (1 -∏(1 -𝑃 𝑖 (𝑘)) 𝑀 𝑗 𝑘≠𝑗 )

Figure. 5 . 4 :

 54 Figure. 5.4: The expected utility function varies with the number of nodes neighbors and the probability 𝑃 𝑖 (𝑗)

Figure. 5 . 5 :

 55 Figure. 5.5: Entering game probability varies with the number of nodes neighbors for a Nash Equilibrium mixed strategies.

Fig. 5 .

 5 Fig.5.5 depicts the entering game probability that is given in Eq. 24 with increasing number of neighbors of the source, from 1 to 30, for different values of actual energy in the battery 𝑔 𝑖 (𝑗). When the number of neighbors decreases (from 30 to 1) when some neighbors nodes dead, the forwarding entering game increases.

  In figure fig.5.6, we compare the energy consumed by the network for 7000 rounds by comparing our proposed approach with other protocols from the literature: the Leach clustering protocol and a clustering based protocol. The results show that these Leach protocol consumes all its energy after 2000 rounds. An improved version of Leach via a low energy and location based clustering approach (LELC) presented in[START_REF] Muruganathan | A centralized energy-efficient routing protocol for wireless sensor networks[END_REF] stills have energy for 5000 rounds. Figure fig. 5.6 shows also the results of the two versions of the proposed PEMG with Game Theory (GT), Popt GT and Pnash

Figure. 5 . 6 :

 56 Figure. 5.6: Energy consumption by the network.

Figure. 5 . 7 :

 57 Figure. 5.7: Dead Nodes in the network.

Figure. 5 . 8 :

 58 Figure. 5.8: Number of Data Packets received by the BS. With Pnash GT, the network is still active as the packets continue to be received by BS beyond the other protocols.

  For the network organization, a clustering protocol based on sensors locations and energy consumptions is used and a GT based algorithm is deployed within each cluster. The objective is to find out the Nash Equilibrium (NE) solution for mixed strategies. The simulation results show that the proposed approaches outperform those without GT P a g e 89 | 126 in terms of energy consumption, nodes and network lifetimes. In other words, combining a GT based approach with a clustering protocol provides an efficient solution for energy harvesting to prolong WSNs lifetime. The future work will focus on the control of the energy harvesting process in the sensors.

-91 6 . 2 .

 62 Related work-

-91 6 . 3 .

 63 The proposed adaptive energy management system-

-94 6 . 4 .

 64 Piezoelectric energy harvesting model-

-99 6 . 5 .

 65 Performance evaluation-

  possibility to avert replacing drained batteries is extremely important in Wireless Sensor Networks to avoid the high cost and time to replace batteries and redeploy them. Therefore, energy harvesting has attracted researchers and developers to recharge low power devices as Vivo-Nano-Robots, MEMS, Claytronics and WSN. Energy harvesting is the method of extracting energy from the environment through different sources of energy. It is considered an emerging and reasonably mature technology to overcome the limited lifetime of battery-operated wearable devices and allows continuous recharging of the energy stored during use. The environmental energy for scavenging is mostly provided by ambient light (artificial lighting and solar lighting), radio frequency, thermal sources, kinetic and vibration sources. To harvest energy from vibration, there are different techniques such as electrostatic, electromagnetic, magnetostrictive and piezoelectric.

  last years, researchers have proposed different methods to increase the WSN lifetime via saving approaches. The main goal of these methods is saving energy during the communication between sensors without a compensation for the energy dissipated via scavenging ambient energy. Specifically, we can classify energy methods in different energy saving mechanisms to reduce the energy consumption in WSN and energy harvesting to recompense the energy consumed during sensors' activities. Most of proposed approaches in literature are saving energy based mechanisms such as sensors deployment strategies, data oriented techniques, topology control methods, energy routing protocols. For example in the literature, some researchers propose a statistical strategy for the deployment of nodes. The statistical node deployment strategy is based on Quasi-P a g e 92 | 126 random method of low-discrepancy sequences to increase the lifetime and the coverage of the network.The attentiveness to data oriented techniques has increased recently. These approaches reduce the size of information via compression. Therefore, data oriented techniques are efficient to save energy during transmission and reception of these data information. In addition, the reduction of data is not only efficient in saving energy but also in saving more memory in the sensors.An adaptive Energy efficient distributed compression technique based on DCS andQCS is proposed in the literature for sensor network data compressed gathering. This method is more performance in terms of conservation energy compared to conventional QCS architecture for data collection in WSN.Other researchers proposed a Distributed Distortion-Rate Optimized Compressed Sensing (DQCS) method to compress data in WSN under complexity-constrained encoding which minimizes a weighted sum between the mean square error (MSE) signal reconstruction distortion and the average encoding rate.A local lossless neighborhood indexing sequence (NIS) compression algorithm for data compression in WSN is proposed. NIS approach dynamically assigns shorter length code information to each character in the input sequence by exploiting the occurrence of neighboring bits and every data packet is decompressed independently from others. Therefore, NIS algorithm helps to decrease the network load, which results in low data packet loss.Regarding topology control techniques in saving energy category in WSN, energy harvesting Media Access Control (MAC) protocols are one of the most notable mechanisms and recently used. Different energy harvesting MAC protocols are discussed to prolong the network lifetime.

  fig. 6.1.

Figure. 6 . 1 :

 61 Figure. 6.1: Architecture of the global system of wireless sensor composed of two main blocks one to manage energy consumed and residual energy in the battery and another one to harvest energy from the ambient source

  fig.6.2. When the cantilever vibrates, at the first mode, the force which proceeds on the piezoelectric layers can be simplified to a 1-D model and regarded as a force 𝑓 acting on the lateral surface.

Figure. 6 . 2 :

 62 Figure. 6.2: Schematic of piezoelectric harvesting structure

Figure. 6 . 3 :

 63 Figure. 6.3: Equivalent MSD model

P a g e 102 | 126 𝑃 1 (

 1261 Figure Fig. 6 shows the displacement of the mass 𝑀 𝑝𝑧𝑡 produced by an external vibration effort 𝐹. The DC gain is 1/𝐾 𝑝𝑧𝑡 , so 0.8116 is the final value of the output for a unit step input. The rise time is about 0.4 𝑠, the settling time is about 6.562 𝑠 and the overshoot is about 50.6432.

Figure 6 . 4 :

 64 Figure 6.4: Transfer function between the input vibration force and the output displacement for an open loop without the PID controller

Figure. 6 . 5 :

 65 Figure. 6.5: Flowchart to find the transfer function for the PID controller based on Ziegler-Nichols heuristic tuning method

Figure Fig. 8

 8 Figure Fig. 8 shows the displacement of the mass 𝑀 𝑝𝑧𝑡 produced by an external vibration effort 𝐹 controlled by our PID controller. This controller is designed based on Ziegler-Nichols tuning method. The new system has a fast rise time 0.0311 𝑠, it reduces also the settling time 0.0509 𝑠, and minimize the overshoot 1.1887.

Figure. 6 . 6 :

 66 Figure. 6.6: Transfer function between the input vibration force and the output displacement for a closed-loop with an adaptive PID controller based on Ziegler-Nichols tuning method.

Figure

  Figure fig. 6.7 shows the residual energy in the sensor's battery. When the battery is out of energy, the MCU switches to off mode. During the off mode, the battery is charged with a vibration harvesting energy without the adaptive PID controller. The battery is recharged to 0.1245 𝐽𝑜𝑢𝑙𝑒𝑠 and cannot reach more than this value.

Figure. 6 . 7 :

 67 Figure. 6.7: Self-power manager behavior without the adaptive PID controller.

Figure fig. 6 .

 6 Figure fig. 6.8 shows the residual energy in the sensor's battery. When the residual energy is less than a given threshold value (e.g.20%,) of the initial energy 0.5𝐽𝑜𝑢𝑙𝑒𝑠with𝛼 = 1, the MCU switches to off mode. During the off mode, the battery is charged with a vibration harvesting energy controlled by the adaptive PID controller.

Figure. 6 . 8 :

 68 Figure.[START_REF] Muruganathan | A centralized energy-efficient routing protocol for wireless sensor networks[END_REF].8: The self-power manager controller organizes its wake-up mode to do its activities and its off mode to harvest energy controlled by the adaptive PID controller depending on its residual energy.

  wireless communications and Micro-Electro-Mechanical systems have enabled the advancements in wireless sensor networks (WSN), which are used in different fields such as healthcare, supervision, environmental and industrial systems. However, due to the limitation of available energy in battery's sensors, these applications still face a major energy issue that prevent their extensive adoption. In this thesis, we present the topic of Wireless Sensor Network lifetime and we overcome this challenge through several contributions.We proposed distributed algorithms and we developed new architectures and models to improve the WSN lifetime performances and we evaluated our methods through extensive numerical experiments and simulations. At first part of this thesis, we introduce general wireless sensor networks and their various application families. This top-down study surveys a comprehensive review of existing energy-efficient mechanisms designed for wireless sensor networks in the literature and various energy harvesting extraction methods. Afterwards, we proposed a low energy and location based clustering protocol to improve the energy efficiency in wireless sensor network. The network is divided into clusters and each cluster has a cluster head as responsible of data transmission between clusters to transmit data information to the base station. The clusters and their cluster heads are updated depending on residual energy in the battery powered sensors and the distance between sensors and BS. Then, we have proposed a game theory based clustering protocol which is an extension of the low energy and location based clustering protocol that aim at load balancing, minimizing the energy consumed by transmission of data packets and avoiding nodes with low residual energy. The second part of this thesis contains an autonomous energy management system achieving piezoelectric energy harvesting to recompense energy consumed in wireless sensors. This system model proposed can tune the extraction of energy P a g e 116 | 126 from vibration sources in the environment and can satisfy the energy-demand of sensors by PID and MCU controllers.

-50 P a g e 34 | 126 3.1. Introduction

	In recent years, Wireless Sensor Networks (WSNs) have been considered as
	one of the most attractive technologies due to development of sensor devices for
	different domain's applications. In addition, wireless sensors are not high cost and
	can be deployed in inaccessible environments. Therefore, this technology make
	monitoring and controlling easier especially in the hostile areas. However, WSN
	consists of a large number of sensor nodes with limited power capabilities, limited
	storage capacity, limited energy supplied, and multiple on-board sensors that can
	take readings, such as temperature, pressure, humidity and so on. Sensor nodes are
	typically equipped by batteries, which are limited in energy capacity. In addition,
	wireless sensors are mostly deployed in inaccessible or polluted environments where
	replacement batteries is very difficult or even impossible. For this purpose, there is a
	need for efficient power saving mechanisms to decrease the energy consumption for
	sensor nodes and to prolong the network lifetime.
	The rest of this chapter is organized as follows. Section 3.2 is dedicated for
	energy consumption in Wireless Sensor Networks. Then, in Section 3.3, we give an
	overview of the different existing sources of energy harvesting. Section 3.4 provides
	a variety of power saving mechanisms conducted by researchers. Finally, Section 3.5

Table 3 .

 3

1: Comparison of the different kinetic types of harvesting energy

Table 4 .

 4

		1: Simulation parameters
	Parameter	value
	Network area (m2)	100×100
	BS location	(50, 50)
	Sensor nodes 𝑛	100

Table 5 .

 5

		1: Notations definition
	Notations	Definition
	𝑛	Number of sensor nodes in the network
	𝑆𝑖	Sensor node where 𝑖 = {1, 2,…, 𝑛}
	𝐸𝑆	Sensing energy cost
	𝐸𝑃	Processing energy cost
	𝐸𝑇	Transmitting energy cost

A. Rechargeable battery model

Table 5 .

 5

		2: Symetric entering market game matrix
		All 𝑆 𝑖 (𝑘) do not enter	At least one enters the
		the market	market
	𝑆 𝑖 (𝑗) enters the market	𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)	𝑔 𝑖 (𝑗) -𝐶 𝑖 (𝑗)
	𝑆 𝑖 (𝑗) doesn't enter the	0	𝑔 𝑖 (𝑗) + 𝑓 𝑖 (𝑗)
	market

Table 5 . 3 :

 53 Simulation parameters

The proposed adaptive energy management system

	6.3. Wireless Sensor Networks (WSNs) are extensively used in monitoring
	applications such as humidity and temperature sensing in smart buildings, industrial
	automation, and predicting crop health. Sensor nodes are deployed in remote places
	to sense the data information from the environment and to transmit the sensing data
	to the Base Station (BS). When a sensor is drained of energy, it can no longer achieve
	its role without a substituted source of energy. However, limited energy in a sensor's
	battery prevents the long-term process in such applications. In addition, replacing the
	Genetic algorithmbased Energy-efficient Clustering and Routing Approach (GECRA) sensors' batteries and redeploying the sensors is very expensive in terms of time and
	is presented. GECR algorithm aims to calculate the total energy consumed by all budget. To overcome the energy limitation without changing the size of sensors,
	sensor nodes where the algorithm encodes the clustering scheme and routing researchers have proposed the use of energy harvesting to reload the rechargeable
	scheme together in the same chromosome. battery by power. Therefore, efficient power management is required to increase the
	Researchers have worked to reduce the energy consumption in the stage of wireless benefits of having additional environmental energy. This paper presents a new self-
	sensors deployment and during sensors activities as communication and sensing management of energy based on Proportional Integral Derivative controller (PID) to
	data via energy saving mechanisms. Therefore, the WSN lifetime will be extended tune the energy harvesting and Microprocessor Controller Unit (MCU) to control the
	but for a limited time. Instead of applying the energy saving methods, the energy sensor modes.
	harvesting can be used to recharge sensor's battery to increase the power stored in
	the sensor's battery thereby extending the overall network lifetime depending on the The target is an autonomous system to harvest energy spontaneously upon
	availability of the ambient energy harvesting resources. This article presents a new reaching a threshold of energy consumption. The system proposed is composed of
	self-management of energy based on Proportional Integral Derivative (PID) controller two main blocks as designed in figure fig. 6.1: The Battery Management Subsystem
	to tune the energy harvesting and Microprocessor Controller Unit (MCU) to control block that manages a rechargeable battery or a super-capacitor of the sensor and
	the sensor modes. monitors its states via two (Management Controller Unit) MCU modes. In on mode,
	the sensor is active and can sense information from its environment, write/read data
	from memory, and communicate messages with other wireless sensors. During
	achieving activities, the sensor consumes energy until a threshold and the MCU turns
	to off mode. In the off mode, the block piezoelectric energy harvesting structure is
	triggered. This block is controlled via a control loop feedback using a Proportional
	Integral Derivate controller PID that scavenges ambient energy to recharge the
	sensor's battery.
	P a g e 94 | 126

TABLE 6 . 1 :

 61 List of variables used in the study.

	Variables	Definition
	RE (J)	Reference Energy
	𝑒(𝑡)	Energy error in time 𝑡
		Threshold energy
	𝑉 𝑟𝑒𝑓	Reference fixed voltage
	𝑉 𝑟 (𝑡)	Current-voltage
	𝐶	Capacitance
	𝐸 𝑟 (𝑡)	Residual energy at time 𝑡
	𝐸 ℎ (𝑡)	Harvesting energy at time 𝑡
	𝐸 𝑐 (𝑡)	Energy consumed at time 𝑡
	𝐸 𝑆	Sensing energy consumption
	N	Number of bits transmitted by the sensor
	𝛼	Data compression percentage
	𝑇 𝑆𝑒𝑛𝑠 (ms)	Sensing time for one bit data
	𝐼 𝑆𝑒𝑛𝑠 (mA)	Sensing current
	𝐸 𝑃	Processing energy consumption
	𝐼 𝑊𝑟𝑖𝑡𝑒 (mA)	Writing current
	𝑇 𝑊𝑟𝑖𝑡𝑒 (ms)	Writing time for one bit
	𝐼 𝑅𝑒𝑎𝑑 (mA)	Reading current
	𝑇 𝑅𝑒𝑎𝑑 (µs)	Reading time for one bit
	𝐸 𝑇	Data transmission energy consumption
	𝐸 𝑒𝑙𝑒𝑐 (nJ/bit)	Energy dissipation: electronics
		Energy consumption in multipath model

TABLE 6 .

 6

	2: Electro-mechanical analogy of piezoelectric energy harvesting system
	Mechanical system	Electrical system
	Position 𝑧(𝑡)	Electrical charge 𝑞(𝑡)
	Mass 𝑀 𝑝𝑧𝑡	Inductance 𝐿
	Damping coefficient 𝐷 𝑝𝑧𝑡	Resistance 𝑅
	Spring constant 𝐾 𝑝𝑧𝑡	Inverse of capacitance 𝐶
	External force 𝑓(𝑡)	Voltage 𝑉(𝑡)

  𝑇(𝑠) = (𝑘 𝑑 𝑠 2 + 𝑘 𝑝 𝑠 + 𝑘 𝑖 ) (𝑀 𝑝𝑧𝑡 𝑠 3 + (𝐷 𝑝𝑧𝑡 + 𝑘 𝑑 )𝑠 2 + (𝐾 𝑝𝑧𝑡 + 𝑘 𝑝 )𝑠 + 𝑘 𝑖 )

TABLE 6 .

 6 3: The effects of PID controller parameters on a closed-loop systemThere are different methods to find the PID controller parameters that satisfy these goals. The fast rise time, the minimal overshoot, the minimal steady-state error and the minimal settling time. The gains of a PID controller can be found by trial and error method. In our work, we apply Ziegler-Nichols heuristic tuning method to find the PID controller parameters. It consists to find the ultimate gain 𝐾 𝑢 and the period 𝑇 𝑢 by setting the Integral and Derivative gains to zero. The Proportional gain 𝐺 increases until it reaches the ultimate gain, at which the output displacement of the control loop has consistent oscillations. For a classic PID controller, we can find 𝑘 𝑝 , 𝑘 𝑖 and 𝑘 𝑑 by the relations as in Table6.4:

	PID parameter s	Rise Time Overshoot	Settling time	SSteady-State error
	𝑘 𝑝	Decrease	Increase	Small changes Decrease
	𝑘 𝑖	Decrease	Increase	Increase	Decrease
	𝑘 𝑑	Small changes	Decrease	Decrease	No change

TABLE 6 .

 6 4: The PID controller parameters according to Ziegler-Nichols tuning method on a closed-loop systemThe Flow Chart as shown in figure fig.6.5 explains how to find the PID controller parameters based on Ziegler-Nichols heuristic tuning method.

	𝒌 𝒑	𝒌 𝒊	𝒌 𝒅
	0.6𝐾 𝑢	1.2	𝑇 𝑢 𝐾 𝑢	0.075𝐾 𝑢 𝑇 𝑢

P a g e 106 | 126

Table 6

 6 .4. The transfer function for the PID controller can be found from eq. 2 and can be rewritten Applying Routh-Hurwitz criterion, we can verify if the system in a closed loop converges, stable or not.The following structure of Routh-Hurwitz criterion matrix for the complementary sensitivity transfer function is shown as follows: 𝐷 𝑝𝑧𝑡 + 𝑘 𝑑 ) × (K pzt + k p )) -(𝑘 𝑖 × 𝑀 𝑝𝑧𝑡 ))

	as follows:
	𝑢(𝑠) =	9.8699𝑠 2 + 20.2366𝑠 + 10.3729 𝑠	(48)

TABLE 6 .

 6

		5: Simulation parameters
	Parameter		value
	The supply voltage to the sensor 𝑽 𝒅𝒄 (V)	2.7
	Reference Energy RE (J)		0.2
	Threshold energy		0.1
	Sensing time for one bit of data 𝑇 𝑆𝑒𝑛𝑠 (ms)	0.5
	Sensing current 𝐼 𝑆𝑒𝑛𝑠 (mA)		25
	Writing current 𝐼 𝑊𝑟𝑖𝑡𝑒 (mA)		18.4
	Writing time for one bit 𝑇 𝑊𝑟𝑖𝑡𝑒 (ms)		12.9
	Reading current 𝐼 𝑅𝑒𝑎𝑑 (mA)		6.2
	Reading time for one bit 𝑇 𝑅𝑒𝑎𝑑 (µs)		565
	On mode for the MicroController Unit (MCU) current 𝐼 𝐴𝑐𝑡𝑖𝑣𝑒 (mA)	8
	On mode for the MicroController Unit (MCU) time 𝑇 𝐴𝑐𝑡𝑖𝑣𝑒 (ms)	1
	Off mode for the MicroController Unit
	(MCU) current 𝐼 𝑆𝑙𝑒𝑒𝑝 (µA)		1
	Off mode for the MicroController Unit
	(MCU) time 𝑇 𝑆𝑙𝑒𝑒𝑝 (ms)		299
	Initial energy (J) 𝐸 0		0.5 𝛼
	Energy dissipation: electronics 𝐸 𝑒𝑙𝑒𝑐 (nJ/bit) 50
	parameters of amplifier energy consumption 𝐸𝑚𝑝 (pJ/bit/m4) (pJ/bit/m2)	and 𝐸𝑓𝑠	0.0013 and 10