1.1 Power density of popular harvesting technologies [20]. . . . . . . . . . . . . 2.1 Energy consumption of the PowWow platform [15] . . . . . . . . . . . . . 3.1 Performance of dierent energy predictors for solar energy . . . . . . . . . 3.2 Average error (%) of dierent predictor interval . . . . . . . . . . . . . . . 4.1 Comparison of V S (mV) with dierent values of k. . . . . . . . . . . . . . .

List of Figures

to both primary and secondary storages by the energy ow controller. . . 2.6 Multi-sources architecture for photovoltaics (PV), themoelectrics (TEG) and piezoelectrics (PIEZO) using a sharing inductor [START_REF] Bandyopadhyay | Platform architecture for solar, thermal, and vibration energy combining with mppt and single inductor[END_REF]. At a given time, only one harvester is connected to the inductor by selecting EN P V , EN (α H = 0, ∆ = 1). . . . 5.12 Adaptations of ED(1) using WVR-PM with α C = 0.9, α H = 0 and ∆ = 1.

The variation of T W I is signicantly reduced during T N EI as êActive (n + 1) is very close to êActive (n) when α C = 0.9. The variation is reduced down to 60% in average. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.13 N b_V ar and Sum_V ar with dierent values of α H (α C = 0.9, ∆ = 1). . 5.14 Adaptations of ED(1) using WVR-PM with α H = 0.7. . . . . . . . . . . . . 5.15 N b_V ar and Sum_V ar with dierent values of ∆ (α C = 0.9, α H = 0.7). Developments of wireless objects in our living spaces such as mobile phones, RFID tags, home appliances or monitoring cameras have opened a future internet, which is referred as Internet of Things (IoT) [START_REF] Atzori | The internet of things: A survey[END_REF]. Identied by a unique address, any wireless object is able to join the network and communicate with others as traditional computers through internet, based on standard communication protocols [START_REF] Mainetti | Evolution of wireless sensor networks towards the internet of things: A survey[END_REF]. IoT is opening a novel opportunity for the proliferation of Wireless Sensor Networks (WSNs), which can provide a wide range of monitoring applications such as monitoring spaces (environment, agriculture) or monitoring objects (human healthcare, smart buildings) [START_REF] Christin | Wireless sensor networks and the internet of things: selected challenges[END_REF].

A wireless sensor node, the basic object of WSNs, is usually comprised of a low-power sensing device, an embedded microcontroller, and a wireless transceiver [START_REF] Potdar | Wireless sensor networks: A survey[END_REF]. The embedded microcontroller is generally used for collecting and processing the data taken from the sensors. Sensor elements produce a measurable response to a change in the physical world such as temperature, humidity, pressure, moisture... The wireless transceiver provides a medium to transfer information extracted from the sensors to a base station or through intercommunications among many nodes. Recent advancements in microelectro-mechanical-systems (MEMS) technology, radio transceivers for wireless communications, and digital electronics have facilitated the development of low-cost, low power, multi-functional sensor nodes that are small in size and more and more ecient for processing and wireless communications [START_REF] Pasha | Toward ultra low-power hardware specialization of a wireless sensor network node[END_REF]. Due to the short distance of the radio frequency Chapter 1. Introduction 2 (RF) transceiver (e.g. 30m with CC2420, the transceiver from Texas Instruments), monitoring applications for remote places or large areas require a large number of randomly deployed wireless sensor nodes that construct a WSN [START_REF] Puccinelli | Wireless sensor networks: applications and challenges of ubiquitous sensing[END_REF]. Many types of sensors such as seismic, magnetic, thermal, visual, infrared, and acoustic provide various WSN applications ranging from home automation [START_REF] Chou | Energy-ecient platform designs for real-world wireless sensing applications[END_REF], parking guidance [START_REF] Idris | Parking guidance system utilizing wireless sensor network and ultrasonic sensor[END_REF], patient healthcare [START_REF] Rincon | Os-based sensor node platform and energy estimation model for health-care wireless sensor networks[END_REF], to military surveillance [START_REF] He | Vigilnet: An integrated sensor network system for energy-ecient surveillance[END_REF].

In the last decade, many platforms of wireless sensor nodes from academia to industry are proposed such as Crossbow Mica2 [START_REF] Hill | The platforms enabling wireless sensor networks[END_REF], BTnode [START_REF] Beutel | Next-generation prototyping of sensor networks[END_REF] or Intel Imote [START_REF] Nachman | The intel mote platform: A bluetooth-based sensor network for industrial monitoring[END_REF]. All of these platforms are low cost, small size and have especially, high energy eciency since energy consumption is the main constraint in WSNs. However, energy optimization of these platforms is performed mainly on hardware components. Therefore, the PowWow platform proposed in [START_REF] Berder | Powwow : Power optimized hardware/software framework for wireless motes[END_REF], which benets from both hardware, software and protocol optimizations, is used in this thesis. PowWow is based on a low-power microcontroller MSP430 and RF transceiver CC2420. The design of PowWow is exible and can easily integrate a harvesting circuit for additional energy. Moreover, the protocol based on RICER (Receiver Initiated CyclEd Receiver) for communications between wireless nodes has been optimized. The consumed energy of protocol stacks has also been characterized in terms of energy and execution time [START_REF] Alam | A hybrid model for accurate energy analysis of wsn nodes[END_REF]. The memory footprint is only 6 kbytes for the stack layers and less than 11 kbytes, including an application of temperature sensing and mobile node tracking [START_REF] Berder | Powwow : Power optimized hardware/software framework for wireless motes[END_REF]. These advantages make PowWow a suitable choice for validating our works in this thesis.

Energy Harvesting Wireless Sensor Networks

Although WSNs can be easily used for monitoring applications in some places where cables are dicult and costly to draw, battery maintenance becomes a burden if widely used (e.g. large factories, dangerous places). This issue opens two dierent approaches in extending the system lifetime of WSNs. In the rst one, a variety of methods and techniques to reduce power consumption, and therefore increase the system lifetime, has been proposed. Reducing RF energy consumption of the devices signicantly reduces the power consumption, so there are a number of novel hardware (e.g. wake-up radio Table 1.1: Power density of popular harvesting technologies [START_REF] Stojcev | Power management and energy harvesting techniques for wireless sensor nodes[END_REF].

Harvesting device

Power density PV-direct sun 15mW/cm 2 PV-cloudy day 0.15 mW/cm 2 PV-indoor 6 µW/cm 2 PV-desk lamp 0.57 mW/cm 2 Piezoelectric-shoe 330 µW/cm 2 Thermoelectric-100C gradient 40 µW/cm 2 Wind generator-15km/h [START_REF] Carli | A high-eciency wind-ow energy harvester using micro turbine[END_REF] 7.93mW

receivers [START_REF] Demirkol | Wake-up receivers for wireless sensor networks: benets and challenges[END_REF]), software (e.g. MAC and routing algorithm [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF]) and duty cycle optimization approaches in this eld [START_REF] Anastasi | Extending the lifetime of wireless sensor networks through adaptive sleep[END_REF] [START_REF] Karumbu | Optimum sleep-wake scheduling of sensors for quickest event detection in small extent wireless sensor networks[END_REF]. Although system lifetime is improved, it is still crippled by the limited energy in the batteries, which are used for the energy storage.

In a second approach, alternative energy sources have been integrated to supplement, or even replace batteries. Thanks to advancements in energy harvesting techniques, everlasting environmental energy can be extracted and brings a breakthrough to design completely autonomous Energy Harvesting (EH) WSNs. A wide range of harvesting devices, which are cheap, tiny, and high power density have been proposed and can be applied to EH-WSNs such as photovoltaics (PVs) for solar energy [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF], themoelectric for thermal energy [START_REF] Stordeur | Low power thermoelectric generator-self-sucient energy supply for micro systems[END_REF] or wind generator for airow energy [START_REF] Carli | A high-eciency wind-ow energy harvester using micro turbine[END_REF]. Table 1.1 summarizes their potential power density. It can be observed that solar energy extracted by PVs provides the highest power density. Moreover, PVs are small in size, inexpensive and easy to deploy compared to other harvesters. Therefore, PVs are the most widely used in EH-WSN platforms [START_REF] Weddell | A survey of multi-source energy harvesting systems[END_REF].

Although environmental energy can be scavenged for as long as desired, the amount of harvested energy has temporal variations due to the change of environmental conditions.

Moreover, dierent energy sources usually have dierent behaviors. While solar energy in outdoor and light energy in indoor are periodic and can be predicted in a short future, thermal and wind energy are more arbitrary and unpredictable. The EH-WSN node cannot work at a xed performance as it can be underowed (the node is shut down) when harvested energy is reduced and overowed (harvested energy is wasted) when harvested energy is increased. Therefore, a Power Manager (PM) is often embedded in the EH-WSN node to adapt its computation load according to the harvested energy.

Instead of minimizing the consumed energy [START_REF] Sinha | Dynamic power management in wireless sensor networks[END_REF] [26] or maximizing the system lifetime [START_REF] Younis | Energy-aware routing in cluster-based sensor networks[END_REF] [START_REF] Shah | Energy aware routing for low energy ad hoc sensor networks[END_REF] as in battery-powered WSN, the PM makes the harvesting node converge to Chapter 1. Introduction

Energy Neutral Operation (ENO) [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF], which means that consumed energy is equal to harvested energy for a long period. This strategy can provide a theoretically innite lifetime (until its hardware is outdated). The next section will present an overview of current PM techniques in EH-WSN.

Power Manager in Energy Harvesting WSNs

Power manager techniques which are applied in EH-WSNs are classied into three categories: dynamic power manager, dynamic voltage and frequency scaling, and duty cycle power manager. Among these techniques, duty cycle power manager is the most popular approach. Their basic principles are described in the following subsections.

Dynamic power manager

Dynamic Power Manager (DPM) techniques are well studied and practiced in embedded systems. A fundamental principle of DPM techniques is that, all components composed a system are not required to work at their full performance [START_REF] Benini | System-level power estimation and optimization[END_REF]. Because most systems are designed to perform multiple functions (e.g. sensing, processing, and communicating in WSNs), many components are not required to be activated at a given time. As a consequence, these components can be turned into low-power states such as idle or sleep by the DPM [START_REF] Srivastava | Predictive system shutdown and other architectural techniques for energy ecient programmable computation[END_REF][32] for energy saving. DPM does not have a xed shape and can be implemented in either software or hardware [START_REF] Weng | A survey of dynamic power optimization techniques[END_REF]. In software, DPM can be implemented on the Operating System (OS) or directly at the application level. In hardware, DPM can be applied to a circuit module or a chip [START_REF] Benini | System-level power estimation and optimization[END_REF]. However, the main drawback of DPM techniques is that once in a low-power state, bringing a component back to the active state requires additional energy and especially, the latency to serve incoming tasks. In addition, simply shutting o components as soon as they are not used might waste energy.

It is due to the fact that it takes time to recover system states and sometimes the saved energy is less than the consumed energy during state transitions [START_REF] Benini | System-level power estimation and optimization[END_REF].

In two dierent surveys of DPM techniques presented in [START_REF] Weng | A survey of dynamic power optimization techniques[END_REF] [START_REF] Benini | A survey of design techniques for system-level dynamic power management[END_REF], authors classify DPM into two categories: predictive schemes and stochastic schemes. Instead of waiting for a xed time period as in timeout schemes, predictive schemes attempt to predict the timing of future input events of the system and schedule shutdown (usually to a single power ecient state) based on these predictions. However, predictive approaches suer wrong predictions as shown in Fig. 1.1. The system can be turned into a low-power state too late or wake-up too early, that causes the waste of energy. Therefore, it is necessary to improve the average error of prediction methods and reduce the delay overhead [31][35] in predictive schemes.

On the other hand, stochastic schemes, rather than eliminating the uncertainty of predictions, model the system and the workload as Markov chains [36][34]. In these approaches, the energy consumption and transition times between many states (e.g. active, idle, sleep) of the system and other complex components (e.g. buers, queues) are modeled as an optimization problem. Then, a trade-o between energy consumption and performance of the system is determined [START_REF] Benini | A survey of design techniques for system-level dynamic power management[END_REF]. Stochastic schemes are well-researched category of DPM techniques [37][38][39] due to their several advantages compared to predictive schemes. The most advantage is that, they present global optimizations as dierent states and resources of a system are considered in Markov models. In addition, they provide a solution (in polynomial time) for the performance-constrained energy optimization problem. Finally, the strength and optimality of randomized policies can be exploited in stochastic schemes [34][40]. However, the limitation of stochastic techniques is an assumption of the prior knowledge of the system and its workload. In reality, workloads are often non-stationary and depend on real-time activities of a system.

Dynamic Voltage and Frequency Scaling (DVFS)

Dynamic Voltage and Frequency Scaling (DVFS) technique is another technique which is aimed at changing the system energy consumption prole. This technique exploits the variations in the workload for dynamically adjusting the voltage and frequency of processors in order to reduce power and energy consumption. These techniques have a potential for reducing the energy consumption. The challenge, however, is to preserve the feasibility of schedule and provide deadline guarantees. These techniques are of particular eectiveness and interest because energy consumption of the processor is quadratically related to the supply voltage [START_REF] Pillai | Real-time dynamic voltage scaling for low-power embedded operating systems[END_REF] [START_REF] Chandrakasan | Low-power cmos digital design[END_REF]. Another advantage of these techniques is that, when the energy availability is low, they allow the system to keep running at a low speed with reduced energy consumption, without compromising the execution of important tasks.

DVFS techniques save energy consumption by using idle time of sensor nodes. Fig.

illustrates a simple example of how a DVFS technique saves energy consumption

under the control of a scheduler. There are two tasks T1 and T2 where deadlines are 5 and 7, respectively. Taking into account the idle time for each task, DVFS schemes might reduce the system voltage, and therefore, prolong the system lifetime. They have been demonstrated to obtain signicant energy savings in time-constrained systems [START_REF] Pillai | Real-time dynamic voltage scaling for low-power embedded operating systems[END_REF] [43] [START_REF] Zhuo | Energy-ecient dynamic task scheduling algorithms for dvs systems[END_REF]. However, these algorithms do not perform well without considering the WSN workloads. Researches in [START_REF] Tuming | A dynamic voltage scaling algorithm for wireless sensor networks[END_REF] proposed a task-driven feedback algorithm according to the workloads of a sensor node and x the errors through the PID control model. The improved algorithm could save 30% energy consumption compared to previous DVFS techniques.

In [START_REF] Hamachiyo | A cooperative power-saving technique using dvs and dms based on load prediction in sensor networks[END_REF], authors apply a cooperative technique using DVFS and Dynamic Modulation Scaling (DMS) to sensor nodes for minimizing energy consumption. DMS saves energy by varying the constellation size (the number of bits per symbol) in transmission [START_REF] Schurgers | Modulation scaling for energy aware communication systems[END_REF].

Similar to DVFS, DMS reduces energy consumption in exchange of the communication speed. Employing both DVFS and DMS can bring even more reserved energy compared to independently used. This cooperative architecture is based on the predicted workload of the processor and the transceiver. Prediction algorithm focuses on the periodic operations of sensor nodes such as acquiring temperature and humidity every minute.

Sleep Active

Communications Wake-up interval (T WI )

Time

T WI is reduced due to higher harvested energy Therefore, the behavior of threads and communications on the sensor nodes also has periodic characteristics. The system predicts the workload of the processor and transceiver based on the stored log data. After that, it determines the appropriate processor voltage (or clock frequency) and constellation size based on prediction results. It can be observed that the cooperative architecture is able to achieve up to 40% energy reduction when compared to non energy saving systems.

Duty Cycle Power Manager

This approach directly controls the MAC protocol, which is a source of waste for the radio bearing the channel as transmitting packets [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF]. MAC protocols are specically designed to turn WSN nodes alternately between active and sleeping periods. During the active period, nodes are able to transmit or receive data, but during the sleeping period, nodes are completely turned o for energy saving. Therefore, the global consumed energy of the node can be adapted by a power manager by changing the sleeping period (or wake-up interval) of MAC protocols as shown in Fig. 1.3. Dierent MAC protocols for WSNs can be found in [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF]. However, we focus on the specic family of duty cycle protocols that can be used by a power manager in EH-WSNs. These duty cycle MAC protocols can be roughly categorized into two categories [START_REF] Sun | Ri-mac: a receiver-initiated asynchronous duty cycle mac protocol for dynamic trac loads in wireless sensor networks[END_REF]: synchronous and asynchronous protocols.

Synchronous approaches such as S-MAC [START_REF] Ye | An energy-ecient mac protocol for wireless sensor networks[END_REF], T-MAC [START_REF] Dam | An adaptive energy-ecient mac protocol for wireless sensor networks[END_REF], R-MAC [START_REF] Du | Rmac: A routing-enhanced duty-cycle mac protocol for wireless sensor networks[END_REF] and DW-MAC [START_REF] Sun | Dw-mac: a low latency, energy ecient demand-wakeup mac protocol for wireless sensor networks[END_REF] synchronize neighboring nodes in order to align their active or sleeping periods.

Neighbor nodes start exchanging packets only within the common active time, enabling a node to sleep for most of the time within an operational cycle without missing any incoming packet. Generally, these approaches aim to reduce the global consumed energy by reducing the idle listening. However, the required synchronization introduces extra overhead and high complexity. Moreover, a node has to wake up multiple times if its neighbors are on dierent periods. These disadvantages limit the use of synchronous MAC protocols in EH-WSNs.

Existing asynchronous approaches such as B-MAC [START_REF] Polastre | Versatile low power media access for wireless sensor networks[END_REF], X-MAC [START_REF] Buettner | X-mac: a short preamble mac protocol for duty-cycled wireless sensor networks[END_REF], TICER [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF], RICER [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF] and WiseMAC [START_REF] El-Hoiydi | Wisemac: An ultra low power mac protocol for multi-hop wireless sensor networks[END_REF], on the other hand, allow nodes to operate independently, with their own wake-up interval. Such protocols typically employ a low-power listening, in which, prior to data transmission, a sender transmits a preamble lasting at least as long as the sleep period of the receiver. When the receiver wakes up and detects the preamble, it stays awake to receive the data. These protocols achieve high energy eciency and remove the synchronization overhead required in synchronous approaches. Moreover, they are mainly optimized for low trac [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF] and therefore, suitable for EH-WSNs which usually have low data rate [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF].

Compared to other approaches, duty cycle power managers have two main advantages when applied in EH-WSNs. Firstly, they do not require complex hardware as DVFS, which have to support multiple system voltage and frequency levels. Secondly, duty cycle power managers directly control the MAC protocol, which is generally responsible for the main consumed energy in EH-WSNs, instead of optimizing at higher layers as DPM. Therefore, an ecient duty cycle power manager can reduce the global consumed energy to increase the QoS while following the ENO condition. These advantages make duty cycle adaptation the most popular technique for PMs embedded in EH-WSNs [START_REF] Kansal | Harvesting aware power management for sensor networks[END_REF][59][60] [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF].

In this thesis, a set of novel duty cycle PMs for EH-WSNs are presented. PMs adapt the average consumed energy of the node by changing its wake-up interval. Our PMs oer the following advantages compared to state-of-the-art PMs:

As there are many attentions on EH-WSNs using solar energy, our PMs are rather more generic as they can be easily applied to dierent kinds of energy sources.

The ENO condition over a long period must be satised for long-term operations in EH-WSNs. To achieve this, the ENO is periodically considered every period, depending on the behaviors of harvesting energy sources. It can be a few minutes for continuous energy sources where harvested energy is available most of the time (e.g. light energy in a hospital, thermal energy from an industrial machine) or up to 24 hours for periodic energy sources where harvested energy can be scavenged a certain period (e.g. solar energy in the outdoor environment, light energy in an oce).

Our PMs maximize the QoS while satisfying ENO condition. This is required in most of monitoring applications to increase the system performance in terms of data throughput and latency.

Finally, our PMs are low complexity, have small memory footprint, and can be implemented on a real EH-WSN node.

Following these features, the thesis outline and contributions are presented in the next section.

Thesis Outline and Contributions

Chapter 2: Energy Neutral Design Framework for Autonomous Wireless Sensor Network.

In this chapter, principles of potential harvesting energy sources for WSN are rstly presented, followed by a brief introduction of state-of-the-art platforms. Our ecient energy harvesting system, named MESC (Multiple Energy Sources Converter), which is compatible with various environmental sources such as light, heat, or wind energy is proposed. Our platform takes advantage of double level capacitors not only to prolong the system lifetime, but also to enable robust booting from the exhausting energy of the system. Moreover, a precise system design model, providing a methodology to optimize the size of storage devices and the quality of service of a WSN node according to the consumed and the harvested energy is also proposed. Energy monitor for supercapacitor-based EH-WSNs.

Energy predictor using the adaptive lter.

Chapter 4: Duty Cycle Power Manager for Continuous Energy Sources.

In this chapter, a set of power managers are proposed for WSN applications powered by continuous energy sources such as light energy in hospitals or heat energy from industrial machines. As energy can be scavenged most of the time without interruption, the EH-WSN node can consume energy equal to its harvested energy during a dened time slot.

Based on this principle, two dierent approaches are proposed in this chapter. shows an interest of our approach as monitoring harvested as well as consumed energy is not required. However, PID-PM suers from a trial-and-error process before converging to ENO, which is mandatory in any adaptive controller. Therefore, the second approach is proposed based on the energy monitor proposed in Chapter 3. Not only based on the current voltage, estimations of harvested and consumed energy are also considered to determine the next wake-up interval. As a consequence, the energy monitor-based power manager (EM-PM) brings faster convergence to ENO than PID-PM. Main contributions described in this chapter are:

PID-PM: Power manager with a PID controller.

EM-PM: Power manager with the energy monitor.

Both PID-PM and EM-PM are low power, low complexity, energy sources independent, and can be easily implemented on a real wireless node with dierent harvesters. Another issue addressed in this chapter, especially when considering a multi-hop EH-WSN is the eect of wake-up interval variations to the global QoS. Due to its low harvested energy, a relay node is impractical to synchronize with a transmitter if its wake-up interval regularly changes and therefore, degrading the global QoS. Therefore, a new power manager, named Wake-up Variation Reduction Power Manager (WVR-PM) is proposed. As the wake-up interval does not change for a long period, the synchronization among EH-WSN nodes is maintained during this period, and therefore, guaranteeing global QoS. Moreover, a new Synchronized Wake-up Interval MAC protocol (SyWiM) is also proposed to take advantage of the fact that the wake-up interval of the node is rarely changed. Based on this information, the transmitter can wake-up at the right time to communicate with the receiver. The main contributions proposed in this chapter are:

A power manager for periodic energy source (PEO-PM).

A wake-up variation reduction power manager (WVR-PM).

A synchronized wake-up interval MAC protocol (SyWiM).

Finally, conclusions and perspectives are given at the last chapter of this thesis (Chapter 6: Conclusions and Perspectives).

Chapter 2

Energy Neutral Design Framework for Autonomous Wireless Sensor Network

Energy Sources for Wireless Sensor Network

In this section, an overview of potential energy sources for WSN is presented. According to our knowledge, popular sources of ambient energy considered suitable for WSN are solar, vibration, wind and thermal energy. In the following subsections, basic understanding of conversion principles and a brief survey of research eorts in these areas are described.

Solar Energy

Solar energy is directly extracted by the energy radiated from the sun or uorescent lights in an oce. Technologies to produce solar energy are widely classied as passive and active solar depending on the way they capture, convert and distribute solar energy.

Active solar techniques include the use of photovoltaic panels and solar thermal collectors to harness the energy. Passive solar techniques include orienting a building to the sun, selecting materials with favorable thermal mass or light dispersing properties, and designing spaces that naturally circulate air.

Chapter 2. Energy neutral design framework for autonomous WSN [START_REF] Didioui | Harvwsnet: A co-simulation framework for energy harvesting wireless sensor networks[END_REF]. MPPT mechanism is designed to provide the highest power from PVs.

In the context of small devices as WSN nodes, solar energy with photovoltaic cells (PVs)

is the most popular method for supplying additional energy. It has been shown that among energy sources, solar provide the highest power density [START_REF] Roundy | Power sources for wireless sensor networks[END_REF]. Moreover, PVs are small in size, cheap and easy to implement when compared to other strategies. Another advantage of solar energy is that the available energy can be predicted at a given time [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF]. This feature helps the PM achieve ENO in a more ecient way with a low error energy predictor. A typical solar powered WSN platform is Heliomote where PVs are directly connected to rechargeable batteries used for the storage device [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF]. This design is simple but inecient as the energy extracted from PVs is much less than its maximum value.

To increase the energy conversion eciency, a low-power Maximum Power Point Tracking (MPPT) has been proposed. This technique aims to automatically nd the voltage V M P P or current I M P P at which PVs provide the maximum output power P M P P even in nonoptimal weather conditions as illustrated in Fig. 2.1. A small photosensor is used in AmbiMax platform to estimate the light condition and force the PVs working around its MPP [START_REF] Park | Ambimax: Autonomous energy harvesting platform for multi-supply wireless sensor nodes[END_REF]. Other MPPT techniques specially designed for PVs are discussed in [START_REF] Esram | Comparison of photovoltaic array maximum power point tracking techniques[END_REF].

Chapter 2. Energy neutral design framework for autonomous WSN 15

Thermal Energy

In this kind of energy, the dierence of temperature between two surfaces of metals or semiconductors causes a voltage. Thermal energy can be extracted by using a thermogenerator (TEG) [START_REF] Mateu | Energy harvesting for wireless communication systems using thermogenerators[END_REF]. The hot surface of the TEG is attached to a heat source while the cold surface is commonly chosen to provide the heat spreading eect towards a heat dictable and uncontrollable, monitoring applications based on thermal-powered WSN usually only require tracking data whenever the harvested energy from TEG is available.

One example involves the health monitoring of industrial engines, which can exploit excess or wasted heat when they are running. Another example concerns the monitoring of patient health in a hospital with the heat from body skin.

Wind Energy

The wind-powered WSN is limited to special applications due to the size of the turbine, which is used to harvest wind energy. Although it is less attractive compared to solar or thermal-based WSN, some eorts have been conducted to generate wind power at a very small scale recently. As shown in [START_REF] Carli | A high-eciency wind-ow energy harvester using micro turbine[END_REF], a micro turbine with high eciency conversion is quite promising. Its overall volume is below 300cm 3 and suitable for powering a small WSN node in an outdoor environment. This turbine can generate up to 10mW within a wind speed of 16km/h. In order to maximize the harvested energy from the wind turbine over a wide range of operating conditions, a MPPT is also applied [START_REF] Park | Ambimax: Autonomous energy harvesting platform for multi-supply wireless sensor nodes[END_REF]. Moreover, an eective power-saving architecture has been proposed for the control circuit to turn the whole harvester o during the periods of wind absence. An experimental setup for wind energy can be found in Fig. 2.3. A low-power diode bridge is used to convert the AC voltage at the output of the turbine into a DC voltage for powering the WSN node.

AC/DC converter

Other Energy Sources

Other energy sources, which seem to have great potential, such as vibration and fuel energy are discussed in this section. Vibration energy available for energy harvesting wireless sensor node can be found in buildings or human motions. There are three kinds of converters to extract vibration energy: electromagnetic, electrostatic and piezoelectric converters [START_REF] Roundy | A study of low level vibrations as a power source for wireless sensor nodes[END_REF]. In the rst case, the relative motion of a coil in a magnetic eld causes an output voltage. Electrostatic converters consist of two capacitor plates and a voltage is created when these plates are moved. Finally, piezoelectric converters exploit the ability of some materials like crystals or ceramics to generate a voltage when there is a mechanical stress. Simulations and measurements in [START_REF] Roundy | A study of low level vibrations as a power source for wireless sensor nodes[END_REF][67] [START_REF] Meninger | Vibration-to-electric energy conversion[END_REF] show that piezoelectric converters provide the highest power density, up to 200µW/cm 3 [START_REF] Roundy | Power sources for wireless sensor networks[END_REF]. Therefore, many vibration-based WSNs are using piezoelectric converters for harvesting energy. A WSN node proposed in [START_REF] Vijayaraghavan | Active control based energy harvesting for battery-lesswireless trac sensors: Theory and experiments[END_REF] can also be solely powered by the short duration vibrations when a vehicle passes over can be used for trac monitoring applications. A vibration-based platform using a piezoelectric push-button in [START_REF] Tan | Energy harvesting using piezoelectric igniter for self-powered radio frequency (rf) wireless sensors[END_REF] shows that vibration energy when the button is depressed is sucient to transmit a 24-bit frame. These results open new opportunities for autonomous WSNs using vibration energy.

Energy from fuel cells is useful when the energy storage devices (rechargeable batteries or supercapacitors) are going to deeply discharge and the ambient energy is not available. The WSN node then, activates the fuel cell interface in order to rapidly recharge its storage and avoid the system shut down. Fuel cells are rechargeable electrochemical energy conversion devices where electricity and heat are produced as long as hydrogen is supplied to react with oxygen [START_REF] Chraim | Fuel cell applications in wireless sensor networks[END_REF]. The advantage of fuel cells is both energy storage and power delivery is much higher than traditional storage devices (batteries and supercapacitors). However, they require hydrogen that complicates their deployment.

The WSN using fuel cells is therefore, only available in the research environment. Some typical platforms in this eld can be found in [START_REF] Chraim | Fuel cell applications in wireless sensor networks[END_REF] and [START_REF] Magno | Smart power unit with ultra low power radio trigger capabilities for wireless sensor networks[END_REF].

Hardware Design Challenges for EH-WSNs

Environmental energy can be converted from a wide range of harvesters such as photovoltaic cells (PVs) [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF], thermal generators (TEGs) [START_REF] Stordeur | Low power thermoelectric generator-self-sucient energy supply for micro systems[END_REF] or wind turbines [START_REF] Carli | A high-eciency wind-ow energy harvester using micro turbine[END_REF]. However, energy harvesting (EH) WSNs need to cope with dierent shapes of the energy extracted from harvesters. While indoor/outdoor PVs provide high voltage but low current, TEGs output low voltage but high current. Meanwhile, wind turbines produce an AC output which needs to be adapted.

Another challenge in EH-WSNs is how to rapidly power on the system from its empty energy state. When the system must boot from exhausted energy, it has to wait for a sucient voltage of the storage device to power on electronic devices. Moreover, the buered energy must be enough for booting process at this voltage. For a standalone storage device, if the capacity is big, it takes a long time to charge the storage device to its powered voltage level. In contrast, if the capacity is small, the charging time is reduced, but it cannot store enough energy for a long period without harvesting. As a consequence, a better design is the use of double layers for the storage device. The rst one, with small capacity, directly powers the whole system. The second one, with bigger capacity, is connected to the rst one and allowed to be charged as soon as the rst storage device reaches the regulated voltage. This structure benets from a short charging time for the small capacitor and is able to store a lot of energy in the second one [START_REF] Chen | Duracap: A supercapacitor-based, powerbootstrapping, maximum power point tracking energy-harvesting system[END_REF].

Moreover, the operational lifetime of the energy storage device is an important feature when designing a long-term energy harvesting platform. Rechargeable batteries which are compatible with battery powered WSNs can be easily used as energy storage devices. However, the limited recharge cycles of batteries (500 cycles for a Lithium battery [START_REF] Simjee | Ecient charging of supercapacitors for extended lifetime of wireless sensor nodes[END_REF]) only provides around two years lifetime for a daily recharge system. Moreover, a battery-based WSN requires an additional charging control circuit to avoid overow or underow, increasing the hardware footprints. Supercapacitors which have more than half a million recharge cycles and over 10-year operational lifetime [START_REF] Simjee | Ecient charging of supercapacitors for extended lifetime of wireless sensor nodes[END_REF] are widely used in energy harvesting WSNs [74][75]. Unfortunately, the leakage energy of a supercapacitor is higher than a battery [START_REF] Zhu | Leakage-aware energy synchronization for wireless sensor networks[END_REF] and therefore, needs to be properly considered in the design of a supercapacitor-based EH-WSN.

Instead of minimizing the consumed energy as in case of battery-powered system, a selfpowered WSN node with ambient energy adapts the consumed energy by changing its quality of service (QoS) according to the available harvested energy when it is deployed.

For an autonomous node, it is requisite to ensure that the consumed energy is equal to the harvested energy over a long period. This leads to Energy Neutral Operation (ENO) [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF] with a theoretically innite system lifetime. The size of the storage devices needs to be meticulously considered to satisfy ENO condition when designing an ecient EH-WSN node. As the harvested energy is not always available, it has to be buered in the storage device during harvesting interval and is used during non-harvesting interval. If there is not sucient space, a part of harvested energy is discarded and degrades the average QoS. However, increasing the size of supercapacitor-based storage may not improve the QoS as the total consumed energy is also increased due to the leakage energy.

In Finally, this chapter ends with conclusions.

State-of-the-Art WSN Platforms

In recent years, a large number of energy harvesting wireless sensor platforms ranging from academia to industry have been proposed. The independence of recharging or replacing batteries signicantly increases the autonomy of the WSN nodes. This advantage makes energy harvesting WSN widely used in remote places where cables are becoming impractical and costly to draw such as volcano monitoring [START_REF] Allen | Fidelity and yield in a volcano monitoring sensor network[END_REF] or glacial movement monitoring [START_REF] Martinez | Glacsweb: a sensor network for hostile environments[END_REF]. In this section, an overview of existing platforms, which are classied into two categories, named single-path and dual-path architectures, is presented.

Single-Path Architecture

In this traditional architecture, there is only a main energy storage, which can be a battery, a supercapacitor or both of them. All energy from harvesters is used to charge the energy storage for powering the WSN node through a DC/DC converter. The energy ow is depicted in Fig. 2 storage is usually charged to a certain voltage before deploying the nodes. Some typical platforms belonging to this category are listed in the following.

Heliomote [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF] is one of the rst solar energy harvesting WSN systems. Two small PVs are directly connected to rechargeable batteries through a protection diode. This solution is simple, but inecient as the harvested energy is reserved only when the output voltage from PVs is 0.7V higher than the battery voltage due to the diode drop. Moreover, batteries have limited recharge cycles [START_REF] Simjee | Ecient charging of supercapacitors for extended lifetime of wireless sensor nodes[END_REF] so that the system lifetime is reduced to less than two years for a daily recharge. Although Heliomote is specically designed for solar energy, a MPPT circuit is not included. To increase the conversion eciency, two NiMH batteries are used for the energy storage whose voltage varies between 2.2V and 2.8V.

Therefore, with a diode used to prevent reverse current ow into the solar panel, the output voltage of the PVs remains close to the optimal point (from 2.9V to 3.5V). A system design model presented in [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF] provides a framework to estimate the battery size as well as the average QoS of a WSN node in the ENO mode. However, this model is only applied for a periodic solar energy source.

Prometheus [START_REF] Jiang | Perpetual environmentally powered sensor networks[END_REF] has a similar design to Heliomote but a hybrid energy storage. It is a combination of a supercapacitor and a rechargeable battery to overcome the limited system lifetime of Heliomote [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF]. When the supercapacitor is fully charged, the surplus energy will charge the battery. Otherwise, when the supercapacitor voltage is below a predened threshold, the demanded energy is driven from the battery. By this way, the energy consumed by the WSN node is mostly served by the supercapacitor and access to the battery is reduced. This solution takes advantage of more than half a million recharge cycles of a supercapacitor [START_REF] Simjee | Ecient charging of supercapacitors for extended lifetime of wireless sensor nodes[END_REF]. Therefore, the battery lifetime can be extended up to four years under an average of 10% load. Unfortunately, supercapacitors have higher leakage current compared to rechargeable batteries. Moreover, the larger the capacity is, the greater the leakage current is. Therefore, an energy model is proposed in [START_REF] Jiang | Perpetual environmentally powered sensor networks[END_REF] to provide a trade-o between the size of the supercapacitor, the leakage energy and the average consumed energy of a WSN node. However, this energy model aims to maximize the system life time, but does not satisfy the ENO condition. Prometheus also lacks a MPPT circuit and requires a high start-up voltage, which is similar to Heliomote.

Everlast [START_REF] Simjee | Ecient charging of supercapacitors for extended lifetime of wireless sensor nodes[END_REF] is an EH-WSN platform which only uses a supercapacitor as its energy storage. By removing the battery, the system lifetime is extremely increased. Authors claimed that Everlast can operate for an estimated lifetime of 20 years without any maintenance. Moreover, a MPPT is implemented by software to improve the output power from PVs. The MPPT algorithm is implemented on the microcontroller with an I-V tracer. This accurate MPPT increases the conversion eciency of PVs up to 89%.

Another improvement in this work is the use of a pulse-frequency modulated (PFM) to transfer the energy from PVs instead of directly connecting them to the energy storage.

Since the supercapacitor is seen as a short circuit to the PVs when they are connected together, the voltage of PVs is quickly fallen to the voltage of the supercapacitor which is usually far from the optimal operating point for PVs. By giving a series of pulse, the switch in PFM regulator keeps voltage of PVs around the MPP. This solution enables charging the supercapacitor up to 400% faster than direct charging as Heliomote or Prometheus. Unfortunately, Everlast requires a high harvested power in order of W due to the high speed operations of its MPPT, and is therefore, not a suitable choice for indoor harvesting systems where harvested power is only in order of mW. Another lack in this work is a design framework for meticulously sizing the supercapacitor for the energy storage.

Dual-Path Architecture

In a dual-path architecture, the fundamental dierence compared to single-path architecture is the use of an energy ow controller. A primary and a secondary storage (PS and SS) are used to buer harvested energy instead of only one storage as in case of single-path architecture. The basic blocks of the dual-path architecture are presented in Fig. 2.5. When environmental energy is available, all harvested energy will charge the PS for powering the WSN node. As soon as the PS is fully charged, surplus energy is driven into the SS. Otherwise, when the environment energy is insucient, the remaining energy is taken from the SS to the PS for ensuring continuous operations of the WSN node. The advantage of this architecture is a fast booting from both empty PS and SS. Due to the small capacity of the PS, it is quickly charged to a minimum voltage sucient to enable the voltage regulator and then, the WSN node can be activated. Moreover, SS has a big capacity to provide long-term operations during the period of energy absence.

DuraCap [START_REF] Chen | Duracap: A supercapacitor-based, powerbootstrapping, maximum power point tracking energy-harvesting system[END_REF] is an example of a supercapacitor-based energy storage using dual-path architecture. This platform addresses two problems in solar-powered WSN including the booting process and MPPT with dierent PVs. Harvested energy is rstly charged in a small capacitor for booting the WSN node before fullling an array of bigger supercapacitors. A PFM regulator and an I-V tracer similar to Prometheus are implemented to perform MPPT function. Another improvement in this platform is a bound-control circuit using a low-power comparator to generate the control signals for the PFM regulator.

The MCU is not required to be active all the time for performing MPPT in software as in case of Prometheus, and therefore, reduces the global consumed energy. When a new PV is plugged-in, the MCU temporarily disconnects it from the system to track the I-V curve. As soon as the MPP is determined, the MCU only needs to send two non-volatile values to set the upper and lower-bound for the comparator in the bound-control circuit. Then, the PFM regulator enables the new conguration to accomplish the MPPT function. This structure is extended in EscaCap [START_REF] Kim | Energy harvesting by sweeping voltage-escalated charging of a recongurable supercapacitor array[END_REF] with a dynamic conguration for the SS. The array of supercapacitors can be connected in series or parallel by means of a switch array. Experimental results show that EscaCap eciently reduces the leakage energy and improves the charging speed.

As energy harvesting for single source, especially for solar, has been well investigated, recent works deal with the combination of several sources. When considering stationary outdoor applications, the most popular sources are solar and wind energy due to their wide availability and high power. Moreover, there seems to be a mutual complementarity between these sources: strong winds usually occur when the weather is bad rather than in sunny days, or during the night-time where solar energy is not available [START_REF] Liu | An ecient wind-photovoltaic hybrid generation system using doubly excited permanent-magnet brushless machine[END_REF]. These reasons make solar and wind energy be widely chosen in a multi-source platform such as Ambimax [START_REF] Park | Ambimax: Autonomous energy harvesting platform for multi-supply wireless sensor nodes[END_REF] and Capnet [START_REF] Ferry | Power and energy aware design of an autonomous wireless sensor node[END_REF]. However, with only a single energy storage, they suer from booting time from an exhausted energy as explained in Section 2.3.1. Combined with the dual-path architecture, a multi-source, multi-storage EH-WSN platform is proposed in [START_REF] Carli | An eective multi-source energy harvester for low power applications[END_REF]. A supercapacitor and a rechargeable battery are used for the PS and SS, respectively. The power management circuit in this work acts as the energy ow controller illustrated in Fig. 2.5. Moreover, to increase the lifetime of the battery, the charge/discharge control circuit is designed to provide overcharge and undercharge protection.

To reduce the hardware cost due to the number of external o-chip components when multiple energy sources are used, an inductor sharing-based architecture is proposed in [START_REF] Bandyopadhyay | Platform architecture for solar, thermal, and vibration energy combining with mppt and single inductor[END_REF]. Three dierent harvesters are supported on this platform: PV, TEG and piezoelectric (PIEZO) for vibration energy. Each harvester is equipped with a power converter to normalize the output energy. Since all of these converters are designed to operate in the discontinuous conduction mode (DCM), there is only one harvester connected to the inductor at a given time instead of the combination of all input energy sources as in [START_REF] Carli | An eective multi-source energy harvester for low power applications[END_REF]. In this design, which is illustrated in Fig. 2.6, a switch matrix has been used that can be recongured into all of the power converters. When a converter is disconnected from the inductor, harvested energy still keeps charging its temporary capacitor until its schedule (C P V , C T EG and C P IEZO ). The energy in this capacitor is then, immediately transferred through the inductor to the energy storage, including both primary storage (C P S ) and secondary storage (C SS ). Therefore, there is no wasted energy during the idle period of each converter.

Following this trend, we propose a Multiple Energy Sources Converter (MESC). Our platform is simple, exible and easy to implement with commercial o-the-shelf (COTS) components. For higher eciency, a dual-path architecture with two separate capacitors for the energy storage are applied in MESC. This structure not only provides a long system lifetime, but also supports fast booting capability. However, the MPPT technique is not used in our platform because it is directly linked to a particular harvester. While

PVs for solar energy have the MPP from 70% to 80% [START_REF] Esram | Comparison of photovoltaic array maximum power point tracking techniques[END_REF], TEGs for thermal energy have the MPP around 50% [START_REF] Bandyopadhyay | Platform architecture for solar, thermal, and vibration energy combining with mppt and single inductor[END_REF] of the open circuit. In this work, we focus on the system design model, providing a methodology to optimize the size of the supercapacitor-based energy storage in order to satisfy ENO condition and maximize the system QoS. Both periodic and continuous energy sources are considered in our design model.

Chapter 2. Energy neutral design framework for autonomous WSN

25 EN V O V S V THR V THF Power on V Re Init RF StoreCap is charged StoreCap charges OutCap Power off V Max V Re V S = V O V RF

Booting delay Communication

Energy harvesting interval Non-energy harvesting interval During the harvesting energy interval, the WSN node is kept shut down until V O reaches V T HR . During the non-harvesting energy interval, the WSN node is turned o when V O decreases below V T HF .

System Architecture and Operation Principles

The hardware architecture of MESC is shown in Fig. 2.7. Energy adapters are added to normalize the shape of output energy from multiple harvesters. For instance, PVs usually have the high open circuit voltage (e.g. 5V with two PVs in series [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF]) to simplify the energy converter with a resistor to limit the output current. Meanwhile, TEGs for heat energy bring a very low output voltage (e.g. 20mV [START_REF] Carlson | A 20 mv input boost converter with ecient digital control for thermoelectric energy harvesting[END_REF]). In this case, a step-up transformer is added to amplify this output voltage. A combination of a diode-bridge and a step-up transformer can be used for a wind generator due to its low AC-voltage output. Energy adapters should be low-powered devices to minimize power losses. For instance, Schottky diodes (BAT47) for the diode bridge [START_REF] Carli | A high-eciency wind-ow energy harvester using micro turbine[END_REF] the beginning, the system is powered o and storage devices are empty. All energy from harvesting devices will charge the OutCap. When V O reaches the rising threshold (V T HR ), the available energy in OutCap is sucient for booting the system. Therefore, a comparator brings the enable signal EN from low to high for powering the WSN node.

However, the available energy in the OutCap at V T HR is only enough for booting the system and initializing some basic modules of the microcontroller (MCU) such as I/O, Timer and ADC. Therefore, the MCU runs into sleep mode, periodically monitors V O using a low ADC, and then initializes the radio chip as soon as

V O reaches V RF .
The energy ow controller keeps on charging OutCap to its regulated voltage (V Re ).

After V O has reached regulation, StoreCap is allowed to be charged. However, when V S is charged to its maximum voltage (V M ax ), there is no space for the harvested energy.

Therefore, the WSN node should utilize all harvested energy to increase the QoS and also avoid wasted energy. When there is no more energy from harvesters (non-harvesting energy interval in Fig. 2.8), V S charges V O to V Re as long as V S is greater than V Re .

However, when V S is less than V Re , V S and V O decrease together and the system is powered o when V O is under the falling threshold V T HF .

Leakage Energy and DC/DC Converter Eciency

The electrical characteristics of MESC including the leakage energy and the DC/DC converter eciency are estimated from experimental measurements. A CapXX supercapacitor of 0.9F [85] is used for StoreCap (C S ). At the beginning of the measurement, V S is 4.925V and the WSN node is disconnected from MESC. Due to the leakage energy,

V S is linearly reduced to 4.825V after 10000s. Therefore, the total leakage energy for a period T can be estimated by

E Leak = P Leak T, (2.1)
where P Leak is the leakage power dened as follows: This leakage power comes mainly from the 0.9F supercapacitor used for StoreCap. By characterizing this StoreCap alone, its leakage power is estimated around 30µW. Therefore, the total leakage power of remaining components in MESC including OutCap, the energy ow controller and the comparator is approximated to 13µW.

P Leak = 1 2 (0.9F ) (4.925V ) 2 -(4.825V ) 2 (10000s) = 43(µW ) (2.
The DC/DC converter is designed to provide stable output voltage to the WSN node.

Unfortunately, its eciency not only depends on the input voltage but also on the output current. For instance, the TPS61030 component used in this work, has eciency in range of [80 -93]%. It is impractical to achieve this eciency precisely as the input voltage from OutCap usually changes due to environmental conditions and the output current depends on activities of the WSN node (transmission, reception or sensing). Therefore, the average DC/DC converter eciency (η) has been characterized in our design model.

Two dierent scenarios with the same PowWow WSN node [86] are setup to evaluate η. In the rst scenario, the power supply of the node (V dd ) is directly connected to V O without using the DC/DC converter. In the second one, V dd is connected to the output of the DC/DC converter. The average consumed energy for both scenarios is 830µJ and 971µJ, respectively. As a consequence, the average converter eciency can be approximated to η = 0.85.

An overview of the hardware design of MESC can be found in Fig. low-powered battery monitor IC in [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF] is not required in our platform since the available energy in a supercapacitor can be easily estimated by measuring its voltage.

Energy Neutral Design Framework

In this section, the implications of MESC when it is used as a plug-in for a practical energy harvesting WSN node are considered. In particular, the size of energy storage devices (C O and C S ) and the system QoS at the design phase are discussed.

First of all, we present how to eciently determine the size of OutCap (C O ). For a complete booting of the WSN node from the exhaustion of energy, the buered energy in OutCap must satisfy the following constraint:

η 2 C O (V 2 T HR -V 2 T HF ) > E Reset (2.3)
where E Reset is the energy consumed by the WSN node during the booting process.

The booting process is enabled as soon as V O reaches V T HR . An amount of consumed energy E Reset causes a voltage drop of V O . However, V O must be higher than V T HF for a successful booting. The constraint in (2.3) means that energy buered in C O from voltage level V T HF to V T HR must be sucient for the booting process.

Once the system has booted, the WSN node periodically performs communication in the active period (T Active ) and then stays in low power mode during the sleep period (T Sleep )

until the next wake-up interval (T W I ) as shown in Fig. the consumed energy as well as the harvested energy. Let us show their relationships in the following subsections.

Periodic Energy Source

In this subsection, it is assumed that the energy is only harvested during an energy interval (T EI ) but not during a non-energy interval (T N EI ) (e.g. solar energy in an outdoor environment). This behavior is periodically repeated. A proposed technique to estimate T EI and T N EI can be found in [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF]. Fig. 2.10 shows one cycle of the periodic energy source where the WSN node is required to send data each wake-up interval T W I during the whole cycle. Therefore, during the T EI , a part of the harvested energy must be reserved in StoreCap for activities of the wireless node during the T N EI . The total harvested energy buered in StoreCap during T EI is estimated as

E EI = P H T EI - 1 η T EI T W I E Active + P Sleep T EI -P Leak T EI (2.4)
where P H is the harvested power, T EI T W I is the number of wake-up times during T EI , E Active is the consumed energy of the WSN node for each wake-up, P Sleep is the consumed power in sleeping mode and P Leak is the leakage power of the whole system. The rst term is the harvested energy during the energy interval, while the second one represents the consumed energy of the WSN node including active and sleeping period. From Fig.

2.10, the total sleep time of the node is

T Sleep = T EI - T EI T W I T Active (2.5)
However, the second term in (2.5) is in order of ms (as T Active is only µs), which is negligible compared to the rst term (in order of hour). Therefore, the total leakage energy is estimated as P Leak T EI , where T EI is approximated as the total sleep time of the node during harvesting energy interval. In order to buer this amount of energy (E EI ), the size of StoreCap must satisfy

1 2 C S (V 2 M ax -V 2 T HF ) > E EI (2.6)
where V M ax is the maximum voltage of the StoreCap. Meanwhile, the consumed energy during the T N EI is

E N EI = 1 η T N EI T W I E Active + P Sleep T N EI + P Leak T N EI (2.7)
To achieve theoretical innite lifetime of the WSN node, the ENO condition after a cycle including T EI and T N EI needs to be satised. For that, we must have E EI = E N EI , and so

T W I = (T EI + T N EI ) E Active ηP H T EI -ηP Leak + P Sleep (T EI + T N EI ) (2.8)
This result presents the trade-o between the QoS of the system (T W I ), the consumed energy in the active period (E Active ) and the harvested power (P H ). Obviously, the lower E Active and the higher P H will decrease T W I and therefore, increase the system QoS. Moreover, (2.4) and (2.6) present how C S can be determined to maximize QoS during the design phase. The increase of P H requires more space in StoreCap otherwise, a part of harvested energy is discarded. Instead of having E EI , only

1 2 C S V 2 M ax -V 2
T HF can be used for operations during non-harvesting energy interval (T N EI ). The consumed energy during T N EI has to be reduced for ensuring ENO condition, but it also reduces the overall system QoS. It is interesting to notice that only the minimum C S satisfying (2.6) provides the maximum QoS. Bigger capacitance would not improve the QoS due to higher leakage energy. Moreover, it is obvious that V S at the end of a cycle should be equal to the value at the beginning of this cycle when ENO is satised. Unfortunately, the minimum V S during non-harvesting energy interval (T N EI ) is V T HF which means that the ENO is only considered when V S > V T HF during harvesting energy interval (T EI ). A simple solution is to charge V S to V T HF before its deployment. Otherwise, the ENO cannot be satised until V S reaches V T HF .

Continuous Energy Source

In constrast with energy sources presented in the previous subsection, a continuous energy source (e.g. heat energy from an industrial machine, light energy in a hospital) usually provides harvested energy most of the time. A proposed WSN behavior when using this energy source is that the WSN node always maximizes the QoS of the system without the need of buering energy in StoreCap. In this context, the ENO condition is only considered during the harvesting energy interval. Therefore, from (2.8), let T N EI = 0, we have

T W I = E Active η (P H -P Leak ) -P Sleep (2.9)
This result shows that the ENO condition is satised at anytime of the energy interval because T W I is independent of T EI . As a consequence, the size C S is not important in this design because there is no surplus energy to charge the StoreCap. This behavior is also useful when C S is fully charged (V S = V M ax in Fig. 2.8). Since there is no more space for storing the harvested energy, the wireless node can maximize the QoS according to the harvested energy to avoid wasting energy. Moreover, (2.9) can be used to estimate the QoS of the system according to E Active and P H when the environmental energy is available.

Our design framework presents a trade-o to design an ecient MESC for a particular sensor node. It is obvious that when energy is only available for a certain period, harvesting node has to buer a part of harvested energy for continuous operations during the non-harvesting energy interval. Therefore, the size of the supercapacitor must be optimized not only to suciently buer the harvested energy, but also to minimize the leakage energy. However, when harvested energy is available most of the time, the wireless node consumes energy as much as it can harvest while respecting the ENO condition. In this context, the smaller size of StoreCap provides a better QoS as it reduces the leakage. Next section shows in details how capacitors are optimized when MESC is used for powering a PowWow WSN node. For communications between nodes, asynchronous protocols have shown their energy eciency for a low trac EH-WSN [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF]. These protocols are based on a non-scheduled preample sampling that saves wasted energy due to the synchronization between many nodes. Some typical protocols in this eld are RICER (Receiver Initiated Cycled Receiver) [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF], TICER (Transmitter Initiated Cycled Receiver) [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF], WiseMAC (Wireless sensor MAC) [START_REF] El-Hoiydi | Wisemac: An ultra low power mac protocol for multi-hop wireless sensor networks[END_REF] and TAD-MAC (Trac-Aware Dynamic MAC) [START_REF] Alam | Tad-mac: Trac-aware dynamic mac protocol for wireless body area sensor networks[END_REF]. A meticulous choice of the MAC protocol is a key to improve the QoS of the EH-WSNs due to its direct impact on the consumed energy (E Active ). TAD-MAC was shown to be an ultra low power and energy ecient protocol for wireless body area sensor networks. Therefore, it can be used for a single-hop EH-WSN with a low variation on the wake-up interval of many nodes. Meanwhile, WiseMAC is able to deal with the variation in a multi-hop network by exploiting the knowledge of the schedule of neighbor nodes to provide a minimized preamble sampling. In a general network, either RICER or TICER can be used.

For ensuring the data transmission, the preamble must be long enough in order to have a successful rendez-vous.

In this work, a simple MAC protocol based on RICER [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF] illustrated on Fig. 2.11 is used.

This protocol has been validated on the PowWow platform and the energy consumed by dierent states of the protocol stack was fully characterized [START_REF] Alam | A hybrid model for accurate energy analysis of wsn nodes[END_REF]. The receiver, powered by batteries and connected to a host computer, periodically sends a wake-up beacon (WUB) each T b = 50ms. When a transmitter wants to send a data packet, it has to wait for the WUB from the receiver (idle listening). After receiving the WUB, the transmitter performs a Clear Channel Assessment (CCA), Calculation Before Transmission (CBT) and a data packet transmission (DT). In order to deal with clock drift, the maximum idle listening period at the transmitter (T idle ) must be slightly higher than T b and is set to 52ms. Acknowledgement packets (ACK) are not used in this protocol as retransmission is not implemented at the transmitter. To evaluate the average consumed energy for each wake-up of the node, Table 2.1 summarizing the consumed energy of each state is used. For a long time measurement, we assume that T idle = T b 2 as the average of idle listening time and therefore,

E Active = P Rx T idle + E W U B + E CCA + E CBT + E DT = 2081µJ (2.10)
To determine C O , the consumed energy for booting a PowWow node (E Reset ) has been experimentally measured to 1094µJ. Therefore, from (2.3), C O must be at least greater than 560µF. From the list of available capacitors and also to provide fault tolerance, a low leakage 680µF is used for the OutCap. V O is rapidly increased. As soon as V O reaches V T HR = 2.9V, the EN signal is enabled to power the node. Then, a voltage drop can be observed on V O due to the consumed energy of the booting process. However, V O is still higher than V T HF = 1.8V. From this point, the MCU stays in low power mode and wakes up every 4s to read V O through a low power ADC channel. As it can be seen on Fig. 2.12, V O is slower increased when the MCU has booted since a part of harvested energy is consumed by the MCU. As soon as

Volt (V) EN V O V T HR V T HF RF is initialized MCU is initialized V O = V Re PVs are disconnected V O is decreased due to leakage
V O is higher than V RF = 3V , the RF chip can be initialized to successfully complete the booting process. If a bigger capacitance is used for V O the booting time is longer due to the delay to charge V O to V T HR . By using (2.3), we can optimize C O to minimize the booting time, which is about 50s in this measurement, as shown in Fig. 2.12.

Another promising energy source is the heat energy from industrial machines. WSNs with thermal energy can be used for monitoring the health condition of these machines.

By analyzing their temperature, vibration, strain, and pressure in real time, engine maintenance will not be required until these data show it is necessary, rather than regularly scheduled, therefore, saving a great deal of cost. A TEG of size 30x30x3.3mm is used in this study. The hot surface is attached to a laptop adapter as the heat source while the cold surface is chosen to provide the heat spreading eect towards a heat sink. The Due to lower harvested energy, a longer booting time is required compared to solarpowered node. After about 80s, the MCU is powered on when

Volt (V) EN V O V T HR V T HF Heat energy is insufficient to increase V O to V Re PowWow node is powered off when V O < V T H F
V O = V T HR . V O keeps
increasing as the harvested energy is still greater than consumed energy of the PowWow node which is so far, only related to the MSP430. However, after initializing the RF transceiver CC2420, the harvested energy is less than the total consumed energy of the node including both MSP430 and CC2420. Therefore, V O is decreased instead of continuously increasing to V Re . The PowWow node is completely powered o when

V O = V T HF .
The node wakes up again when there is enough buered energy in the OutCap until its next shut down. Although the booting process is successful, heat energy in this measurement is insucient to satisfy ENO. A simple solution is to add one more TEG to increase the input power which can be found in [START_REF] Le | Duty-Cycle power manager for Thermal-Powered wireless sensor networks[END_REF].

Design Framework Validation

In this section, measurements to validate our design framework with two kinds of energy sources are presented. The PowWow node is now powered by two PVs of size 4x6cm setup in an oce where the light condition is around 800lux. The measured harvested power at this condition is 574µW in average and can be considered as a periodic energy source. Therefore, from (2.8), T W I = 52s. The Fig. 2.14 shows a short measurement of Fluorescent light conditions in a hospital or an industrial environment is a potential energy source for monitoring applications using energy harvesting WSNs. This kind of energy source can provide a theoretical innite T EI because the indoor lights are usually switched on all the time. Therefore, it can be considered as a continuous energy source.

Considering two PVs used in the previous measurement, and using (2.9) to simulate this situation, T W I is reduced to 5.7s. Fig. 2.15 presents the V S for two nodes with dierent start up voltage in this context (3.87V for the rst node and 3.80V for the second node). V S is almost constant because, from (2.9), the ENO condition is always satised. Experimental results presented in Fig. 2.14 and Fig. 2.15 show the high accuracy of our system design model as the node behavior exactly satises the proposed application and the ENO condition.

Maximize QoS in ENO

In this section, our design framework is used to determine C S in order to maximize the QoS in ENO for EH-WSN nodes. The leakage power of the whole system is modeled by

P Leak = 13 + C S 0.9F 30(µW ) (2.11)
The leakage power of C S is linearly increased with its capacitance and is modeled based on the leakage power of a CapXX 0.9F characterized in Section 2.4. Meanwhile, the leakage power of the remaining components of MESC is estimated to 13(µW).

First of all, from (2.8) for a periodic energy source, the harvested power must be at least greater than 500(µW) to achieve a valid value of T W I (the denominator must be greater than zero). Lower harvested powered can not satisfy the ENO condition. In this context, the harvested energy is less than the total consumed energy even if the WSN node always stays in sleeping mode without any communication. Fig. 2.16 presents the average T W I according to dierent levels of harvested power (P H ) and capacitance of the storage (C S ). As it can be observed when P H = 600µW and C S = 0.9F, from (2.8), the wake-up interval during T EI can reach 23s. Unfortunately, the condition (2.6) is false, C S is fully charged to V M ax and then, all of harvested energy is discarded. To ensure ENO, the performance during T N EI , when there is no more harvested energy, must be reduced to 49s. Therefore, the average wake-up interval is only 33s as it can be found in Fig.

2.16.

When the size of C S is increased to 1.1F, the system has more space to store the

P H = 700(µW )
Figure 2.17: Average wake-up interval (T W I ) according to dierent harvested power (P H ) and storage capacitance (C S ) with a continuous energy source.

harvested energy and the wake-up interval during T N EI is reduced to 28s. However, due to higher leakage, the performance during T EI is reduced to 25s resulting to an average wake-up interval of 27s. In this case, just a small part of harvested energy is wasted. The best performance with minimum wake-up interval is achieved (T W I = 25s) when C S = 1.2F. All harvested energy is stored in the storage and is used for packet transmissions.

Increasing the size of C S greater than 1.2F is inecient as the global consumed energy is also increased due to leakage energy and the wake-up interval has to increase to satisfy ENO. Although there is no wasted harvested energy when C S = 1.8F, the overall QoS (T W I = 32s) is still lower than the case when C S = 1F (T W I = 30s).

When the harvested power is increased, the average wake-up interval is reduced to provide better QoS as presented in Fig. 2.16. For each level of harvested power, there is an optimized C S which provides the highest QoS with minimum T W I . However, the impact of leakage energy on the average wake-up interval is reduced with high harvested power.

When P H = 1000µW for instance, the minimum T W I is 15s when C S = 1.8F. However, when C S is increased to 2.6F, T W I is only reduced to 18s. Based on these results, if the harvested power is estimated in range of [800 -1000](µW), two capacitors 0.9F can be used to provide 1.8F for the energy storage. From the datasheet of a harvester, its generated power (P H ) can be estimated and from our design framework, an optimized capacitance (C S ) can be determined to provide a maximized QoS in ENO for EH-WSN nodes.

Chapter 2. Energy neutral design framework for autonomous WSN 39 For a continuous energy source, the harvested power should be greater than 200µW to satisfy the ENO as the denominator in (2.9) must be greater than zero. As it can be observed from Fig. 2.17, the leakage energy due to bigger capacitance has a linear impact on the wake-up interval of the node. This problem is discussed in Chapter 4

where an Energy Monitor-based Power Manager (EM-PM) adapts the wake-up interval of a thermal-powered PowWow node in real-time. The average performance is reduced by about 1s when two 0.09F capacitors, instead of only one, are used for C S . However, the wake-up interval is more stable with higher capacitance as the EM-PM does not regard the small change on V S , which is the main input to perform adaptations. In contrast, with lower capacitance, the EM-PM is more sensitive to the change of V S which results in more variations of the wake-up interval. This behavior should be taken into account when designing MAC protocols. For instance, TAD-MAC requires a stable wake-up of the transmitter to optimize the wake-up interval of the receiver. Meanwhile, WiseMAC is able to deal with variations of the wake-up interval. Finally, the impact of leakage energy is also reduced when more energy can be harvested.

Conclusions

In this chapter, the Multiple Energy Sources Converter (MESC) which is compatible with dierent environmental sources has been proposed. It is exible and provides a complete solution for EH-WSN. The system has robust booting and durable energy storage by using two capacitors for the energy storage devices. Moreover, the precise energy neutral design framework provides various considerations for an ecient EH-WSN platform such as the size of energy storages to maximize the QoS of the system according to the consumed energy, and the harvested energy when the node is operating in ENO.

The following chapters will focus on a dynamic power manager which adapts the system QoS according to harvested energy. To achieve this, an energy monitor providing energy proles such as the harvested energy, consumed energy as well as the energy in storage devices in running phase must be embedded. When supercapacitors are used for the energy storage, our design takes advantage of a simple energy monitor which is proposed in Chapter 3. By only reading the voltage of StoreCap and tracking all activities of a EH-WSN node, these energy proles are estimated. Moreover, an energy predictor using the adaptive lter is also proposed to provide the prediction of the potential harvested Chapter 2. Energy neutral design framework for autonomous WSN 40 energy. Based on these information, the power manager is able to perform adaptations in order to achieve ENO on an EH-WSN node.

Chapter 3 Energy Monitor and Energy

Predictor for Supercapacitor-based Energy Storage

Introduction

Wireless Sensor Networks (WSNs) provide a powerful combination of distributed sensing, computing and wireless communication that can be useful in various monitoring applications [START_REF] Puccinelli | Wireless sensor networks: applications and challenges of ubiquitous sensing[END_REF]. In order to design autonomous WSNs, energy harvesting (EH) and supercapacitor-based energy storage are considered as a promising approach. Moreover, a power manager (PM) is also embedded inside the wireless node to respect the Energy Neutral Operation (ENO) [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF]. In the cooperation with the PM, an energy monitor is designed to provide energy proles, including the harvested energy (ẽ H ), the consumed energy (ẽ C ), the leakage energy (ẽ Leak ) as well as the available energy in the storage device (ẽ S ). These data will be used by the PM to dynamically adapt activities of the wireless node while respecting ENO condition. usage. These computations are usually done on the MCU. This approach can be found in [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF] where a low-power monitor IC (DS2438) is used to perform the role of energy monitor. Information regarding instantaneous solar power availability, battery terminal voltage and accumulated current are provided by a one-wire interface (SPI or I2C).

However, on-board ADC in [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF] is only 10 bits which limits the resolution. To achieve higher resolution, a scalable power observation tool (SPOT) taking advantage of a voltage-to-frequency converter (VFC) to read the voltage drop from a shunt resistor is proposed in [START_REF] Jiang | Micro power meter for energy monitoring of wireless sensor networks at scale[END_REF]. Since VFC is entirely an analog device, it provides an innite resolution. In order to minimize MCU overhead, an internal 32-bit counter is used to directly provide the energy measurement. In this way, SPOT is well adapted for a WSN node to monitor its harvested and consumed energy. Moreover, with extremely high sampling frequency (20kHz) compared to monitor IC ( 36.41Hz with DS2438 [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF]), SPOT is able to follow the change of current during a short active period of the WSN node instead of an assumed constant current in [START_REF] Raghunathan | Design considerations for solar energy harvesting wireless embedded systems[END_REF].

Software-based Energy Monitor

Software approaches do not require sophisticated hardware designs. However, some ofine characterizations need to be performed before the implementation. A simple energy monitor presented in [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF] is based on a small illuminance sensor and the consumed energy proles for each actitivity of the WSN such as sensing and RF transmission. Experimentations are performed on a WSN platform (TI ez430) equipped with a small PV in size of 2.25x2.25in. The harvested energy is approximated by a function of the light intensity from the illuminance sensor (expressed in lux). Meanwhile, the consumed energy is Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 45 the average current delivered from the battery during a period and is computed independently for sensing, transmitting and receiving a data packet. This solution can be extended to dierent kinds of harvesters and is depicted in Fig. 3.3. However, an environmental sensor is required to estimate the harvested energy (e.g. illuminance sensor for solar energy, temperature for thermal energy) and a look up table (LUT) is used to estimate the consumed energy of the WSN node.

While state-of-the-art energy monitors are designed for battery-based systems, our proposed energy monitor is dedicated for supercapacitor-based EH-WSNs. This energy monitor also benets from a LUT to provide a precise consumed energy of the WSN node.

When a supercapacitor is used for energy storage, harvested energy can be estimated by reading the voltage of supercapacitor without any environmental sensor. Therefore, our approach can be easily applied for dierent energy sources.

Supercapacitor-based Energy Monitor

In capacitor energy. Fig. 3.4 shows the equivalent circuit used to analyze the energy in the capacitor with the assumption that the load power is constant. The power drawn from the capacitor must be equal to the power consumed by the load and resistor ESR as follows:

p Cap (t) = -(P Load + p R (t)) (3.1)
where p Cap (t) is the power drawn from capacitor:

p Cap (t) = CV C (t) dV C (t) dt (3.2)
and p R (t) is the power loss from ESR resistor:

p R (t) = i 2 (t)R (3.3)
and P L oad is the power consumed by the wireless node, which is assumed as a constant value. Therefore, it can be approximated as the average energy consumption during a time slot lasting for T S :

P Load = ẽC (n) T S (3.4)
From results in [START_REF] Olivo | Analysis of ultra capacitors as ups energy storage devices[END_REF], the instantaneous current is evaluated as:

i(t) = C dV C (t) d(t) = -V C (t) + V 2 C (t) -4RP Load 2R (3.5)
Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 47

Sampling the voltage across the capacitor many times during slot n, we can approximate the average current I(n). Therefore:

ẽS (n) = 1 2 CV 2 S (n) -I 2 (n)RT S (n) (3.6)
The rst term in (3.6) is the energy stored in the capacitor that comes from (3.2) while the second one is the energy dissipated in R. However, the low consumed current of the WSN node (in order of mA) and low ESR (in order of mΩ) implies that the second term in (3.6) is in order of pJ, and is therefore, negligible when compared to the rst one (in mJ). Consequently, the buered energy in StoreCap can be approximated according to its voltage V S (n) and capacitance C S as:

ẽS (n) = 1 2 C S V 2 S (n) (3.7)
Meanwhile, the leakage energy of the whole system during slot n depends on C S . As

shown in [START_REF] Zhu | Leakage-aware energy synchronization for wireless sensor networks[END_REF], leakage energy is more severe for a larger capacitor than for a smaller one.

However, for a specied C S , leakage energy can be estimated by

ẽLeak (n) = P Leak T S (n), (3.8)
where P Leak is the leakage power, which is considered as a constant.

Consumed Energy Model

In a WSN application, a node periodically wakes up and briey turns on active mode for sensing and RF communication. It stays most of the time in sleeping mode for energy saving. Therefore, the total consumed energy during slot n can be divided into two parts:

ẽActive (n), the consumed energy in active time, and ẽSleep (n), the consumed energy in sleeping time. As the consumed current is nearly constant in sleep mode, ẽSleep (n) can be easily estimated based on the sleep power P Sleep as

ẽSleep (n) = P Sleep T Sleep (n) ≈ P Sleep T S (n) (3.9)
The total sleeping time during slot n (T Sleep (n)) is approximated to T S (n) as the total active time is negligible (in order of ms) compared to the sleeping time (in order of s).

Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 48 However, it is more complex to estimate ẽActive (n) based on the power as many dierent scenarios in the active time results in a variation of the consumed current of the WSN node. Therefore, to obtain more accurate measurements, it is necessary to identify the consumed energy of many scenarios according to a specic WSN platform.

In this work, PowWow [86] platforms, which are based on the MSP430 microcontroller and the CC2420 RF transceiver, are used to evaluate our approach. The consumed energy for these activities performed on PowWow platform has been already characterized and gathered into Table 2.1 which is used as a LUT. The microcontroller keeps tracking all activities of the node to estimate the consumed energy in active mode during a slot. Let us apply the energy monitor into two PowWow nodes performing communications based on the RICER protocol as shown in Fig. 2.11. In this example, when the transmitter wants to send a data packet, it has to wait for a Wake-Up Beacon (WUB) from the receiver. Once a WUB is received, the transmitter performs Clear Channel Assessment (CCA) and Calculation Before Transmission (CBT) before transmitting a data packet (DT). We dene k the number of wake-up occurring during slot n of two nodes. Therefore, the total consumed energy in active period of the transmitter can be expressed as

ẽActive (n) = k i=1 t idle (i)P Rx + k(E W U B + E CCA + E CBT + E DT ) (3.10)
where t idle (i) represents the i th idle listening time for the WUB. Meanwhile, the receiver sends its WUB every wake-up and waits for a potential packet. Therefore, the consumed energy at the receiver during slot n, occurring k wake-up times, is

ẽActive (n) = k i=1 (E W U B + t DR (i)P Rx ) + θE DR (3.11)
where t DR is the idle listening time for a data packet from the transmitter, θ is the number of received packets during slot n. It is obvious that θ ≤ k, as a data packet is not always responded after the receiver sends its WUB. Finally, the total consumed energy during slot n is estimated as

ẽC (n) = ẽActive (n) + P Sleep T S (n) (3.12)
where ẽActive (n) is estimated from tracking all activities of a WSN node during slot n.

Harvested Energy Model

Following the energy ow principles explained in Section 2.4, the harvested energy is distributed into two parts by the energy ow controller (see Fig. 2.7). While the rst part is directly consumed by the DC/DC converter to power the wireless node, the second one is stored into the StoreCap and is consumed by the leakage eect. Therefore, the harvested energy during slot n can be estimated by

ẽH (n) = 1 η ẽC (n) + [ẽ S (n) -ẽS (n -1)] + ẽLeak (n) (3.13)
where η is the DC/DC converter eciency, ẽC (n) is the consumed energy of the wireless node, [ẽ S (n) -ẽS (n -1)] is the energy stored in the StoreCap during the slot n and ẽLeak is the leakage energy. When the harvested energy is greater than the sum of the consumed energy and the leakage energy, a part of harvested energy is consumed by the wireless node and leakage and therefore, only the surplus harvested energy charges StoreCap. On the other hand, when the harvested energy is less than the sum of the consumed energy and the leakage energy, all harvested energy is transferred to the load and the remaining energy is served by the StoreCap. In this case, the second term in

(3.13), [ẽ S (n) -ẽS (n -1)], is negative.
Our software energy monitor only requires the voltage of StoreCap (V S ) and a LUT to estimate all energy proles used for the PM such as the available energy in the StoreCap (ẽ S ), the consumed energy (ẽ C ) and the harvested energy (ẽ H ). The main advantage of our approach is that no additional hardware device is required. Therefore, this energy monitor is independent of harvesters and can be extended to dierent energy sources (e.g. solar, thermal, wind energy). However, the energy monitor suers from the noise when reading V S (n) and therefore, an average lter should be used to reduce this noise. PVs in an oce. The energy monitor is activated every T S (n) = 2 minutes. The average voltage of StoreCap used to evaluate all energy proles is computed from three values of V S . As it can be observed from Fig. 3.5, ẽC (n) has a high variation due to dierent t idle in (3.10). Meanwhile, ẽH (n) presents a lower variation since the light condition is almost constant during our measurement. This variation is considered as the mandatory error of our measurement method. Therefore, a PM using this energy monitor must has a fault tolerant scheme to overcome this problem.

State-of-the-Art Energy Predictor

In order to design an eective PM, some approaches benet from an accurate energy predictor to estimate the energy that can be harvested in the near future. Based on several historical values of harvested energy, a potential energy can be predicted. When considering an EH-WSN node with limited resources, the energy predictor must have a low complexity and a small memory space for historical values. State-of-the-art prediction algorithms suitable to EH-WSNs can be divided into two categories: predictors based on analysis and predictors based on learning.

Analysis-based Predictor

In the rst category, we can nd Exponentially Weighted Moving Average (EWMA) [START_REF] Kansal | Harvesting aware power management for sensor networks[END_REF] and Weather Condition Moving Average (WCMA) [START_REF] Bergonzini | Algorithms for harvested energy prediction in batteryless wireless sensor networks[END_REF] for solar energy. The principle of EWMA is to divide the time into several slots with the same duration and to apply a weighting factor to previous predicted values to estimate the energy that will be harvested during the next slot. With an array for previous predicted energy and an array for real harvested energy in a day, a full prole of the predicted energy in the next day can be provided by EWMA. However, EWMA has a low accuracy when a cloudy day is followed by a sunny day or vice versa. The WCMA overcomes this drawback by taking into account some previous harvested energy in a given day to estimate the weather condition. In this way, WCMA provides a better prediction as harvested energy close to the present value has a high relevance to the next value. However, both EWMA and WCMA have xed internal parameters which are optimized for solar energy prediction.

Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 51

Learning-based Predictor

In the second category, QLearning [START_REF] Hsu | Reinforcement learning-based dynamic power management for energy harvesting wireless sensor network[END_REF] and Neural Network algorithms [START_REF] Karunanithi | Using neural networks in reliability prediction[END_REF] can be used for any harvesting device with a learning phase to automatically congure their internal parameters. In QLearning algorithm, a decision maker agent, namely the learner, takes actions to the environment and receives reward. After several tries-and-errors, the agent gradually learns the best policy, which is the sequence of actions that maximizes the total reward. On the other hand, the neural network consists of many neurons whose connections are represented by weighted factors. During the training phase, these weighted factors are updated to respect an error less than a threshold. This phase is the disadvantage of both neural network and QLearning when compared to WCMA or EWMA as it requires an additional energy cost due to their high complexity.

These approaches can be used to design a predictive PM and optimize future activities of the EH-WSN node. However, while analysis-based predictors are specically designed for solar-powered EH-WSN, learning-based predictors are expensive in term of memory space and complexity. In our approach, an energy predictor based on an adaptive lter is proposed. Not only low complexity with acceptable accuracy, our energy predictor also has a small memory space as only a previous harvested energy is used to estimate the future energy. Moreover, this energy predictor is able to extend to dierent energy sources as its internal coecients are updated after each prediction to minimize the error.

Energy Predictor using Adaptive Filter

In the PM designed for EH-WSN, the time axis is divided into slots of the same duration and the adaptation calculation is carried out at the end of each slot [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF]. Therefore, the energy predictor is also periodically activated together with the PM to provide the predicted harvested energy proles. We dene the following discrete values used for the adaptive lter-based energy predictor (AF-EP):

p, µ : order and step size of the adaptive lter. W (n) : vector of p coecients of the AF-EP,

W (n) = [w 1 (n), w 2 (n), ..., w p (n)].
êH (n) : predicted harvested energy for slot n.

err(n) : error of the prediction for slot n.

T S (n) : predictor interval or slot duration.

The architecture of the energy predictor using an adaptive lter is presented in Fig. 3.6.

A historical harvested energy prole is stored in the vector ẼH (n). Based on this prole, the predicted harvested energy for the next slot (ê H (n+1)) is produced by a dot product between ẼH (n) and the coecients of the adaptive lter W (n) as follows:

êH (n + 1) = ẼH (n).W (n) (3.14)
After a slot duration T S , an energy monitor is activated by a timer to estimate the harvested energy during slot (n + 1). The error of the prediction err(n + 1), which is the dierence between the predicted energy and the real energy as

err(n + 1) = êH (n + 1) -ẽH (n + 1) (3.15)
is fed back to adjust coecients of the AF-EP in the next slot W (n + 1). Meanwhile, ẽH (n + 1) is used to update the next historical values ẼH (n + 1).

Algorithms to adjust the lter coecients can be divided into two categories : the Least Mean Square (LMS) and the Recursive Least Square (RLS). Compared to the RLS algorithm, the LMS does not involve any matrix operation. Therefore, the LMS requires fewer computation resources and less memory compared to RLS. The implementation of the LMS is therefore, less complicated than the RLS. The standard LMS updates the coecients of the adaptive lter as follows:

W (n + 1) = W (n) + µerr(n + 1) ẼH (n) (3.16)
However, the main drawback of the standard LMS is that it is sensitive to the change of the input vector ( ẼH (n)). Therefore, it is impractical to determine the optimized step size µ. The Normalized Least Mean Squares lter (NLMS) is a modied form of the standard LMS that overcomes this problem by normalizing the input vector. The NLMS algorithm updates the lter coecients as

W (n + 1) = W (n) + µerr(n + 1) ẼH (n) ẼH (n) 2 (3.17)
where ẼH (n) is the modulus of vector ẼH (n), which is dened as

ẼH (n) = ẽ2 H (n) + ẽ2 H (n -1) + ... + ẽ2 H (n -p + 1) (3.18)
Unfortunately, the implementation of NLMS faces with divide-by-zero errors when (3.18) returns 0. This scenario occurs during the night in harvesting WNSs based on solar energy. Therefore, the predictor needs to check ẼH (n) before updating its coecients.

When ẼH (n) = 0, the next value of harvested energy is simply predicted as zero.

Simulation Results

Our simulations are performed on a dataset downloaded from National Energy Renewable Laboratory (NERL) [94]. The visualization of the harvested power, which is another shape of harvested energy is shown in Fig. 3.7. In order to obtain a higher accuracy of the results, only harvested energy proles during the day are considered. The consecutive Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 54 zero harvested energy during the night can aect the average error and therefore, are discarded. This simulation set-up is also used in [START_REF] Bergonzini | Algorithms for harvested energy prediction in batteryless wireless sensor networks[END_REF].

The energy predictor should have the same activated period as the PM. Authors in [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF] have shown that the optimized adaptation period is a few minutes or even a few seconds rather than 30 minutes as in [START_REF] Kansal | Harvesting aware power management for sensor networks[END_REF]. Therefore, the predictor interval (T S (n)) is set to 5 minutes for simulations carried out in this section. The metrics used to compare dierent energy predictors are dened as follows:

E avg (%) : average error of the predictor, dened as

E avg = 1 N N n=1 err(n) ẽH (n) (3.19)
where N is the number of slots over 14 days and err(n) returns the absolute value of the prediction error err(n).

M em (16-bit words): memory space for storing historical energy proles.

M ul: number of multiplications for the prediction algorithm.

The Fig. 3.8 shows the average error of the predictor for dierent lter order (p) and step size (µ) values. As it can be observed, the lowest average error (14.7%) is obtained when p = 1 and µ = 0.3. This result shows an extremely small memory space of our AF-EP as only one historical value of harvested energy is used to predict the next one. In this conguration, (3.17) is simplied as follows:

W (n + 1) = W (n) + µerr(n + 1) ẽH (n) (3.20)
since ẼH (n) only has one element ẽH (n). Therefore, the complexity of the NLMS algorithm is signicantly reduced.

The Table 3.1 shows a comparison of our energy predictor using NLMS with WCMA and EWMA. The WCMA presented in [START_REF] Bergonzini | Algorithms for harvested energy prediction in batteryless wireless sensor networks[END_REF] requires ve days of harvested energy proles to estimate the current weather condition and therefore, 1152 words are used to store historical values. The WCMA brings the lowest average error, but also the highest computations compared to NLMS and EWMA as it needs to compute the GAP factor which represents the dierence between the weather on the current day and ve previous Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 56 days at a given time. The predictor based on WCMA takes into account both the previous harvested energy proles and the current weather condition to improve the accuracy. Meanwhile, the EWMA predictor presented in [START_REF] Kansal | Harvesting aware power management for sensor networks[END_REF] only needs 288 words for the historical harvested energy proles in a day. The prediction of harvested energy in a slot is based on the previous predicted energy and is weighted with the real energy in the historical proles at the same slot. In this way, EWMA is able to provide a predicted energy prole for all slots in the next day without waiting for the real energy in some previous slots as NLMS or WCMA. However, this is also the drawback of EWMA when the weather condition changes from a sunny day to a cloudy day or vice versa [START_REF] Piorno | Prediction and management in energy harvested wireless sensor nodes[END_REF].

Predictor based on EWMA has the lowest computation requirement (expressed with the number of multiplications) as the predicted energy is the sum of the previous real energy and the previous predicted energy. In this comparison, the weighting factor for the previous real energy of both WCMA and EWMA is set to 0.5.

In our predictor using NLMS, the next harvested energy is only based on the previous real harvested energy. Therefore, our predictor has the lowest memory space for historical values. However, it is able to provide a low average error (14.7%) with p=1 and µ = 0.3.

The improvement of our predictor compared to WCMA and EWMA is the error of each prediction is fed back to adjust the lter coecients. In this way, the energy predictor is able to faithfully follow the tendency of the incoming harvested energy.

The dynamic adaptation of the lter coecients in our predictor provides a capability to apply to dierent energy sources. In order to demonstrate this dynamic adaptation, we have performed the simulation with a wind power prole extracted from an actual output data recorded by NERL from large wind power plants in the Midwest [START_REF] Milligan | Statistical wind power forecasting models: results for us wind farms[END_REF]. The average error with dierent values of the lter order (p) and step size (µ) is shown in Fig. 3.9. When p = 1 and 0.2 < µ < 1.5, the predictor achieves a high accuracy with an average error less than 10%.

Another simulation has been performed with a solar harvested power prole extracted from a PowWow [86] wireless sensor node set up near a window in an oce. The Fig. 3.10 shows that our energy predictor has precise decisions, even if there is a sudden change of the harvested power at around 80 minutes, when the curtain of the window is opened. At the beginning, the predicted power is far from the real one. However, the AF-EP coecients can be rapidly adjusted as the error is fed back after each prediction. From around 10 minutes, the predicted power follows closely the real power. The average error of the predictor in this case is very low, at 2.5%.

The adaptive lter is analyzed with dierent predictor interval (T S (n)) for p = 1 and µ = 0.3. The dataset for solar energy in Fig. 3.7 and wind energy in [START_REF] Milligan | Statistical wind power forecasting models: results for us wind farms[END_REF] are used for this simulation. Simulation results are summarized in Table 3.2. It can be observed that the average error is proportionally increased with the prediction interval as the previous real value has a lower relevance with the next value when T S (n) is increased.

Conclusions

The energy monitor is designed to provide the energy aware capability in EH-WSNs. It should be low complexity and low power consumption due to the limited resource of the WSN node. In this chapter, a software-based energy monitor for supercapacitor-based energy storage is proposed. By reading the voltage of the supercapacitor and tracking all activities of the WSN node, the harvested energy, the consumed energy as well as available energy in the supercapacitor are estimated. Therefore, this energy monitor can be easily extended to dierent energy sources without any change in hardware or software. In the following chapters, the energy monitor is used by the PM to satisfy the ENO condition of an EH-WSN node. However, the PM must have a fault tolerant scheme to overcome the error of the energy monitor due to the noise when reading the voltage of the supercapacitor.

Another contribution in this chapter is an energy predictor using the adaptive lter.

Compared to state-of-the-art algorithms, the adaptive lter with NLMS algorithm has a low complexity and small memory space. The advantage of our predictor is that the lter coecients are automatically updated by the error of the predictions. By this way, the energy predictor can be used with dierent energy sources such as solar energy in Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 59 outdoor environments, wind energy or light energy in an oce. The energy predictor performs well when the prediction interval is 5 minutes. In this case, the average error is under 15%.

Chapter 4

Duty-Cycle Power Manager for Continuous Energy Sources

Introduction

This chapter presents a set of novel power managers (PM) for EH-WSNs. Instead of minimizing the consumed energy as in the case of battery-powered systems, it makes the harvesting node converge to Energy Neutral Operation (ENO) by adapting its duty cycle according to the harvested energy. When there is more harvested energy, the wireless node wakes-up frequently (high duty cycle) in order to increase the system Quality of Service (QoS). Otherwise, when there is less harvested energy, the wireless node stays in low power mode longer (low duty cycle) for ensuring perpetual operations without any interruption.

As presented in Section 2.6, energy sources in this work are divided into two categories: continuous and periodic sources. In this chapter, the PM is specically designed for a continuous energy source. An example of this kind of energy source is uorescent lights in a hospital environment, where indoor lights are switched on all the time. As energy can be scavenged with rare interruption, the wireless node can consume an amount of energy equal to the harvested energy to maximize QoS while still satisfying ENO condition.

Another promising source belonging to this category is the heat energy from industrial machines. Thermoelectric generators (TEG) are used to convert the excess or wasted heat into electric energy for powering wireless nodes. Thermal-powered WSNs are amenable for monitoring health condition of these machines. By analyzing their temperature, vibration, strain, and pressure in real time, an engine maintenance will not be required until these data show it is necessary, rather than regularly scheduled, and therefore, saving a great deal of cost. Another example concerns the monitoring of patient health in a hospital with the heat from body skin. Some other kinds of energy sources such as wind and vibration energy, even if they are not continuous, can be also classied in this category if monitoring applications using these sources only require tracking data whenever the harvested energy is available. One example involves the tracking of a train, which can exploit the wind energy when it passes through a tunnel [START_REF] Cammarano | Energy-harvesting wsns for structural health monitoring of underground train tunnels[END_REF]. Another example concerns the monitoring pressure in tires of a car with vibration energy [START_REF] Westby | Design and modeling of a patterned-electret-based energy harvester for tire pressure monitoring systems[END_REF].

When following ENO in the context of continuous sources, the energy in the supercapacitor (StoreCap) is kept at a constant level as the consumed energy is always expected to be equal to the harvested energy at a given time. Therefore, two dierent approaches are proposed in this chapter. Firstly, an adaptive PM using a Proportional Integral Derivative (PID) is proposed and evaluated in this work. The controller estimates the buered energy by measuring the voltage of the StoreCap (V S ). Then, the controller adapts the QoS of the application to the available energy by determining the next wake-up interval of the node (T W I ) to reach stable operations. The main advantage of the PM with a PID controller is that it does not require monitoring either harvested or consumed energy.

However, the PID controller requires a long adaptation time to converge the WSN node to ENO state. This problem is overcome in the second PM that utilizes the simple energy monitor proposed in Chapter 3. By exploiting the estimation of the harvested energy as well as the consumed energy, PM with energy monitor takes better decisions to satisfy ENO condition. Both PMs aim to be independent of harvesters and therefore, can be easily extended with dierent energy sources.

The rest of this chapter is organized as follows. In Section 4.

Network Topology and Protocol

A single-hop EH-WSN (see Fig. 4.1), where a base station node (BS) collects data from other end device nodes (ED), is used in this work. Each ED is equipped with a harvester and a supercapacitor. Communications among EDs and BS are based on the RICER MAC protocol [START_REF] Lin | Power-ecient rendez-vous schemes for dense wireless sensor networks[END_REF] shown in Fig. 2.11. However, the ED reads a value from its sensor through an ADC channel (e.g. temperature, pressure,...) (SEN) before waiting for a WUB from the receiver (idle listening). Once a WUB is received, the ED performs CCA, CBT and DT.

The PM is implemented in each ED node to adapt its wake-up interval (T W I ) to satisfy the ENO condition. The time domain is divided into slots of duration T S (n) and the PM is carried out at the end of each slot. After a predened number of wake-up times (k), the PM is activated to estimate the next wake-up interval T W I as illustrated in Fig.

T S (n) = kT W I (n) (4.1)
This dynamic period as shown in [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF], provides a way to adapt the reactiveness of the PM according to the current T W I (n). It is obvious that when there is more harvested energy, T S (n) is decreased with a small value of T W I . It also means that the PM can be activated more frequently and therefore, provides a fast reactivity to the change of harvested energy. Otherwise, when there is less harvested energy, it is reasonable for the PM to have a longer adaptation period as the PM also consumes an amount of energy.

When an ED node has converged to ENO, the energy in StoreCap is kept at a constant level. It also means that V S is kept around a pre-dened desired voltage V Ref as the energy in a supercapacitor is squared proportional to its voltage, which is explained in (3.7) . Therefore, the PM proposed in this chapter estimates the next wake-up interval (T W I ) in order to converge V S to V Ref .

PID Controller-based Power Manager

In this section, a PID controller, used as a low complex power manager, is applied to energy harvesting ED nodes. PID is a generic control loop feedback mechanism [START_REF] Ang | Pid control system analysis, design, and technology[END_REF]. The PID controller calculates the error as the dierence between a measured and a desired value and attempts to minimize this error by adjusting the controlled output. Fig. 4.3

shows the PID structure with three components: Proportional is the error at the present time, Integral is the accumulation of past errors, and Derivative is the prediction of future errors. The weighted sum of these components is applied to an adaptation function f to generate the controlled output T W I (n)). This wake-up interval is applied to the WSN node and then, a new value of V S (n) is fed back to the input of the controller.

To apply the PID controller to a supercapacitor-based EH-WSN node, the error err(n),

which is evaluated at the end of current slot n, is dened as the change of energy in the StoreCap which acts as the storage device: The adaptive function f is inversely proportional to the accumulated error acc(n). When acc(n) > 0, energy harvesting has a tendency to be higher than energy consumption, the next wake-up interval T W I (n+1) should be reduced. When acc(n) < 0, adaptive function will increase T W I (n + 1). Therefore, f gives the next T W I (n + 1) through

err(n) = 1 2 C S V 2 S (n) -V 2 Ref (4.2) Proportional K p Integral K i Derivative K d WSN node WSN node Store Cap Store Cap MCU RF MCU RF f T WI (n+1) V Ref acc(n) err(n) + V S (n+1)
T W I (n + 1) = T W I (n) -acc(n) T S (n) (4.
3)

The PID controller-based PM (PID-PM) shows the interest of our approach since monitoring the harvested energy, consumed energy as well as leakage energy is not required.

It provides practical adaptations to match harvested and consumed energy of a wireless node when it is powered by a continuous energy source. Based on the principle of ENO, the PID-PM adapts the wake-up interval of a harvesting node according to the voltage level of a supercapacitor, which is used as the energy storage. Moreover, this design is also independent of harvesters and therefore, can be easily extended to dierent energy sources.

However, this PM suers from a trial-and-error process before converging to ENO state since it has no awareness of both harvested and consumed energy. The duration of this process is non-deterministic due to variations of the harvested energy in a dynamic environment. This duration directly depends on the three parameters of the PID controller K p , K i and K d . However, it is impractical to nd the optimal values for these parameters with dierent behaviors of harvesting sources. The second approach proposed in Section 4.4 takes advantage of a simple energy monitor to overcome this problem. When the energy monitor is embedded, the PM is able to estimate both the harvested energy and the consumed energy. In consequence, the PM can quickly converge the harvesting node to ENO state.

Energy Monitor-based Power Manager

In this section, a PM named Energy Monitor-based Power Manager (EM-PM) is proposed. The aim of this PM is also to determine the next wake-up interval (T W I ) to respect the ENO in the next slot and keep V S around V Ref as PID-PM. The EM-PM also has a dynamic adaptation period as presented in (4.1). Our approach aims to obtain a low complexity and multi-sources PM.

At the end of slot n, the energy monitor is activated to provide energy proles, including consumed energy ẽC (n) and harvested energy ẽH (n). Based on these proles, the PM predicts the consumed energy êC (n + 1) and the harvested energy êH (n + 1) for the next slot (n + 1). Firstly, êC (n + 1) can be predicted as êC (n + 1) = êActive (n + 1) + P Sleep kT W I (n + 1) (4.4)

where êActive (n + 1) is the predicted energy during active period and P Sleep kT W I (n + 1)

stands for sleep energy in slot (n + 1).

The harvested energy in the next slot êH (n + 1) cannot be predicted from the current harvested energy ẽH (n) as two consecutive slots usually have dierent durations with the dynamic adaptation period of the PM. However, it can be expressed as

êH (n + 1) = PH (n + 1)T S (n + 1) = PH (n + 1)kT W I (n + 1) (4.5)
where PH (n + 1) is the prediction of the harvested power in the next slot (n + 1), which can be predicted from the harvested power in the previous slot PH (n).

Moreover, the residual energy at the end of slot n, which is the energy dierence between the current state and desired state (when V S = V Ref ) can be considered as the additional budget energy for the next slot and is dened as following:

ẽBud (n + 1) = ẽS (n) - 1 2 C S V 2 Ref = 1 2 C S (V 2 S (n) -V 2 Ref ) (4.6)
Therefore, to satisfy the ENO condition in the slot (n+1), the following constraint needs to be respected:

êH (n + 1) + ẽBud (n + 1) = 1 η êC (n + 1) + ẽLeak (n + 1) (4.7)
where ẽLeak (n + 1) represents the leakage energy in the slot (n + 1) and can be estimated from the leakage power (P Leak ), which is considered as a constant for a particular supercapacitor, as

ẽLeak (n + 1) = P Leak T S (n + 1) = P Leak kT W I (n + 1) (4.8)
By applying (4.4), (4.5) and (4.8) into (4.7), the next wake-up interval can be determined as follows

T W I (n + 1) = [ê Active (n + 1) -ηẽ Bud (n + 1)] k η( PH (n + 1) -P Leak ) -P Sleep (4.9)
To predict êActive (n+1), let us consider how ẽActive (n) is estimated. Even if two consecutive slots usually have dierent period, the harvesting node has exactly k wake-up times.

For each wake-up, the node reads data from its sensor (SEN), opens an idle listening window for a WUB from the BS and then, performs CCA, CBT and DT. Therefore, t idle (i) converges to 1 2 kT idle . In a consequence, the Exponentially Weighted Moving Average lter (EWMA) [START_REF] Hsu | Adaptive duty cycling for energy harvesting systems[END_REF] is used to predict êActive (n + 1) as

ẽActive (n) = P Rx k i=1 t idle (i) + k(E SEN + E W U B + E CCA + E CBT + E DT ) + E EM (4.
êActive (n + 1) = αê Active (n) + (1 -α)ẽ Active (n) (4.11)
where α ∈ [0, 1] is a weighted factor. α should be greater than 0.5 to reduce the contribution of the recent value ẽActive (n) and

êActive (0) = 1 2 kT idle P Rx + k(E SEN + E W U B + E CCA + E CBT + E DT ) + E EM (4.12)
at the beginning (e.g. slot 0). This strategy makes êActive (n + 1) converge to the average energy for k communications in a slot.

To predict PH (n + 1) in (4.9), the energy predictor using the adaptive lter presented in Chapter 3 can be used. However, the leakage power P Leak must be characterized when a new supercapacitor is used for the energy storage since leakage power has a capacitance eect. In this section, a new approach to design an EM-PM without characterizing the leakage power is proposed. Let us consider (3.13), by dividing both sides by T S (n), we have

PH (n) = ẽActive (n) ηT S (n) + P Sleep η + ẽS (n) -ẽS (n -1) T S (n) + P Leak (4.13)
By moving P Sleep η and P Leak to the left and then, multiplying both sides by η, we achieve

η( PH (n) -P Leak ) -P Sleep = η [ẽ S (n) -ẽS (n -1)] + ẽActive (n) (4.14)
The only dierence between the denominator of (4.9) and (4.14) is PH (n+1) and PH (n).

Therefore, if we assume that the harvested power in the next slot ( PH (n+1)) is the same as the harvested power in the previous slot ( PH (n)), we have:

T W I (n + 1) = [ê Active (n + 1) -ηẽ Bud (n)] T W I (n) η [ẽ S (n) -ẽS (n -1)] + ẽActive (n) (4.15)
This result shows the simple implementation of the PM. At the beginning of the next slot, the PM only needs to read the voltage of the StoreCap (V S (n)) and based on the consumed energy in previous slot (ẽ Active (n)), the next wake-up interval is computed.

The prediction errors for both êC (n+1) and PH (n+1) are implicitly included in ẽBud (n+ 1). For instance, if the real harvested power PH (n + 1) is the same as PH (n + 1) but the consumed energy ẽC (n + 1) is less than êC (n + 1). It means that in the slot (n + 1), the total consumed energy is less than the harvested energy due to the error when predicting êC (n + 1). The amount of energy êC (n + 1) -ẽC (n + 1) makes V S increase (V S (n + 1) > V S (n)). Therefore, from (4.6), the node has a negative budget energy, which is used to increase its QoS in the slot (n + 2). When the error occurs at both PH (n + 1) and êC (n + 1), the WSN node does not evaluate each error as it is included when considering the budget energy. the EM-PM independent of harvesters and therefore, it can be extended to several kinds of energy sources such as light, heat or wind energy. Another advantage in PID-PM and EM-PM is that the leakage energy for supercapacitor-based energy storage does not need to be characterized. However, both PMs can provide an invalid T W I due to the noise when reading V S through the ADC channel of the wireless node. To reduce this problem, three samples of V S are read when the PM is activated and, an average value V S is used to evaluate the next wake-up interval. Therefore, the consumed energy of the PID-PM can be estimated as follows:

E P ID = 3E SEN + E CBT (4.16)
where 3E SEN represents the energy for reading V S and E CBT is the overhead computations of the PID-PM. Meanwhile, the consumed energy of EM-PM can be estimated as:

E EM = 3E SEN + 2E CBT (4.17)
where 3E SEN stands for the energy when reading V S , which is used as the input to both energy monitor and the PM. The second term in (4.17) is the consumed energy for when executing the energy monitor and the PM. It is also found from Table 4.1 that the variation of V S is increased proportionally to k. When k = 5, the PM has a fast response to the change of harvested energy and therefore, V S is more stable compared to higher values of k. Moreover, as there is more occurence of the PM, the total number of transmitted packets is reduced when k = 5.

However, this eect is minor as the energy consumed by the PM is negligible compared to the energy for wireless communications during a slot. When k = 10, E EM represents only around 0.9% of ẽC (n). Therefore, the choice of k is only required to balance the variation of V S . If the energy source has a high variation (e.g. outdoor solar energy), k should be low in order to follow the change of harvested energy. Otherwise, higher value of k is suitable for a low variation energy source (e.g. indoor light energy). For the next experiments, k = 10 is used as it presents a good tradeo between the reactiveness and overhead computations of the PM. Finally, V S has a higher variation when using PID-PM instead of EM-PM. The reason is the trial-and-error phase of the PID controller, which is explained in detail in the following subsections.

PID-PM with Light Energy

In this subsection, the PID-PM is implemented on a PowWow platform equipped with two PVs in size of 4x6cm. This node acts as an ED sending a packet to the BS every wake-up. Fig. 4.4 shows the voltage of the StoreCap (V S ) according to the wake-up interval (T W I ) of the node over time. At the beginning of the experiment, the default T W I is 1s, V Ref is set to the initial voltage of the supercapacitor (around 3.86V). The high value of K p and K d causes a big step size for the next wake-up interval. After 10s for the rst adaptation, T W I = 12s and V S has the tendency to increase. Therefore, the accumulated error is positive and T W I is decreased. In the rst half, when the light condition is around 1000lux, T W I stays around 4s. In the second half, the light condition is changed to 450lux. T W I is therefore increased to 19s by the PID controller.

V S converges when T W I is around 10s. Even with abrupt changes in light condition, the proposed controller can adapt the wake-up interval to stay in ENO. However, the PID-PM takes time before converging to ENO due to its trial-and-error process. As it can be seen on Fig. 4.4, the PID-PM converges to ENO after around 1500s in the rst haft and 2000s in the second one. If the light condition is changed during convergence time, the trial-and-error may be much more longer. around V Ref . In the second half, the light condition is reduced to around 450lux and T W I seems to be converged to 9s. During the whole measurement, V S is rarely moved far from V Ref as in case of the PID-PM and quickly converges the wireless node to ENO state. Therefore, EM-PM is chosen to extend to the heat energy and is presented in following subsection.

T W I (s) V S V Ref T W I Light condtion ≈ 1000lux Light condtion ≈ 450lux

EM-PM with Heat Energy

In this subsection, the EM-PM is extended to heat energy when an ED node is equipped with two TEGs connected in parallel. Their hot surfaces are attached to a laptop charger as the heat source while cold surfaces are chosen to provide the heat spreading eect towards the heat sinks. The temperature gradient between the working PC adapter and the ambient air provides around 50mV output by two TEGs. At the beginning of our measurements, a 0.09F supercapacitor is used for the StoreCap. T W I (s) seems to be more heat from TEG and T W I is around 4s in average. Moreover, due to bigger capacitance, V S is more stable than the case shown in Fig. 4.6. This behavior can be amenable for monitoring the health of industrial engines by exploiting the wasted heat when they are running. Real time detection and diagnosis of patient disease is also a promising application for our EM-PM.

T W I V Ref V S

Conclusions

The power manager is the heart of energy harvesting WSNs. Its main goal is to adapt the system to ENO. In this chapter, PID controller-based PM (PID-PM) and Energy

Monitor-based PM (EM-PM) provide practical adaptations to match harvested and consumed energy in a dynamic environment. It is amenable to monitoring applications with solar cells in industrial or hospital environments where indoor lights are switched on all the time. While the PID-PM only considers the available energy in the supercapacitor used as the storage device (StoreCap), the EM-PM exploits from a simple energy monitor to provide a short convergence time. Our measurements show that the controller can achieve stable voltage to power the PowWow node for a long period of time. Both Chapter 5

Power Manager for Periodic Energy Sources

Introduction

In this chapter, a set of novel power managers (PMs) is designed for an EH-WSN node when it is powered by a periodic energy source such as: outdoor solar energy or indoor light energy in homes or oces. In these kinds of energy sources, harvested energy is only available during energy harvesting interval (T EI ) and there is no more harvested energy in the next non-energy harvesting interval (T N EI ). This behavior is repeated every cycle T C = T EI + T N EI as presented in Section 2.6. In order to operate continuously during a whole cycle T C , the PM must have a strategy to save energy during T EI and then, uses it in the next T N EI . Therefore, the voltage of a supercapacitor which is used for the secondary storage (StoreCap) is increased during T EI and decreased during T N EI . In order to satisfy the ENO condition, which is the primary goal of the PM for EH-WSNs, its voltage at the end of T N EI has to be equal to the value at the beginning of a cycle.

Not only satisfying the ENO condition, the secondary goal of PMs proposed in this chapter is to balance the Quality of Service (QoS) of an EH node during the whole cycle T C . This feature is really useful for EH-WSNs based monitoring applications, which usually require regular data tracking. This is the improvement of our PMs compared to state-of-the-art PMs proposed in [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF] or [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF]. These PMs only satisfy ENO but do not take into account the QoS between T EI and T N EI . Therefore, these PMs usually ED( 1)

ED(2)

Relay Device (RD) End Device (ED) Base Station (BS)

Wire link Wireless link ) is used to ensure that whenever an ED wakes-up, it receives an appropriate WUB and then, sends a data packet to the BS. However, in a multi-hop EH WSN depicted in Fig. 5.1, it is impractical for a relay device (RD) to send its WUB every 50ms. Due to the limitation of radio range, an ED must forward its packet to a RD and then, the RD sends this packet to the BS. To increase the data rate between the ED and RD, a partial synchronous protocol is required instead of an asynchronous protocol as RICER. The wake-up interval of the ED must have a low variation in order to keep the synchronization with the RD. For instance, when the wake-up intervals of the ED and RD are 10s and 5s, respectively and they are synchronized to each other. Therefore, whenever the ED wakes-up, it can meet the RD to forward its packet. Unfortunately, the harvested energy of ED can be reduced and its wake-up interval must be increased to 12s for instance.

However the wake-up interval of the RD can be still 5s. In this case, the synchronization between them is lost for at least 60s (ve wake-up times of the ED). If there is clockdrift, this period can be longer and therefore, reduces the overall data rate between ED and RD. A better solution is to keep a 10s wake-up interval of the ED or increase it up to 15s, which is a multiple of 5s. This strategy will keep the synchronization among many EH nodes by reducing the variation of their wake-up period. Following this goal, a new PM, named Wake-up Variation Reduction Power Manager (WVR-PM), is proposed.

Its main core is the PEO-PM and some simple components (such as an Exponentially

Weighted Moving Average lter (EWMA) and a uniform quantizer) are added to reduce the variation of the wake-up interval. Moreover, a new Synchronized Wake-up Interval MAC protocol (SyWiM) is also proposed to take advantage of the fact that the wake-up interval of the node is a multiple of a quantization step size (e.g. 5s). The protocol stack of SyWiM is similar to RICER shown in Fig. 2.11. However, after receiving a data packet, the transmitter responses an acknowledgement (ACK) which includes the synchronization time as proposed in WiseMAC [START_REF] El-Hoiydi | Wisemac: An ultra low power mac protocol for multi-hop wireless sensor networks[END_REF]. Based on this information, the transmitter can wake-up at the right time to receive a WUB from the receiver.

The rest of this chapter is organized as follows. In Section 5.2, an overview of stateof-the-art PMs is presented. The PEO-PM and WVR-PM are proposed in Section 5.3.

Then, the SyWiM protocol is presented in Section 5.4. Experimentations performed on OMNET++ simulator are presented in Section 5.5. Finally, the chapter ends with conclusions.

State-of-the-Art Power Managers

Kansal et al. [START_REF] Hsu | Adaptive duty cycling for energy harvesting systems[END_REF] proposed a low complexity PM (KAN-PM) for dynamically adapting the duty cycle of a wireless node by considering the predicted harvested energy. Their approach takes advantage of the periodic energy source when photovoltaics (PVs) are used in an outdoor environment. Indeed, the harvested energy can be predicted based on previous samples. A cycle lasting for a day is divided into slots of the same duration (e.g. 30 minutes) and adaptation calculations are performed at the end of each slot.

To ensure ENO condition, the residual energy (the dierence between predicted energy and real harvested energy) of previous slot is used to adapt the duty cycle of future slots. However, the initial duty cycle of slots is determined at the beginning of a day Chapter 5. Power Manager for Periodic Energy Sources 78 and is only based on the harvested energy of a previous day. Therefore, when there is a change from a sunny to a cloudy day, or vice versa, it has been demonstrated in [START_REF] Piorno | Prediction and management in energy harvested wireless sensor nodes[END_REF] that the PM performs poorly. Moreover, the objective of KAN-PM is only to maximize instead of balancing the QoS during a whole day. As a consequence, the EH-WSN node usually has a high QoS during day-time, when there is plenty of harvested energy, but very low QoS during night-time, when there is no more harvested energy. Another disadvantage of KAN-PM is that its adaptations do not take into account the State-of-Charge (SoC) of the storage device. Therefore, if the node is deployed with a minimum SoC and the energy predictor using an Exponential Weighted Moving Average (EWMA) lter produces a negative error, the SoC can be less than the minimum level after a day and the node operations must be interrupted. To overcome this problem, EH-WSN nodes using KAN-PM are usually charged to a high level before their deployment. The ENO concept is extended in [START_REF] Moser | Adaptive power management in energy harvesting systems[END_REF] by using a class of linear programs to model various constraints in energy harvesting WSNs such as the duty cycle, the sensing rate or the local memory access. In this approach, the PM is able to maximize the minimum duty cycle of the node, which presents an idea to balance QoS during a day. However, solving the optimization problem in their approach requires high complexity in terms of running time and energy consumption [START_REF] Moser | Adaptive power management in energy harvesting systems[END_REF]. Therefore, most of the computation overhead is moved to an oine computer before a real sensor node implementation.

Castagnetti et al. [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF] presented two PMs, the Open-Loop (OL-PM) and Close-Loop (CL-PM) which improve the throughput of a WSN node up to 50% compared to [START_REF] Hsu | Adaptive duty cycling for energy harvesting systems[END_REF].

Simulation results are based on the TI EZ430, a real solar energy harvesting WSN platform with a solar cell in size of 2.25in x 2.25in. The harvested energy is approximated by a function of the light intensity using an illuminance sensor (expressed in lux). Meanwhile, the consumed energy is the average current delivered from the battery during a period and is computed independently for each activity of the WSN node (e.g. sensing, RF transmission). Their approach takes advantage of a dynamic adaptation period (instead of a xed one as KAN-PM) to trade-o between the computation overhead and the reactivity of the PM. Moreover, the battery SoC is considered in CL-PM to increase the QoS during the night-time while still ensuring the SoC is always greater than a minimum value. However, the next wake-up interval T W I of positive energy PM in CL-PM is computed by a ceil function of T W I produced by an energy neutral PM. As this function always returns a higher wake-up interval than the real neutral value, there is always a small part of harvested energy that is used to charge the battery. Therefore, CL-PM provides very high QoS during day-time but very low QoS during the night-time, especially when the node is deployed with a low initial SoC. In this case, the voltage of the battery is lightly increased during the day and to satisfy ENO, QoS during the night must be reduced. This problem is more crucial when considering the supercapacitor-based EH-WSN, where leakage energy is much higher than the battery and can therefore, degrade QoS. Moreover, the harvested energy is estimated from a function of the value from the illuminance sensor. Therefore, this function needs to be characterized again when the solar cells are changed.

In this chapter, our periodic power manager (PEO-PM) not only satises the ENO condition, but also balances the QoS during a whole cycle of a periodic energy source (e.g. light energy in an oce). The concept of dynamic adaptation period [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF] is also used in our approaches in order to adapt the reactiveness of the PM according to the current harvested energy. PEO-PM is able to monitor the SoC of a supercapacitor used for the energy storage by reading its voltage and therefore, ensures it always greater than the minimum value. Finally, a wake-up variation reduction power manager (WVR-PM) is proposed to reduce the variation of the wake-up interval. This WVR-PM copes with the problem of PEO-PM when EH-WSN nodes are deployed in a multi-hop EH-WSN.

Periodic Power Manager Architecture

The architecture of our periodic power manager (PEO-PM) is depicted in Fig. dynamic adaptation period, which means two consecutive slots usually have dierent durations. Therefore, instead of predicting the harvested energy, the harvested power is predicted in our PEO-PM.

According to the amount of harvested energy, either PE-PM or NE-PM is chosen. When PH (n) is greater than a pre-dened value ε, PEO-PM considers that the node is staying in T EI and therefore, PE-PM is activated. Not only adapting the wake-up interval according to the harvested energy, PE-PM also needs to buer energy for the non-energy harvesting interval (T N EI ), which begins when PH (n) ≤ ε. Then, NE-PM regularly monitors the available energy of the StoreCap as well as the remaining time of the non-harvesting period to ensure continuous operations until the next cycle of the periodic energy source, when harvested energy is available again. Moreover, in order to balance the QoS during a whole cycle, a zero energy interval (ZEI) predictor is activated to estimate T EI and T N EI . The ratio between T EI and T N EI directly impacts the next wake-up interval during energy-harvesting interval. The longer T N EI requires higher T W I during T EI to respect the same QoS during both T EI and T N EI . In this context, more harvested energy is saved during T EI for operations during T N EI . Following subsections will explain in details how PE-PM and NE-PM adapt the wake-up interval of the node powered by a periodic energy source.

Positive Energy Power Manager

A cycle of a periodic energy source lasting for T C is divided into two sub-intervals, the energy harvesting (T EI ) and non-energy harvesting interval (T N EI ) as shown in Fig. 5.3.

The PE-PM is applied during the rst interval (T EI ) while NE-PM is used during the second one (T N EI ). This subsection explains how PE-PM adapts the wake-up interval in the next slot while still saving energy for the non-energy harvesting interval.

At the end of the slot n, the energy monitor is activated to provide energy proles, including the consumed energy ẽC (n) and the harvested power PH (n), which is obtained by dividing the harvested energy (ẽ H (n)) by the slot duration T S (n). Then, the prediction of both harvested energy (ê H (n+1)) and consumed energy (ê C (n+1)) for slot (n+1) is performed. Similar to the energy monitor-based power manager (EM-PM) proposed in Chapter 4, êC (n + 1) is divided into two parts:

êC (n + 1) = êActive (n + 1) + P Sleep kT W I (n + 1) (5.1)
where êActive (n + 1) is the consumed energy for active mode and P Sleep kT S (n + 1) stands for the energy consumed in sleep mode during slot (n+1) (P Sleep is the consumed power in sleep mode). Since the node has the same k wake-up times in every slot, êActive (n+1) can be predicted from the consumed energy for active mode in the previous slot (ẽ Active (n))

by using EWMA:

êActive (n + 1) = α C êActive (n) + (1 -α C )ẽ Active (n) (5.2)
where α C ∈ [0, 1] is a weighting factor, which will be determined later in this chapter.

Meanwhile the harvested power ( PH (n + 1)) is predicted by using the AF-EP. As a consequence, the harvested energy in slot (n+1) is predicted as follows:

êH (n + 1) = PH (n + 1)kT W I (n + 1) (5.3)
In order to respect the ENO condition after a cycle of the periodic energy source, this predicted energy is not only used for the next slot (n+1), but also for a period ϕT S (n+1) in non-energy harvesting interval, with ϕ = T N EI T EI . By this way, harvested energy during T EI is buered for T N EI . Moreover, we expect the same QoS during the whole cycle of the periodic energy source. Therefore, the wake-up interval during ϕT S (n + 1) (see Fig. 5.3) is assumed to be T W I (n + 1), which is the same wake-up interval during slot (n + 1). Total consumed energy in ϕT S (n + 1) can be predicted as ϕê C (n + 1) since this period is ϕ times longer than slot (n + 1). Finally, the following constraint needs to be satised:

êH (n + 1) = 1 + ϕ η êC (n + 1) + (1 + ϕ)P Leak T S (n + 1) (5.4)
where η is the converter eciency and P Leak is the leakage power of the harvesting circuit, which have been characterized in Chapter 2. The left hand side in (5.4) is the prediction of total harvested energy in slot (n + 1). The rst term of the right hand side is the prediction of consumed energy of the wireless node, while the second term is the total leakage energy during slot (n + 1) and ϕ slots during T N EI . By applying (5.1), (

and T S (n + 1) = kT W I (n + 1) into (5.4), the next wake-up interval during T EI is achieved as:

T W I (n + 1) = (1 + ϕ) êActive (n + 1) k η PH (n + 1) -(1 + ϕ)(ηP Leak + P Sleep ) (5.5)
This result shows a simple implementation of the PE-PM, which is applied during the energy harvesting interval (T EI ) of a periodic energy source. By tracking all activities of the wireless node during slot n, ẽActive (n) is estimated and then, êActive (n+1) is predicted by using the EWMA lter dened in (5.2). Moreover, PH (n + 1) is also predicted from First, NE-PM estimates the remaining time of the non-harvesting energy duration (R(n+ 1)), which is set to T N EI at the end of energy-harvesting interval, as follows:

PH (n) = ẽH (n) T S (
R(n + 1) = R(n) -T S (n) = R(n) -kT W I (n) (5.6)
Then, the available energy for sending packets during R(n + 1) is computed from the voltage of StoreCap at the end of slot n (V S (n)) as:

E R (n + 1) = 1 2 C S (V 2 S (n) -V 2 0 ) -(P Sleep + ηP Leak )R(n + 1) (5.7)
Based on the prediction of the consumed energy for sending k packets (ê Active (n + 1)) in (5.2), the total number of packets (P kt(n + 1)) allowed during R(n + 1) is:

P kt(n + 1) = kE R (n + 1) êActive (n + 1) (5.8)
Therefore, the next wake-up interval can be determined as follows:

T W I (n + 1) = R(n + 1) P kt(n + 1) = R(n + 1)ê Active (n + 1) kE R (n + 1) (5.9)
The NE-PM is activated again after T S (n + 1) = kT W I (n + 1) to converge V S to V 0 during non-energy harvesting interval (T N EI ). At the beginning of a new cycle, V S is expected to recover to V 0 that satises the ENO condition after each cycle T C . However, NE-PM does not try to balance the wake-up interval between T EI and T N EI . The main goal of NE-PM is to satisfy the ENO condition after the non-energy harvesting interval (V S is expected to be equal to V 0 ). In fact, the average T W I during T N EI depends on V S at the beginning of the non-energy harvesting interval. Therefore, the same QoS during the whole cycle is achieved when these conditions are met:

1. ZEI predictor provides precise values of T EI and T N EI .

2. StoreCap is sucient to buer harvested energy during energy harvesting interval.

When the rst condition is false, for example, if the real non-harvesting interval is greater than the predicted value, T W I should be increased to satisfy ENO. Otherwise, if the real non-harvesting interval is less than the predicted value, T W I can be increased to maximize the QoS. Moreover, when the second condition is not satised, a part of harvested energy is discarded as there is not enough free space in the StoreCap. T W I is therefore, reduced in order to respect the ENO condition. The size of StoreCap must be meticulously chosen in the design phase, which is proposed in Chapter 2. Impacts of the ZEI and the size of StoreCap on the T W I during T N EI are explained in more details in the simulation results.

Wake-up Variation Reduction Power Manager

As explained in Section 5. wake-up interval T W I (n + 1) (see (5.5) and (5.9)). Therefore, reducing the variation of êActive (n+1) will also reduce the variation of T W I (n+1). As êActive (n+1) is predicted by using an EWMA lter (5.2), its variation is reduced when α C is close to 1. This solution is also applied to PH (n + 1), which aects the variation of T W I (n + 1) in (5.5). In consequence, another EWMA lter, whose weighted factor is α H , is added after the adaptive lter-base energy predictor (AF-EP). However, the value of α H must be a trade-o as the variation of T W I (n + 1) also presents the adaptations of the wireless node according to the harvested energy. When α H is close to 1, the variation of T W I (n + 1) is reduced but then, the wireless node has a low response to the change of the real harvested energy.

Finally, a uniform quantizer with a quantization step size ∆ is embedded to normalize the output T W I (n + 1) as shown in Fig. 5.5, and can be expressed as:

Q(T W I (n + 1)) = ∆ ⌊ T W I (n + 1) ∆ ⌋ (5.10)
where ⌊

T W I (n+1) ∆
⌋ returns the nearest integer number when dividing T W I (n + 1) by ∆.

The new wake-up interval is therefore, always a multiple of ∆. A MAC protocol can take the advantage of WVR-PM to reduce the global consumed energy.

With a quantization step size ∆, any variation of T W I lower than ∆ 2 is discarded.

Therefore, the choice of ∆ is also a trade-o between the variation of T W I and the adaptation of the wireless node. When ∆ is high, T W I is rarely changed even if the harvested energy has a big variation. Otherwise, when ∆ is low, T W I is frequently updated, which causes many variations on T W I . Moreover, it is noticed that the minimum wake-up interval is ∆ as zero is an invalid value.

Synchronized Wake-Up Interval MAC Protocol

In this section, a new protocol designed for EH-WSN node using WVR-PM, named Synchronized Wake-up Interval MAC (SyWiM), is proposed. SyWiM takes advantage of the fact that a node wake-up interval is always a multiple of ∆. The basic idea of SyWiM is that after synchronizing the timer of the transmitter with the receiver, the time domain from this point is divided in blocks of ∆. Whenever the transmitter wakesup, the receiver is also expected to wake-up around this point. SyWiM uses the theory proposed in WiseMAC to overcome the clock-drift problem so that the transmitter can receive the WUB from the receiver with a short idle listening time.

The rst communication between two nodes when they are not synchronized is done with a long idle listening time (e.g. ∆). Once there is a successful communication at time t 0 , this idle listening time can be reduced as the ACK packet contains the remaining time until the next t 0 + ∆, as this moment is a potential wake-up time of the receiver. To deal with clock drift between the transmitter and the receiver, the transmitter must wakeup sooner than the potential wake-up timer of the receiver an interval ρ. Let θ be the frequency tolerance of the clock source used in EH-WSN nodes, according to WiseMAC [START_REF] El-Hoiydi | Wisemac: An ultra low power mac protocol for multi-hop wireless sensor networks[END_REF], we have:

ρ = min(2θL, T W I (n)) (5.11)
where L is the interval from the last successful communication and current wake-up time of the transmitter, T W I (n) is the current wake-up interval of the transmitter. The result in 5.11 is explained as follows: let us assume that two nodes have a rst communication at t 0 and they have the same wake-up interval T W I (n). However, the receiver has a real frequency f (1 + θ) instead of f . Therefore, it will wake-up θT W I (n) sooner than

t 0 + T W I (n).
If the transmitter has a precise frequency f , it has to wake up at t 0 + ∆ -θT W I (n) to cover the clock drift at the receiver. However, the clock source of the transmitter can be late if its real frequency is f (1 -θ). Therefore, the transmitter must wake-up 2θT W I (n) in advance (in this example, L = T W I (n)). In the case L is very large (e.g. lost synchronization or the wake-up interval of the receiver is increased due to bad harvested energy), 2θL can be greater than T W I (n) and therefore, ρ = T W I (n).

After waking-up, the transmitter opens an idle listening window which is able to take into account the clock-drift when the clock source of the receiver and transmitter is f (1 -θ) and f (1 + θ), respectively. In this case, the receiver wakes up 2θL later than the transmitter. As the transmitter needs to wake-up 2θL sooner for the rst scenarios, its maximum idle listening (T idle ) must be at least 4θL. Similarly to ρ, a min function is used to avoid having the case 4θL is greater than T W I (n):

T idle = min(4θL, T W I (n)) (5.12)
Not only considering the frequency tolerance of hardware components (e.g. oscillators), the fault tolerance of software components is also included in this work (e.g. software timers). For instance, the real-time clock source 32,768kHz used for timers in PowWow platform has a frequency tolerance ±20 ppm (parts per million), or the peak frequency variation (δ f ) is

δ f = 20(ppm)f (Hz) 10 6
(5.13) Therefore, the maximum error of this clock source after 1s is err H = f δ f ≈ 0.02ms.

This clock source is used as the input for a software timer, which causes an interrupt every 10ms. Therefore, the value 328 is loaded to the timer counter register (TCR). However, 328 clocks is approximated to 10.0098ms or the error is 0.0098 for each 10ms. Finally, the error due to software timer after each second is err S = 0.98ms. By considering both errors of hardware and software components, θ can be set to err H + err S = 0.1. If all EH-WSNs are consistent with the same clock source and software timer conguration, err S can be removed. However, ρ and T idle must be quantized with a step size 10ms, which is the resolution of the software timer. For instance, if θ = err H , L = 20, from (5.11), ρ = 0.8ms. Unfortunately, the software timer cannot indicate this period. In this case, the node will wake up 10ms in advance instead of 0.8ms. It is noticed that

T W I (n)
is in order of second and therefore, much more longer than ρ and T idle , which are in order of ms. Based on ρ and T idle , the transmitter is able to wake up at the right time in order to receive a WUB from its receiver.

The detail of SyWiM is depicted in Fig. 5.6. After the rst successful communication at t 0 , the transmitter resets its timer and waits until its next wake-up time (t 0 +T W I (n-1)).

As the wake-up interval at each node is a multiple of step size ∆, the receiver can also wake-up at t 0 + T W I (n -1). Therefore, the transmitter wakes-up at t 0 + T W I (n -1) -ρ, where ρ = 2θT W I (n -1), and opens a short idle listening window for T idle = 4θT W I (n -1)

(ρ and T idle now are much less than T W I (n -1) in (5.11) and (5.12)). If there is no WUB, the transmitter immediately turns into sleep mode until its next wake-up time

(t 0 + T W I (n -1) + T W I (n)).
It wakes-up again with greater ρ and T idle than previous wake-up since L at this time is

T W I (n -1) + T W I (n) instead of T W I (n -1).
The rst dierence between WiseMAC and SyWiM is that WiseMAC is proposed based on TICER protocol (Transmitted Initiated Cycled Receiver) where the transmitter starts the communication by sending its WUB to the receiver. Meanwhile, SyWiM is relied on RICER protocol (Receiver Initiated Cycled Receiver) where the communication initialization is performed by the receiver. The second dierence is WiseMAC is designed for battery-based system which the aim to minimize the consumed energy of the MAC protocol by reducing the idle listening time. In WiseMAC, the wake-up interval of the transmitter can be updated according to the wake-up interval of the receiver to achieve an energy ecient communication. In contrast, the wake-up interval of the transmitter in SyWiM cannot be updated as the node has to satisfy ENO condition in EH-WSNs.

Each node has its own wake-up interval produced by the WVR-PM, which is a multiple of the step size ∆. The SyWiM takes advantage of this feature and tries to synchronize the wake-up time of the transmitter node. Whenever the transmitter wakes-up, it only needs to open a short idle listening window to communicate with the receiver.

Simulation Results on OMNET++

In this section, the validations of both PEO-PM and WVR-PM on OMNET++ network simulation are presented. Then the performance comparisons of PEO-PM and two state-of-the-art PMs in a single-hop network, including KAN-PM and CL-PM, show the improvement of QoS during non-harvesting energy interval when using PEO-PM. Finally, the WVR-PM is implemented in a multi-hop EH-WSN, which is able to increase the QoS by reducing the average consumed energy for one successful communication.

Simulation Setup on OMNET++

The single-hop network shown in Fig. 4.1 with three EDs and one BS is used to validate our periodic power manager (PEO-PM). The resolution of T W I is set to second by applying a rounding function to (5.5) and (5.9) in order to produce the nearest integer number.

Therefore, SyWiM can be applied in this simulation with a step size ∆ = 1, θ = 0.02ms as explained in Section 5.4. The value of k in (4.1) when determining the adataption period of the PM is set to 10. This value is the trade-o between the reactiveness and computations overhead of the PM which can be found in Section 4.5.2 or [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF]. In order to predict êActive (n + 1) by using the EWMA lter in (5.2), α C is commonly set to 0.5 due to its minimum prediction error [START_REF] Kansal | Power management in energy harvesting sensor networks[END_REF]. All EDs are equipped with the same harvested power prole, which is extracted from a real PowWow node powered by two PVs of size 4x6cm and setup in our oce. The threshold ε used to switch between PE-PM and NE-PM is set to 200µW. Table 2.1 is used to evaluate the consumed energy of EDs.

The rst condition to achieve a balanced QoS using PEO-PM is that the size of StoreCap must be sucient to buer harvested energy during the energy interval (T EI ). According to the measurements of the design framework presented in Section 2. M em (16-bit words): the memory footprint for the PM.

M ul: the number of multiplications required for PM computations.

B f (minute): the battery failure duration.

Gap: the dierence of W EI and W N EI compared W C , which is dened as:

Gap = W EI -W C + W N EI -W C W C (5.14)
In order to balance W EI and W N EI , Gap is expected as small as possible.

PEO-PM Simulation Results

As the role of all three EDs is the same in the single-hop network shown in Fig. 4.1, the rst ED is chosen to analyze its adaptations. Fig. 5.7 presents its wake-up interval (T W I ) and voltage of StoreCap (V S ) according to the harvested power (P H ). At the beginning of the rst day, T W I is around 20s and PE-PM is activated. Then, T W I is reduced to 10s by the PEO-PM as the harvested power keeps increasing during ve hours. After that, the harvested energy is lower and therefore, T W I must be increased up to 45s. After around ten hours, the NE-PM is activated as P H is less than ε. As the PE-PM can save a big amount of harvested energy during T EI , the ED can have a high QoS during T N EI .

In this simulation, the average T W I is around 15s during T N EI of the rst day. It can be observed that the wake-up interval during T N EI has a lower variation compared to T EI as its variation depends on êActive (n + 1) (see (5.9)). Fortunately, ED has the same k = 10 wake-up times every slot, êActive (n + 1) easily converges to the average consumed energy during active period of the node, which results in a low variation of T W I (n + 1). there is a slight decrease of the wake-up interval during T N EI on the last day.

Behaviors of the

Performance Comparison

This section presents the comparison of our periodic power manager (PEO-PM) with two state-of-the-art PMs: KAN-PM [START_REF] Hsu | Adaptive duty cycling for energy harvesting systems[END_REF] and CL-PM [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF]. Beside the ENO condition of a PM for EH-WSN, we focus on the gap between W EI and W N EI , which is required as small as possible for a monitoring application with regular data tracking. Firstly, adaptations of KAN-PM and CL-PM are presented with the same simulation setup as PEO-PM. Finally, performance comparisons with PEO-PM are summarized. The OL-PM [START_REF] Castagnetti | A framework for modeling and simulating energy harvesting wsn nodes with ecient power management policies[END_REF] is not implemented in this work as its adaptations are the same as CL-PM except that the wake-up interval is set to a maximum value during T N EI .

Chapter 5. Power Manager for Periodic Energy Sources

Kansal Power Manager (KAN-PM)

There are two processes in KAN-PM: duty cycle initialization and duty cycle adaptation.

The rst process is only performed at the beginning of a new cycle (e.g. a new day with light energy in this simulation) while the second one is carried out at the end of each slot to deal with the prediction error of harvested power using EWMA lter. The initialization process uses a full prole of harvested power on the previous day to predict the harvested power for the next 48 slots (each slot is 30 minutes and a cycle T C = 24 hours). Then, it assigns the minimum duty cycle (or maximum wake-up interval) to slots during T N EI and maximum duty cycle (or minimum wake-up interval) to slots during T EI . The total consumed energy for this assignment is estimated and then, compared with total predicted harvested energy. As we focus on indoor monitoring applications in this thesis, this consumed energy is usually greater than the predicted harvested energy.

Therefore, the duty cycle during T EI must be reduced by increasing T W I to balance the total consumed and predicted harvested energy.

The duty cycle adaptation process is activated at the end of a slot in running time to cover the error of EWMA lter, which is the dierence between the predicted and the real harvested power ( PH (n) and PH (n), respectively). When PH (n) < PH (n), the wake-up interval of future slots has to increase to compensate the negative energy error.

In KAN-PM, the slot which has the maximum predicted harvested power is rstly chosen to reduce its wake-up interval to decrease the amount of consumed energy in this slot, which is expected to be equal to the negative energy error. However, if the wake-up interval reaches the maximum value (or minimum duty cycle), the wake-up interval of the second maximum predicted harvested power slot is also reduced, and so on. This loop is only stopped when total reduced consumed energy is equal to the negative energy or all of future slots are assigned with maximum wake-up interval. On the other hand, the positive energy error occurs when PH (n) > PH (n), meaning that more energy has been harvested than predicted. In this case, KAN-PM will reduce the wake-up interval (or increase the duty cycle). A future slot which has the lowest energy is rstly decreased its wake-up interval in order to maximize the total throughput. To apply KAN-PM in our simulation, the consumed energy P C , which is a constant value in KAN-PM, is dened as

P C = ēActive + P Sleep + P Leak η (5.15)
where ēActive is the average consumed energy for one communication. When SyWiM is used for the MAC protocol, ēActive can be evaluated as

ēActive = Tidle P Rx + E SEN + E CCA + E CBT + E DT + E ACK (5.16)
where Tidle is the average idle listening and is set to 28ms. This value is obtained from the simulation performed with PEO-PM. The minimum and maximum wake-up intervals are set to 1s and 300s, respectively. Moreover, the harvested energy prole of the rst day is considered as historical values, which is required by the duty cycle initialization.

With this assumption, the duty cycle adaptation during the rst day is not activated as there is no error of the prediction method. The wake-up interval of the node on this day is used to explain the duty cycle initialization of KAN-PM.

Duty cycle adaptations of KAN-PM are presented in Fig. 5.9. It can be observed that after the wake-up interval during T N EI is assigned to 300s, the wake-up interval during T EI is reduced to 7s. Unfortunately, high wake-up intervals during T N EI also require a longer idle listening window as expressed in (5.12) with L = 300. Therefore, the real consumed energy of the node is greater than the average P C in (5.15). As the SoC of StoreCap is not considered in KAN-PM, the V S is not only less than V 0 (the deployed voltage of StoreCap) but also lower than V M in , causing a battery failure about Chapter 5. Power Manager for Periodic Energy Sources 95 15 minutes at the end of the rst day. As it can be observed from Fig. 5.9, the node is completely turned o for around B f = 15 minutes and turned on again on the second day when harvested energy is available. The rst 5 hours of the second day provide a harvested power similar to the rst day. There is a small amount of negative energy, which requires to increase T W I from 7s to 15s. After that, the harvested power is higher but KAN-PM keeps T W I =7s as on the rst day. Instead of immediately reducing T W I , the positive energy is used to increase the QoS of slots which have the lowest harvested power. Therefore, T W I of a slot at the end of the second day is reduced to 1s. In fact, the consumed energy of the node during this slot is much lower than P C due to the shortest idle listening as L =1 in (5.12). Therefore, at the end of the second day V S is much higher than V 0 = 2.0V. Finally, there are three conclusions when applying KAN-PM in indoor EH-WSNs:

1. KAN-PM has a low response to the change of harvested energy as the energy error is used to update T W I of future slots, which can be very far from current slot. This is the main drawback of KAN-PM when there is a change from a cloudy day to a sunny day. As it can be observed from Fig. 5.9, even if the harvested energy at the beginning of the third day is much more higher than the second one, KAN-PM performs QoS poorly as the second day.

2. KAN-PM does not well satisfy ENO condition after a cycle of a periodic energy source. First, the total consumed energy of the node, which mostly depends on the scenarios of MAC protocol, is considered as a constant. Moreover, the SoC of StoreCap, which is used for the energy storage, is not taken into account in the duty cycle adaptation process.

3. KAN-PM provides a big wake-up interval dierence between T EI and T N EI . It only focuses on minimizing instead of balancing the wake-up interval during a whole cycle. As it can be observed from Table 5.2, the performance during T N EI is very poor compared to T EI .

Close Loop Power Manager (CL-PM)

In contrast to KAN-PM, CL-PM has a fast response to the changes of the harvested energy. The positive energy power manager, which is a combination of a energy neutral there is around 15 minute interruption when using KAN-PM as this PM does not aware of V S .

WVR-PM Simulation Results

In this section, the multi-hop EH WSN depicted in Fig. 5.1 is implemented in OM-NET++ for our simulation. More following metrics are dened to evaluate the performance of WVR-PM:

N b_V ar : Number of variations, increased by 1 whenever the wake-up interval is changed.

Sum_V ar : Summation of variations, which is the absolute summation of the dierence between current and previous wake-up interval.

Sum_V ar

= N i=1 T W I (i) -T W I (i -1) (5.17)
R d : Average data rate, which is dened as the ratio between the size of the data packet (16 bytes) and the wake-up interval.

Idl : Average idle listening time for one successful packet, which is considered at both receiver and transmitter.

E C : Average consumed energy for one successful packet, which is considered at both receiver and transmitter.

In order to reduce variations of the wake-up interval for EH-WSN nodes, both N b_V ar and Sum_V ar are required to be as small as possible. Firstly, the single-hop connection

TW I (s) PH (µW ) VS (mV) V M ax V M in Figure 5
.12: Adaptations of ED(1) using WVR-PM with α C = 0.9, α H = 0 and ∆ = 1.

The variation of T W I is signicantly reduced during T N EI as êActive (n + 1) is very close to êActive (n) when α C = 0.9. The variation is reduced down to 60% in average. Sum_V ar over ve days simulation with α C starting from 0.5, which is the common value for an EWMA lter, and ending with 0.9. It can be observed that N b_V ar is linearly decreased when α C is increased. In average (see Table 5.5), the variation of T W I is reduced by 58.50% when α C = 0.9 (N b_V ar = 264.6) compared to α C = 0.5

(N b_V ar = 109.8). Meanwhile, Sum_V ar presenting the amplitude of the variations is reduced by 37.74%. Adaptations of ED(1) when α C = 0.9 are presented in Fig. 5.12.

When α C = 0.9, êActive (n + 1), which is used for evaluating T W I (n + 1), is very close to êActive (n), which is used for evaluating T W I (n). Therefore, the variation of T W I is rapidly reduced, especially in T N EI , when T W I mostly depends on êActive (n + 1) (see (5.9)).

Although the error of predicting êActive (n + 1) can be dierent with α C = 0.9, the NE-PM still provides a good fault tolerance. By monitoring V S , NE-PM ensures that V S is always close to V 0 = 2.0V to satisfy ENO condition after a cycle of the energy source, which is a day (24 hours) in our simulation. Therefore, α C is set to 0.9 for the rest of our simulations.

Another EWMA lter is applied to PH (n + 1), which impacts on the variation of T W I during energy harvesting interval (see (5.5)). Simulation results are presented in Fig. 5.13 when α H is varied from 0.5 to 0.9. Moreover, the average of N b_V ar, Sum_V ar and their gains over ve days compared to WVR-PM when there is no EWMA lter for PH (n + 1) are summarized in Table 5.6. It can be observed that the improvement of N b_V ar when increasing α H is not so ecient as in case of α C . The reason is PH (n) has a higher variation than ẽActive (n). While the node always has k wake-up times during a slot that results a low variation of ẽActive (n) (which is used to predict êActive (n + 1)), PH (n) is inuenced by various light conditions that brings a high variation. Therefore, increasing α H does not rapidly reduce the variation of T W I .

However, the choice of α H is a trade-o as a higher value does not rapidly reduce the variation of T W I , but will reduce the adaptability of the node according to the change of PH . When there is a drop on PH , T W I must be increased. While a high value of α H only increases T W I with a small step size, a small value makes T W I quickly increasing, 5.7. As it can be observed from these results, the quantizer provides a breakthrough to reduce the variations of T W I (N b_V ar). However, Sum_V ar is not improved so eciently as N b_V ar as the quantizer only removes the variation less than ∆ 2 but still keeps big moves of T W I . This feature allows WVR with a quantizer to adapt T W I only when there is a considerable variation of harvested energy.

Although increasing the step size can reduce the variation of T W I , the performance of the node (QoS) can be reduced. As shown in Table 5.7, the average wake-up interval when ∆ = 15, which represents the QoS, is increased by 18.31% compared to ∆ = 1 (or without the quantizer). As the minimum output of the quantizer in WVR-PM is ∆, T W I cannot be less than ∆ even if there is plenty of harvested energy. In our simulation, this problem occurs at the beginning of the last three days, as shown in Fig. 5.16 when ∆ is set to 15. Without the quantizer, T W I at the beginning of the third day can be reduced to 7s as shown in Fig. 5.13 instead of 15s in Fig. 5.16. This behavior is repeated on the next two days and therefore, degrades the global QoS. When the harvested energy is not available, the negative energy power manager (NE-PM) is activated to converge the voltage of StoreCap (V S ) to V 0 = 2.0V. Unfortunately, whenever T W I is less than ∆ = 15, the next wake-up interval is set to ∆. Therefore, to trade-o between the variation of T W I and the decrease of QoS, ∆ is set to 10 for the quantizer.

Performance between end device (ED(2)) and relay device (RD)

In this section, the WVR-PM with α C = 0.9, α H = 0. It is noticed that RD is also an EH-WSN node and therefore, cannot send the WUB as frequently as the BS (e.g. 1s). The period of this WUB depends on the wake-up interval of RD, which is adapted to satisfy ENO. Therefore, with random change of T W I provided by PEO-PM, CL-PM and KAN-PM, ED(2) cannot receive a WUB from RD.

WVR-PM overcomes this problem by forcing the change of T W I to a multiple of the step size ∆. This solution increases the probability to receive a WUB whenever ED [START_REF] Mainetti | Evolution of wireless sensor networks towards the internet of things: A survey[END_REF] wakes up. As shown in Table 5.8, the average data rate (R d ) is improved by 36.04%, 27.76% and 59.56% using our WVR-PM compared to PEO-PM, CL-PM and KAN-PM, respectively.

It is noticed that not only the throughput (R d ) is improved, the average idle listening considered at both ED(2) and RD, is also signicantly reduced. The reason is that ED [START_REF] Mainetti | Evolution of wireless sensor networks towards the internet of things: A survey[END_REF] has to perform a maximum idle listening window when it wakes-up if the RD does not wake-up at this time. Until the next wake-up time, the idle listening window is increased due to the increase of L in (5.12). With WVR-PM, the ED(2) easily receives a WUB whenever it wakes-up. Finally, the average idle listening (Idl) when using WVR-PM is reduced by 47.70%, 34.08% and 51.80% compared to PEO-PM, CL-PM and KAN-PM, respectively. As idle listening is the dominant energy consumption for communication, the average energy for one successful communication is also reduced by 28.66%, 23.90% and 45.51%.

As the main core of WVR-PM is the PEO-PM, both WVR-PM and PEO-PM are able to ensure that there is no battery failure over ve days. Although CL-PM considers the voltage of StoreCap when determining T W I , there is a battery failure duration lasting for B f =37 minutes. As the average energy for communication (E C ) is increased, the light increase of V S during the energy harvesting interval in CL-PM cannot buer enough Chapter 5. Power Manager for Periodic Energy Sources 105 energy for the next non-energy harvesting interval even if T W I is set to maximum value (300s). The battery failure when using KAN-PM is much longer as it does not consider V S when performing adaptations (B f =63 minutes).

Finally, adaptations of ED(2) are presented in Fig. 5.17. Not only satisfying ENO condition after each day, WVR-PM provides a low variation of T W I but is still able to adapt T W I according to the change of harvested power.

Conclusions

In this chapter, a periodic power manager (PEO-PM) is proposed for EH-WSNs powered by a periodic energy source (e.g light energy in an oce). As the harvested energy is only available during an interval T EI , PEO-PM provides a simple strategy for saving energy, which is used afterwards in the next interval T N EI , when there is no more harvested energy. The rst improvement of PEO-PM is that the ratio between T N EI and T EI is considered for wake-up interval adaptations during T EI . This solution provides a balanced QoS during a whole cycle of energy source and is useful for monitoring applica- B f = 0). Finally, PEO-PM has a low complexity with a small memory footprint. For all these reasons, PEO-PM can be easily implemented on a real sensor node.

However, PEO-PM performance is reduced when applied in a multi-hop EH-WSN. As the relay device (RD) cannot send its beacon (WUB) as frequently as the base station (BS), there are some over idle listenings when the end device (ED) wakes-up. This problem not only reduces the global QoS, but also increases the consumed energy of the ED. Therefore, a wake-up variation reduction power manager (WVR-PM) has been proposed in order to reduce the variation of the wake-up interval (T W I ). Moreover, Chapter 6

T
Conclusions and Perspectives

Conclusions

The limited energy and recharged cycles of batteries are crippling the design of autonomous Wireless Sensor Networks (WSNs). To overcome this issue, everlasting harvested energy and supercapacitor-based energy storage are considered as the potential solution to achieve a theoretically innite lifetime. However, to achieve this goal, a Power Manager (PM) needs to be embedded in each WSN node to respect Energy Neutral Operation (ENO), which means harvested energy is equal to consumed energy for a long period. In this thesis, global approaches for PMs embedded in supercapacitor-based energy harvesting wireless sensor networks (EH-WSN) are presented. PMs are designed independently of harvested energy sources and therefore, can be applied for light, heat or airow energy. In addition, proposed PMs are low complexity and memory footprint and therefore, can be implemented in real EH-WSN nodes. Based on predictions of both harvested and consumed energy for the next slot, the wake-up interval (T W I ) of the node is adapted to respect the energy neutral operation (ENO) condition. When ENO has been satised, the total harvested energy is equal to the consumed energy, providing an innite lifetime of the EH-WSN node.

Environmental energy sources are classied into two categories in this thesis, the continuous and the periodic sources. In the rst one, energy can be scavenged continuously without any interruption. These energy sources can be found from uorescent lights hospitals or heat emitted by industrial machines. The PM using a supercapacitor-based energy monitor (EM-PM) proposed for this kind of energy sources allows the EH-WSN node to consume energy as much as it can harvest. The voltage of the supercapacitor used for the energy storage (V S ) is kept almost constant when ENO has been satised as all harvested energy is used for maximizing the system QoS. There is no more energy for charging the supercapacitor (StoreCap) in this context. This behavior is also useful for applications which only require tracking data when the harvested energy is available such as tire pressure monitoring using vibration energy [START_REF] Roufa | Security and privacy vulnerabilities of in-car wireless networks: A tire pressure monitoring system case study[END_REF] or velocity monitoring using air ow energy passing through a tunnel [START_REF] Sardini | Self-powered wireless sensor for air temperature and velocity measurements with energy harvesting capability[END_REF]. Taking advantage of a simple energy monitor, our EM-PM provides a fast convergence to ENO compared to adaptive controllers such as PID-PM presented as well in this thesis.

When considering the second category of energy sources (periodic energy sources) in this thesis, the ambient energy is only available during energy harvesting (T EI ) interval instead of non-energy harvesting interval (T N EI ). Therefore, the proposed periodic PM (PEO-PM) is able to reserve harvested energy during T EI for perpetual operations during Based on the results in this thesis, several interesting topics can be investigated. This section provides a short discussion of future works.

Multi-Energy Sources Power Manager

Although power managers presented in this thesis are independent of energy sources, there is only one energy source connected at once for powering the wireless node in our measurements or simulations. To the best of our knowledge, there is no PM designed for multi-energy sources WSNs. The combination of dierent energy sources at the same time (e.g. light and air-ow energy) can bring more advantages, but also new challenges in EH-WSNs. The basic advantage of multi-energy sources WSNs is that more energy can be scavenged to increase the system QoS. Moreover, there seems to be a mutual complement between these sources: strong winds usually occur when the weather is bad rather than in sunny days, or during the night-time where solar energy is not available [START_REF] Liu | An ecient wind-photovoltaic hybrid generation system using doubly excited permanent-magnet brushless machine[END_REF]. However, the PM for this system must deal with the variations of total harvested energy, which can be very dierent compared to each source.

Adaptive Quantizer in Wake-Up Variation Reduction Power Manager

In the simulations with wake-up variation reduction power manager (WVR-PM), the main component to reduce the variation of wake-up interval is the quantizer. So far, the quantizer step size (∆) is a xed value before deploying EH-WSN nodes. When harvested energy is extracted from two PVs in our oce, the optimized ∆ seems to be 10. However, if the node is setup in another condition (e.g. monitoring in a supermarket) with higher harvested energy (e.g. better light condition), the best value of ∆ can be changed. Therefore, future works can provide an adaptive algorithm to determine ∆ when the network is deployed. This algorithm may have a high consumed energy due to its complexity and therefore, should be implemented at the base station node, which usually has a permanent energy supply. By learning the average harvested power during a period (e.g. a day), an optimized ∆ can be computed and then, broadcasted to the whole EH-WSN. This information is used by the SyWiM protocol at each node.

Wake-Up Radio-based MAC Protocols Chapter 6. Conclusions and Perspectives 110

When the node has satised the ENO, optimizing the consumed energy of MAC protocols is the key to increase global QoS of the system. Idle listening is the most wasted energy in asynchronous MAC protocols as the node does not know when it is allowed to transmit.

Most of the time the radio is consuming while idle listening, just waiting to awake to be reactive. A nano-watt wake-up radio receiver (WUR) in [START_REF] Magno | Smart power unit with ultra low power radio trigger capabilities for wireless sensor networks[END_REF] can be embedded in an EH-WSN node in order to drastically reduce the energy due to idle listening, which is the most signicant source of energy consumption in MAC protocols, especially with asynchronous protocols [START_REF] Bachir | Mac essentials for wireless sensor networks[END_REF]. Our works in [START_REF] Le | Ultra low power asynchronous mac protocol using wake-up radio for energy neutral wsn[END_REF] show that average energy consumption for one successful communication using TICER protocol can be reduced up to 53% when applying the WUR. However, the WUR is only simulated in a single-hop EH-WSN with a continuous energy source. Future works can extend this simulation in a multi-hop network and then, validate results on real WUR-based platforms when considering a periodic energy source. Another advantage could be achieved is to reduce the network latency, especially in a multi-hop network. As WUR is able to continuously listen to a wireless channel, the relay device (RD) can immediately forward a packet by sending a trigger signal (e.g. a wake-up beacon) to the WUR of the base station (BS) (see Fig.

5.1)

. With SyWiM protocol presented in this thesis, after receiving a packet from an ED, the RD must wait for a WUB from the BS. If there is no WUB, the RD will turn into sleep mode and wait until its next wake-up to forward this packet, thus increasing the network latency.

Recongurable Antennas in Mobility Energy Harvesting WSNs

Another approach to optimize the consumed energy is the use of recongurable antennas for WSN nodes [START_REF] Giorgetti | Exploiting low-cost directional antennas in 2.4 ghz ieee[END_REF]. Indeed, most of the current WSN systems use omni-directional antennas [START_REF] Wu | A scalable solar antenna for autonomous integrated wireless sensor nodes[END_REF]. As the output RF signals are emitted in all directions in this case, it is inecient in terms of power, range and interferences. Recongurable antennas (or directive antennas), that are able to focus the output signal beam in a reduced direction, provide a solution to overcome these issues. They can provide lower power consumption, longer range and reduce interferences, especially in a dense network. However, using recongurable antennas impacts the way WSN nodes will communicate. As a consequence, the physical, MAC [START_REF] Choudhury | Using directional antennas for medium access control in ad hoc networks[END_REF] and network (routing) protocol layers [START_REF] Huang | Achieving maximum ow in interference-aware wireless sensor networks with smart antennas[END_REF] need to be reconsidered. Moreover, the PM must be able to congure the antenna and the RF chip of the EH-WSN node according to its position or its distance from the base station. The combination of the PM and recongurable antennas opens opportunities to design ecient mobile multi-hop EH-WSNs. In that case, the node will have to dynamically control its antenna and its RF circuit according to its position as well as to the uctuating channel conditions.

4 . 6

 46 (n + 1), ẼH (n) and W (n) are updated from err(n + 1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Harvested power prole over consecutive 14 days. . . . . . . . . . . . . . . 3.8 Average error for solar energy prediction with dierent combinations of the order (p) and the step size (µ) of the adaptive lter. . . . . . . . . . . 3.9 Average error of the energy predictor when applied to wind energy in seven consecutive days. With the same order and step size as solar prediction (p = 1 and µ = 0.3), the average error is 9.1%. . . . . . . . . . . . . . . . . . 3.10 Energy prediction of harvested energy recorded by a PowWow node near a window in an oce with p = 1 and µ = 0.3. At around 80 minutes, the curtain is opened and the harvested power suddenly increases. . . . . . . . 4.1 Single-hop EH-WSN with a base station (BS) and many end device nodes (EDs). All EDs are equipped with a harvested device and a supercapacitor for energy storage. The PM adapts the wake-up interval (T W I ) of each ED to respect ENO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Power manager with dynamic adaptation period. . . . . . . . . . . . . . . . 4.3 PID controller-based power manager (PID-PM) with three components: Proportional, Integral and Derivative. Adaptive function f updates the next wake-up interval of the WSN node (T W I (n + 1)) according to accumulated error acc(n). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Voltage of StoreCap when applying PID-PM. In the rst half, T W I converges to around 4s. In the second half, the light condition is changed and T W I converges to around 10s. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Voltage of StoreCap when applying EM-PM. V S is fastly converged to V Ref as EM-PM is able to estimate both the harvested and consumed energy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Power manager performs adaptation on a real PowWow node with a thermal generator (TEG). V S is kept almost constant around V Ref , which represents the ENO condition. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Votage of the StoreCap and wake-up interval of the PowWow node when a new super capacitor 0.18F is used. . . . . . . . . . . . . . . . . . . . . . . 5.1 Multi-hop energy harvesting wireless sensor network. . . . . . . . . . . . . 5.2 Periodic power manager (PEO-PM) architecture with two main components: positive and negative energy power manager. . . . . . . . . . . . . . 5.3 Positive Energy Power manager. The harvested energy is not only used for the next slot, but also for ϕ slots during T N EI . . . . . . . . . . . . . . . 5.4 Negative energy power manager (NE-PM) for non energy harvesting interval (T N EI ). The next wake-up interval (T W I (n + 1)) is estimated from the remaining duration of non energy harvesting and current energy in StoreCap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Wake-up variation reduction power manager (WVR-PM) architecture. Its main core is the periodic power manager (PEO-PM). An EWMA lter and a uniform quantizer are added in order to reduce the variation of the wake-up interval. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Synchronized wake-up interval MAC protocol for low wake-up variations multi-hop EH-WSNs (SyWiM). . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Adaptations of an EH ED node using PEO-PM. The voltage of the Store-Cap (V S ) is recovered to around V 0 = 2V after a day to respect the ENO condition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Adaptations of ED when C S = 0.9F. As a part of harvested energy is discarded once V S = V M ax , T W I has to be increased in order to satisfy ENO condition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Adaptations of ED using PM proposed in [100] (KAN-PM). As KAN-PM does not take into account SoC, there is a short battery failure, B f = 15 (minutes). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Adaptations of ED using PM proposed in [57] (CL-PM). Taking into account SoC of StoreCap, ENO condition is respected without any battery failure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.11 N b_V ar and Sum_V ar with dierent values of α C

5 . 16 1 Introduction 1 . 1

 516111 Adaptations of ED(1) using WVR-PM with ∆ = 15. . . . . . . . . . . . . . 5.17 Adaptations of ED(2) using WVR-PM with ∆ = 10. . . . . . . . . . . . . . Chapter Internet of Things and Wireless Sensor Networks

Figure 1 . 1 :

 11 Figure 1.1: Predictive scheme in dynamic power manager. Waste energy occurs when the system turns to idle too late or wakes-up too soon.

Figure 1 . 2 :

 12 Figure 1.2: Two tasks are scaled by using DVFS technique.

Figure 1 . 3 :

 13 Figure 1.3: Duty cycle power manager. The average consumed energy of the node can be adapted by changing its wake-up interval according to the harvested energy.

  First, an adaptive controller Proportional Integral Derivative (PID) is used as the PM, named PID-PM, to converge the voltage of StoreCap to ENO condition. PID-PM adapts the wake-up interval of the node (T W I ) only according to the change of current voltage. This

Chapter 5 :

 5 Power Manager for Periodic Energy Sources. In contrast to energy sources assumed in Chapter 4, periodic energy sources only provide energy during an energy harvesting interval (T EI ) instead of during the next non-energy harvesting interval (T N EI ). This behavior is repeated every cycle T C = T EI + T N EI . To satisfy the ENO after a cycle T C of the periodic energy sources, the PM has to buer harvested energy during T EI and then, uses it during T N EI . Therefore, a PEriOdic power manager (PEO-PM) is proposed and validated with light energy in our oce. Not only satisfying ENO, PEO-PM is able to balance the Quality of Service (QoS) during the whole cycle T C .

Figure 2 . 1 :

 21 Figure 2.1: Maximum Power Point (MPP) of a PV in three dierent irradiance conditions (G)[START_REF] Didioui | Harvwsnet: A co-simulation framework for energy harvesting wireless sensor networks[END_REF]. MPPT mechanism is designed to provide the highest power from PVs.

Figure 2 . 2 :

 22 Figure 2.2: Typical setup for thermal-powered WSN.

Figure 2 . 3 :

 23 Figure 2.3: Experiment setup for wind-powered WSN. A diode bridge is used to convert the voltage generated by the wind turbine from AC to DC.

Figure 2 . 4 :

 24 Figure 2.4: Single-path architecture for EH-WSN.

Figure 2 . 5 :

 25 Figure 2.5: Dual-path architecture for EH-WSN. Energy from harvesters is distributed to both primary and secondary storages by the energy ow controller.

Figure 2 . 7 :

 27 Figure 2.7: MESC hardware architecture. The harvested energy is normalized by the energy adapter and is distributed to OutCap and StoreCap by the energy ow controller. The comparator enables powering the system when there is sucient energy in OutCap. Both V O and V S are connected to an ADC for monitoring the state-ofcharge of OutCap and StoreCap for further processing (e.g. booting, adaptations ...).

Figure 2 . 8 :

 28 Figure 2.8: System operation diagram of MESC (time and voltage are not scaled).During the harvesting energy interval, the WSN node is kept shut down until V O reaches V T HR . During the non-harvesting energy interval, the WSN node is turned o whenV O decreases below V T HF .

  and Coilcraft step-up transformers (LPR6235) are dedicated to low-powered systems and therefore, can be used in EH-WSNs. Two separate capacitors are used for the energy storage and are charged by the energy ow controller. The output capacitor (OutCap) is connected to a DC/DC converter to provide the voltage supply to the wireless node. The second one is a supercapacitor (StoreCap) which acts as the main energy storage. OutCap has charging priority, but small capacitance compared to the StoreCap. As a result, this design provides a fast booting capability due to short charging time of the small capacitor. Finally, to avoid the decreased radio range caused by the decreased voltage of the OutCap, a DC/DC converter is used to provide a constant 3.3V output which is commonly compatible with WSN nodes. Chapter 2. Energy neutral design framework for autonomous WSN Coordination of the control signals to operate the system is explained in Fig. 2.8. At

Figure 2 . 9 :

 29 Figure 2.9: Hardware design of MESC.

  2.9. The LTC3108 component is used as an energy ow controller to drive the energy extracted from harvesters. It provides a complete solution for EH-WSNs with two outputs. The rst output is connected to OutCap for powering the WSN node while the second one is connected to StoreCap for energy storage. The energy adapter is designed as a multi-choice footprint which can be soldered by a resistor in case of PVs or a step-up transformer in case of TEGs. The low-powered MAX917 component is used for the comparator. Its falling threshold (V T HF ) can be chosen as the minimum input voltage of the DC/DC converter, while the rising threshold (V T HR ) is in the range of [V T HF , V Re ]. In the current version of MESC, V T HF = 1.8V and V T HR = 2.9V. Determining C O and C S in order to satisfy ENO and maximize QoS is described in the next section. To provide harvesting energy aware capability, a low complexity software-based energy monitor will be proposed in Chapter 3. By reading the voltage of the StoreCap and tracking all activities of the WSN node, the harvested energy as well as the consumed energy can be estimated. A

2 . 10 .Figure 2 . 10 :

 210210 Figure 2.10: Communication of the WSN node during a cycle of the periodic source, including the harvesting and non-harvesting energy interval (T EI and T N EI ). However, the harvested energy is only available during T EI .

Figure 2 . 11 :

 211 Figure 2.11: RICER-based protocol for communications between two nodes. When waking up, the transmitter waits for a wake-up beacon (WUB) from the receiver. After receiving a WUB, the transmitter sends a data packet (DT) after Clear Channel Assessment (CCA) and Calculation Before Transmission (CBT).

2. 7

 7 Experimental Result 2.7.1 Measurements Setup Our experimentations to validate the system design model are performed using the Pow-Wow WSN platform [86] which is based on the MSP430 microcontroller and the CC2420 RF transceiver. MESC is congured to provide V Re = 3.3V and V RF = 3V The comparator is designed to provide V T HR = 2.8V and V T HF = 1.8V. The maximum voltage which is allowed for C S is set to V M ax = 5.2V. T EI = 36000s and T N EI = 50400s for a spring day of 24 hours.

Chapter 2 .

 2 Energy neutral design framework for autonomous WSN[START_REF] Benini | A survey of design techniques for system-level dynamic power management[END_REF]

Figure 2 . 12 :

 212 Figure 2.12: Booting process with light energy in an oce.

Figure 2 .

 2 Figure 2.13: V O with thermal energy. After initializing the RF transceiver, the thermal energy is only enough to charge V O to V Re . More harvested power is required to satisfy ENO of EH-WSN node.

Fig. 2 .

 2 Fig. 2.13 shows the voltage of the OutCap when a PowWow node is used with a TEG.

T

  N EI = 2000s T EI = 1000s

Figure 2 .Figure 2 . 15 :

 2215 Figure 2.14: V S of a PowWow node powered by two PVs when T W I = 52s. The harvested energy buered during T EI = 1000s is used in the next 2000s when the environmental energy is no more available (T N EI ).

PFigure 2 . 16 :

 216 Figure 2.16: Average wake-up interval (T W I ) according to dierent harvested power (P H ) and storage capacitance (C S ) with a periodic energy source on OMNET++ simulation.

Fig. 3 .Figure 3 . 1 :Figure 3 . 2 :Figure 3 . 3 :

 3313233 Figure 3.1: Energy monitor in the dual-path architecture.

Figure 3 . 4 :

 34 Figure 3.4: Generic model for the supercapacitor.

Chapter 3 .Figure 3 . 5 :

 335 Figure 3.5: Harvested and consumed energy proles in a PowWow node.

Fig. Chapter 3 .

 3 Energy monitor and energy predictor for Supercapacitor-based Storage 50 3.5 presents ẽH (n) and ẽC (n) obtained with a PowWow node equipped with two 4x6cm

  ẽH (n) : harvested energy during slot n provided by the energy monitor. ẼH (n) : vector of p historical values, Chapter 3. Energy monitor and energy predictor for Supercapacitor-based Storage 52 ẼH (n) = [ẽ H (n), ẽH (n -1), ..., ẽH (n -p + 1)].

Chapter 3 .

 3 Energy monitor and energy predictor for Supercapacitor-based Storage 53 Signal at the start of slot n +1 Signal at the end of slot n +1 err(n+1).

Figure 3 . 6 :

 36 Figure 3.6: Energy predictor using adaptive lter architecture. Historical harvested energy prole is kept in a vector ẼH (n) to predict êH (n + 1) at the beginning of slot (n + 1). Until the end of slot (n + 1), ẼH (n) and W (n) are updated from err(n + 1).

Figure 3 . 7 :

 37 Figure 3.7: Harvested power prole over consecutive 14 days.

Chapter 3 .Figure 3 . 8 :

 338 Figure 3.8: Average error for solar energy prediction with dierent combinations of the order (p) and the step size (µ) of the adaptive lter.

Chapter 3 .Figure 3 . 9 :

 339 Figure 3.9: Average error of the energy predictor when applied to wind energy in seven consecutive days. With the same order and step size as solar prediction (p = 1and µ = 0.3), the average error is 9.1%.

Chapter 3 .Figure 3 . 10 :

 3310 Figure 3.10: Energy prediction of harvested energy recorded by a PowWow node near a window in an oce with p = 1 and µ = 0.3. At around 80 minutes, the curtain is opened and the harvested power suddenly increases.

2 ,Figure 4 . 1 :

 241 Figure 4.1: Single-hop EH-WSN with a base station (BS) and many end device nodes (EDs). All EDs are equipped with a harvested device and a supercapacitor for energy storage. The PM adapts the wake-up interval (T W I ) of each ED to respect ENO.

Figure 4 . 2 :

 42 Figure 4.2: Power manager with dynamic adaptation period.

Chapter 4 . 63 4. 2 .

 4632 Duty-cycle power manager for continuous energy sources Therefore, the adaptation period of the PM is

Figure 4 . 3 :

 43 Figure 4.3: PID controller-based power manager (PID-PM) with three components: Proportional, Integral and Derivative. Adaptive function f updates the next wake-up interval of the WSN node (T W I (n + 1)) according to accumulated error acc(n).

Chapter 4 .

 4 Duty-cycle power manager for continuous energy sources 68 Similar to the PID-PM, adaptations mostly based on the voltage of the StoreCap makes

4. 5

 5 Experimental Results4.5.1 Measurements SetupOur measurements are performed using PowWow platforms which are based on the MSP430 microcontroller and on the CC2420 RF transceiver. The single-hop network shown in Fig.4.1 is used to illustrate a monitoring application. Communications among WSN nodes are based on RICER protocol depicted in Fig.2.11. The BS, connected to a host computer, sends a wake-up beacon each T b = 50ms and the maximum idle listening time at each ED is set to T idle = 52ms to deal with clock drift. The PM is implemented at each ED, which is a harvesting node. A supercapacitor of 0.9F is used for the energy storage and is charged to around 4V before deployment. The resolution of T W I is in second and is set to 1s at the beginning.Parameters of the PID-PM are determined by a manual tuning method. First, K i and K d are set to zero and K p is chosen to provide fast convergence. Then K i and K d are estimated from some trial-and-errors. In this measurement, K p = 5750, K i = 5 and K d = 3500 are used. K i is small in order to reduce the eect of the integral error when V S has reached the desired voltage V Ref . Meanwhile, α is set to 0.6 when predicting the consumed energy in EM-PM.4.5.2 Tuning Reactiveness of PM through OMNET++The harvested energy prole shown in Fig.3.5, which is extracted from a real PowWow node, is used as the input of a simulation program on OMNET++ to determine the k parameter in (4.1), which represents the reactiveness of the PM. The single-hop network shown in Fig.4.1 with two harvesting nodes is used in this simulation. The EM-PM is implemented at the rst ED while the PID-PM is implemented at the second one. The voltage of StoreCap (V S ) for dierent values of k is summarized in Table4.1. It can be observed that both PMs well adapt the harvesting node as the average value of V S during the simulation is very close to the desired ENO voltage (V Ref ), which is set to 4V. The PID-PM provides a better QoS than EM-PM as it has a lower complexity and therefore, a lower consumed energy as shown in (4.16) and (4.17).

  Light condition ≈ 450 lux Light condition ≈ 1000 lux

Figure 4 . 4 :

 44 Figure 4.4: Voltage of StoreCap when applying PID-PM. In the rst half, T W I converges to around 4s. In the second half, the light condition is changed and T W I converges to around 10s.

Figure 4 . 5 :

 45 Figure 4.5: Voltage of StoreCap when applying EM-PM. V S is fastly converged to V Ref as EM-PM is able to estimate both the harvested and consumed energy.

4. 5 . 4 Figure 4 . 6 :

 5446 Figure 4.6: Power manager performs adaptation on a real PowWow node with a thermal generator (TEG). V S is kept almost constant around V Ref , which represents the ENO condition.

Fig. 4 .

 4 Fig.4.6 presents the adaptation of T W I according to the change of V S . At the beginning of the experiment, the default T W I is set to 5s. As k = 10, the PM performs the rst adaptation after 50s and T W I is reduced to 4s for the next 40s, then T W I = 3s as V S is increasing. In the next 30s, V S slightly decreases, but T W I is reduced to 2s as V S is higher than V Ref . The budget energy now is positive and therefore, the numerator in (4.15) is signicantly reduced. After that, T W I is equal to 3s as long as the PM detects a considerable drop of V S . During the experiment, V S is kept almost constant around V Ref . It means that our PM is able to match the harvested and consumed energy over a long period to keep the PowWow node running in ENO.

Figure 4 . 7 :Fig. 4 . 6 .

 4746 Figure 4.7: Votage of the StoreCap and wake-up interval of the PowWow node when a new super capacitor 0.18F is used.

Chapter 4 .

 4 Duty-cycle power manager for continuous energy sources 74 PID-PM and EM-PM can moreover be extended to other energy sources because their adaptations are mostly based on the voltage of the StoreCap.

Figure 5 . 1 :

 51 Figure 5.1: Multi-hop energy harvesting wireless sensor network.

Figure 5 . 2 :

 52 Figure 5.2: Periodic power manager (PEO-PM) architecture with two main components: positive and negative energy power manager.

Figure 5 . 3 :

 53 Figure 5.3: Positive Energy Power manager. The harvested energy is not only used for the next slot, but also for ϕ slots during T N EI .

Figure 5 . 4 :

 54 Figure 5.4: Negative energy power manager (NE-PM) for non energy harvesting interval (T N EI ). The next wake-up interval (T W I (n + 1)) is estimated from the remaining duration of non energy harvesting and current energy in StoreCap.

Figure 5 . 5 :

 55 Figure5.5: Wake-up variation reduction power manager (WVR-PM) architecture. Its main core is the periodic power manager (PEO-PM). An EWMA lter and a uniform quantizer are added in order to reduce the variation of the wake-up interval.

Chapter 5 .DTFigure 5 . 6 :

 556 Figure 5.6: Synchronized wake-up interval MAC protocol for low wake-up variations multi-hop EH-WSNs (SyWiM).

Figure 5 . 7 :

 57 Figure 5.7: Adaptations of an EH ED node using PEO-PM. The voltage of the StoreCap (V S ) is recovered to around V 0 = 2V after a day to respect the ENO condition.

Figure 5 . 8 :

 58 Figure 5.8: Adaptations of ED when C S = 0.9F. As a part of harvested energy is discarded once V S = V M ax , T W I has to be increased in order to satisfy ENO condition.

  node during the rst day are repeated on the next four days. During T EI , the wake-up interval of the node (T W I ) presents an inverse shape with the harvested power (P H ), which shows fast adaptations of PE-PM according to P H . Meanwhile, during T N EI , T W I is adapted by monitoring the voltage of StoreCap (V S ). Therefore, after a day, which is the cycle of the energy source, V S is recovered closely to V 0 = 2V , which is the voltage of StoreCap at the beginning of the cycle. PEO-PM ensures continuous operations of the ED during ve days without any interruptions (B f = 0). However, to balance the average T W I , which actually represents the average QoS, the size of StoreCap (C S ) must be sucient to buer all reservation energy when NE-PM is activated. This simulation is restarted with dierent values of C S and the results are gathered in

Chapter 5 .Figure 5 . 9 :

 559 Figure 5.9: Adaptations of ED using PM proposed in[START_REF] Hsu | Adaptive duty cycling for energy harvesting systems[END_REF] (KAN-PM). As KAN-PM does not take into account SoC, there is a short battery failure, B f = 15 (minutes).

V 0

 0 which means that most of the harvested energy is consumed by the wireless node to maximize the throughput. While PEO-PM balances the total of packets during a whole cycle T C , KAN-PM and CL-PM send plenty of packets during T EI but very limited packets during T N EI . However, the overall packets during ve consecutive days is the same, which results the same average wake-up interval W C .When considering the memory footprint, 11 words are required for PEO-PM, including 10 words for a LUT in the energy monitor and 1 word for the energy predictor using the adaptive lter. Meanwhile, KAN-PM needs 48 words for saving historical values used by the duty cycle initialization. Without the energy predictor, CL-PM only uses 10 words for the LUT to estimate the consumed energy of the wireless node. Among these PMs, KAN-PM has the highest number of multiplications (M ul = 28) due to the complexity of the duty cycle adaptation process. With a simple positive energy power manager (the combination of an energy neutral power manager and a ceil function), CL-PM only requires 9 multiplications. Meanwhile, the positive energy power manager in PEO-PM is more complex than CL-PM as it requires 16 multiplications. Finally, both PEO-PM and CL-PM ensure continuous operations of the node during ve days without any battery failure as the SoC of StoreCap (V S ) is considered in their adaptation. In the contrast,

Figure 5 . 11 :

 511 Figure 5.11: N b_V ar and Sum_V ar with dierent values of α C (α H = 0, ∆ = 1).

Chapter 5 .Figure 5 . 13 :

 5513 Figure 5.13: N b_V ar and Sum_V ar with dierent values of α H (α C = 0.9, ∆ = 1).

TFigure 5 . 14 :

 514 Figure 5.14: Adaptations of ED(1) using WVR-PM with α H = 0.7.

Figure 5 . 15 :

 515 Figure 5.15: N b_V ar and Sum_V ar with dierent values of ∆ (α C = 0.9, α H = 0.7).

7

 7 and adaptations of ED(1) in this case are presented in Fig. 5.13. In average, there are 75.6 variations of T W I over ve days of simulation, which is improved by 31.13% compared to a solution without EWMA for PH (n + 1), which has 109.8 variations. Finally, measurements with three dierent values of step size (∆ = 5, 10 and 15) are performed. Simulation results of N b_V ar and Sum_V ar for each day are presented in Fig. 5.15 while their average values over ve days are summarized in Table

Figure 5 . 16 :

 516 Figure 5.16: Adaptations of ED(1) using WVR-PM with ∆ = 15.

7 Figure 5 . 17 :

 7517 Figure 5.17: Adaptations of ED(2) using WVR-PM with ∆ = 10.

  tions which require regular data tracking. Although the QoS is reduced by 46.93% and 50.92% during T EI , PEO-PM improves the QoS during T N EI up to 84.89% and 83.07% compared to KAN-PM and CL-PM, respectively. The second improvement of PEO-PM comes from CL-PM. By considering the SoC of StoreCap (V S ), PEO-PM ensures continuous operations of the EH WSN node without any interruption (no battery failure,

T

  N EI . The ENO condition in periodic energy sources is respected after a cycle T C = T EI + T N EI . Moreover, our PEO-PM aims to balance the QoS during a whole cycle T C instead of a high QoS during T EI but a low QoS during T N EI as state-of-the-art PMs. The Gap dened in(5.14) which represents the dierence of QoS between T EI and T N EI is reduced up to 98% when using PEO-PM while maintaining the same average QoS. This improvement is amenable for monitoring applications which require regularly tracking data such as temperature monitoring in oces.Not only satisfying ENO, maximizing QoS in a multi-hop EH-WSN is nally addressed in this thesis. Regularly updating T W I can decrease the global QoS due to synchronizations among EH nodes. Therefore, a new approach, named Wake-up Variation Reduction PM (WVR-PM), is presented. The main core of WVR-PM is PEO-PM. However, two Exponential Weighted Moving Average lters and a quantizer are used to reduce variations of T W I . By this way, T W I is kept the same for a long period, guaranteeing successful wireless communications. Moreover, a new MAC protocol, named synchronized wake-up interval MAC (SyWiM), taking advantage of low wake-up interval variations is also proposed.Our simulations show that WVR-PM and SyWiM can improve the QoS up to 36.04% while reduce 28.66% of average energy for one successful communication compared to PEO-PM.

  1.1 Predictive scheme in dynamic power manager. Waste energy occurs when the system turns to idle too late or wakes-up too soon. . . . . . . . . . . . 1.2 Two tasks are scaled by using DVFS technique. . . . . . . . . . . . . . . . . 1.3 Duty cycle power manager. The average consumed energy of the node can be adapted by changing its wake-up interval according to the harvested energy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Maximum Power Point (MPP) of a PV in three dierent irradiance conditions (G) [62]. MPPT mechanism is designed to provide the highest power from PVs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Typical setup for thermal-powered WSN. . . . . . . . . . . . . . . . . . . . 2.3 Experiment setup for wind-powered WSN. A diode bridge is used to convert the voltage generated by the wind turbine from AC to DC. . . . . . . 2.4 Single-path architecture for EH-WSN. . . . . . . . . . . . . . . . . . . . . . 2.5 Dual-path architecture for EH-WSN. Energy from harvesters is distributed

  .7 MESC hardware architecture. The harvested energy is normalized by the energy adapter and is distributed to OutCap and StoreCap by the energy ow controller. The comparator enables powering the system when there is sucient energy in OutCap. Both V O and V S are connected to an ADC for monitoring the state-of-charge of OutCap and StoreCap for further processing (e.g. booting, adaptations ...). . . . . . . . . . . . . . . . . . . . 2.8 System operation diagram of MESC (time and voltage are not scaled). During the harvesting energy interval, the WSN node is kept shut down until V O reaches V T HR . During the non-harvesting energy interval, the WSN node is turned o when V O decreases below V T HF . . . . . . . . . . . 2.9 Hardware design of MESC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Communication of the WSN node during a cycle of the periodic source, including the harvesting and non-harvesting energy interval (T EI and T N EI ). However, the harvested energy is only available during T EI . . . . 2.11 RICER-based protocol for communications between two nodes. When Booting process with light energy in an oce. . . . . . . . . . . . . . . . . . 2.13 V O with thermal energy. After initializing the RF transceiver, the thermal energy is only enough to charge V O to V Re . More harvested power is required to satisfy ENO of EH-WSN node. . . . . . . . . . . . . . . . . . . 2.14 V S of a PowWow node powered by two PVs when T W I = 52s. The harvested energy buered during T EI = 1000s is used in the next 2000s when the environmental energy is no more available (T N EI ). . . . . . . . . . . . 2.15 V S of two dierent PowWow nodes when T W I = 5.7s. V S almost does not change due to the equality of harvested energy and consumed energy. . . 2.16 Average wake-up interval (T W I ) according to dierent harvested power (P H ) and storage capacitance (C S ) with a periodic energy source on OM-NET++ simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.17 Average wake-up interval (T W I ) according to dierent harvested power (P H ) and storage capacitance (C S ) with a continuous energy source. . . .

T EG or EN P IEZO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2waking up, the transmitter waits for a wake-up beacon (WUB) from the receiver. After receiving a WUB, the transmitter sends a data packet (DT) after Clear Channel Assessment (CCA) and Calculation Before Transmission (CBT). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures viii 2.12 3.1 Energy monitor in the dual-path architecture. . . . . . . . . . . . . . . . . 3.2 Generic architecture of a hardware-based energy monitor. . . . . . . . . . 3.3 Generic architecture of a software-based energy monitor. . . . . . . . . . . 3.4 Generic model for the supercapacitor. . . . . . . . . . . . . . . . . . . . . . 3.5 Harvested and consumed energy proles in a PowWow node. . . . . . . . . 3.6 Energy predictor using adaptive lter architecture. Historical harvested energy prole is kept in a vector ẼH (n) to predict êH (n + 1) at the beginning of slot (n + 1). Until the end of slot

  Energy neutral design framework for optimizing the size of two capacitors in order to not only satisfy ENO, but also to maximize the QoS of the EH-WSN node.

	Chapter 1. Introduction	10
	Chapter 3: Energy Monitor and Energy Predictor for Supercapacitor-based
	Energy Storage.
	To provide energy aware capability, an energy monitor is embedded in EH-WSN. By only
	monitoring the voltage of a supercapacitor used for the energy storage (StoreCap) and
	tracking all activities of the node during a time slot, energy proles such as the harvested
	energy, consumed energy and stored energy are provided. Moreover, an energy predictor
	using an adaptive lter is also proposed to provide the prediction of harvested energy

Experiments to validate this model are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. The main contributions in this chapter are: Multiple energy sources converter (MESC) which can be used with dierent energy sources. Its energy storage is composed of two capacitors to provide a fast booting capability.

in a near future. Both energy monitor and energy predictor aim to low resource designs and energy sources independence. Therefore, they provide practical implementations on real EH-WSN nodes with dierent kinds of energy sources such as solar, thermal and wind energy. The contributions in this chapter are summarized as follows:

  to the inductor by selecting EN P V , EN T EG or EN P IEZO .

		EN PV
		EN SS
	V PV
	C PV
		EN TEG	C SS
	V TEG
		Inductor
	C TEG	ENPIEZO
			CPS
	VPIEZO	ENPS
	CPIEZO
	Figure 2.6: Multi-sources architecture for photovoltaics (PV), themoelectrics (TEG)
	and piezoelectrics (PIEZO) using a sharing inductor [83]. At a given time, only one
	harvester is connected

Table 2 .

 2 1: Energy consumption of the PowWow platform[START_REF] Alam | A hybrid model for accurate energy analysis of wsn nodes[END_REF]

	Symbol	Value

Table 3 .

 3

		Mem (words) Mul E avg (%)
	WCMA	1152	20	12.1
	NLMS	1	5	14.7
	EWMA	288	3	34.4

1: Performance of dierent energy predictors for solar energy

Table 3 .

 3

	T S (n) (minutes) Solar Wind
	5	14.8	9.5
	10	27.8	16.0
	15	43.7	23.9
	20	54.5	27.2

2: Average error (%) of dierent predictor interval

Table 4 .

 4 1: Comparison of V S (mV) with dierent values of k.

	PID-PM	EM-PM
	k Min Max Mean Var Packets Min Max Mean Var Packets
	5 3993 4008 3999 6.63 1550 3995 4000 3997 0.92 1540
	10 3992 4018 3997 20.27 1590 3989 4002 3994 4.00 1582
	15 3989 4022 3999 57.87 1605 3984 4032 3991 8.61 1599
	20 3980 4030 3998 101.9 1620 3977 4044 3987 21.32 1612

  1, regular change of wake-up interval can degrade the global QoS in a multi-hop EH-WSNs. In this subsection, ecient solutions are proposed to reduce the wake-up interval variations produced by the PEO-PM. A new power manager, named Wake-up Variation Reduction Power Manager (WVR-PM), is presented.The rst component causing the wake-up interval variations is êActive (n + 1). This component occurs in both PE-PM and NE-PM when it is required to determine the next

	AF-EP	ˆ( 1)	Positive Energy Positive Energy
	Energy		Power Manager
	Monitor		Variation Reduction
			Power Manager
			Negative Energy
	ZEI ZEI		Power Manager
	Predictor

H P n +

Table 5 .

 5 1: Performance of PEO-PM with dierent C S .

			C S = 0.9F			C S = 1.8F		C S = 2.7F
	Day	W EI W N EI	W C	W EI W N EI	W C	W EI W N EI	W C
	1	13.82	35.56 20.93 16.70	16.28 16.45 20.76	20.33 20.52
	2	12.35	31.22 18.12 14.99	11.86 13.16 17.92	13.43 15.25
	3	17.78	30.32 22.66 20.16	16.93 18.09 24.14	21.42 22.69
	4	13.75	38.29 21.50 16.20	16.79 16.53 20.26	20.79 20.55
	5	12.75	32.23 18.78 14.58	11.99 13.08 17.59	13.70 15.32
	Average 14.09	33.52 20.40 16.53	14.77 15.46 20.13	17.93 18.87

Table 5 . 1 .

 51 Chapter 5. Power Manager for Periodic Energy Sources 92 When C S = 0.9F, the average wake-up interval during T EI (W EI ) is reduced compared to C S = 1.8F and C S = 2.7F as the leakage is reduced (P Leak = 43µW). However, when V S reaches V M ax , all harvested energy is discarded. Therefore, the average wake-up interval during T N EI (W N EI ) is increased by NE-PM to ensure V S around V 0 after a cycle. This is the reason why there is a big dierence data rate between T EI and T N EI when C S = 0.9F as shown in Table5.1. Adaptations of the node in this case are presented in Fig.5.8. When C S is increased to 1.8F or 2.7F, there is enough space for the harvested energy during T EI and therefore, W N EI is very close to W EI . However, the average wake-up period during a whole cycle (W C ) when C S = 2.7F is reduced compared to C S = 1.8F as the leakage power is increased to 103µW. Therefore, the value of C S must be meticulously determined at the design phase as presented in Section 2.7.4. Only the smallest value of C S that buers enough harvested energy can not only satisfy the ENO, balance the QoS during a cycle, but also maximize the global QoS. In following simulations, C S is set to 1.8F as the maximum V S during ve days is 4.86V , which is closed to V M ax . The small gap between W EI and W N EI when C

S = 1.8F and C S = 2.7F comes from the prediction error of the ZEI predictor. The biggest dierence can be found from Table

5

.1 is the last day, when the real T EI (11 hours) is longer than usual (10 hours). The PE-PM can buer more energy, but the non-energy harvesting interval (T N EI ) is shorter. Therefore,

Table 5 .

 5 3: Performance of CL-PM over ve days.

		Day 1 Day 2 Day 3 Day 4 Day 5
	W EI	7.84	7.53	9.49	8.07	7.61
	W N EI 189.38 42.10 99.53 73.86 31.29
	W C	16.88 13.36 17.86 16.25 12.78

Table 5 .

 5 4: Peformance comparison of dierent power managers.

		PEO-PM KAN-PM Gain (%) CL-PM Gain (%)
	W EI	16.53	8.77	-46.93	8.11	-50.92
	W N EI	14.77	97.80	84.89	87.23	83.07
	W C	15.46	15.96	3.13	15.42	-0.26
	Gap	0.11	5.58	98.03	5.13	97.86
	M em	11	48	77.08	10	-10.00
	M ul	16	28	42.86	9	-77.78
	B f	0	15	-	0	-

without any battery failure. However, as it can be found on Table

5

.3, the average wakeup interval is really dierent between T EI and T N EI , which is also similar to KAN-PM.

Finally, a comparison of our proposed PEO-PM with KAN-PM and CL-PM is summarized in Table

5

.4. As PEO-PM is able to balance the QoS, average wake-up interval is extremely reduced during T N EI . The QoS during T N EI , when there is no harvested energy is improved 84.89% and 83.07% compared to KAN-PM and CL-PM, respectively.

The dierence of average wake-up interval during ve days (Gap) is reduced 98.03% with KAN-PM and 97.86% with CL-PM. However, PEO-PM does not improve the global QoS compared to KAN-PM and CL-PM as after ve days, the voltage of V S is very close to

Table 5 .

 5 5: Average N b_V ar and Sum_V ar and their gains compared to PEO-PM (α C = 0.5) over ve days (α H = 0, ∆ = 1).

	α C N b_V ar Gain (%) Sum_V ar Gain (%)
	0.5	264.6	-	752.6	-
	0.6	221.0	16.48	633.6	15.81
	0.7	180.4	31.82	563.2	25.17
	0.8	139.2	47.39	512.2	31.94
	0.9	109.8	58.50	468.6	37.74

Table 5 .

 5 6: Average N b_V ar and Sum_V ar and their gains when EWMA is applied to PH . ) and the BS is used for tuning three important parameters of WVR-PM, which are α C , α H and ∆. The improvement of WVR-PM in terms of average wake-up interval (W C ), data rate (R D ) and energy for one successful communication (E C ) using SyWiM is analyzed on communications between ED(1) and the RD.

	α H N b_V ar Gain (%) Sum_V ar Gain (%)
	0.5	90.6	17.49	265.6	43.32
	0.6	83.8	23.68	226.8	51.60
	0.7	75.6	31.15	196.4	58.09
	0.8	72.6	33.88	175.2	62.61
	0.9	69.6	36.61	160.0	65.86
	between the ED(1

5.5.4.1 Tuning α C , α H and ∆

Firstly, α H is set to zero and ∆ is set to 1 for tuning α C . Fig.

5

.11 presents N b_V ar and

Table 5 .

 5 7: Average values of N b_V ar, Sum_V ar and W C over ve days. The gains are compared to WVR-PM without quantizer (or ∆ = 1). adaptability of the node. Therefore, from Table5.6, α H = 0.

	Gain (%)

∆ N b_V ar Gain (%) Sum_V ar Gain (%) W C

Table 5 .

 5 8: Comparison of dierent power manager in a multi-hop EH-WSN.

		WVR-PM PEO-PM Gain (%) CL-PM Gain (%) KAN-PM Gain (%)
	R d	7.42	4.75	36.04	5.36	27.76	3.01	59.56
	Idl	31.44	47.70	35.08	44.15	28.79	65.24	51.80
	E C	3111.40	4361.62	28.66	4088.81	23.90	5709.70	45.51
	B f	0	0		37		63

  W I is quantized and therefore, is always a multiple of a step size (∆). Considering this feature, a synchronized wake-up interval MAC protocol (SyWiM) is proposed as a partial synchronous MAC protocol to increase the global QoS when WVR-PM is applied. Chapter 5. Power Manager for Periodic Energy Sources 106 Simulation results show that WVR-PM with SyWiM can increase by 36.04%, 27.76% and 59.56% the average data rate compared to PEO-PM, CL-PM and KAN-PM, respectively.

power manager and a ceil function for the wake-up interval (T W I ), provides a higher QoS with lower T W I during T EI compared to our PEO-PM. As it can be observed from Fig. 5.2, the average wake-up interval during T EI (W EI ) of the rst day is 7.8s, which is 53% better than PEO-PM (16.70s). Therefore, even if there is no energy predictor, CL-PM still performs well as with a shorter adaptation period T S , harvested power in the next slot is very close to the current harvested energy. It also means that the predicted harvested power in the next slot is the same as the real harvested power in the current slot ( PH (n + 1) = PH (n)). This simplest energy predictor is also applied in the thermal-powered EH-WSNs proposed in Chapter 4. When the PM has a low T W I , it is able to follow the tendency of harvested energy based on the current harvested power.

and Table

However, CL-PM uses a ceil function for its positive energy power manager. Therefore, only a small part of harvested energy is saved for T N EI , leading to a lower QoS when there is no harvested energy.

Abstract:

The limited energy and recharge cycles of batteries are crippling the design of autonomous Wireless Sensor Networks (WSNs). To overcome this issue, everlasting harvested energy and supercapacitor-based energy storage are considered as potential solutions to achieve a theoretically innite lifetime. A Power Manager (PM) is embedded in each WSN node to respect the Energy Neutral Operation condition (ENO). In this thesis, a set of PMs are proposed for energy harvesting WSN nodes to adapt their average consumed energy by changing the wake-up interval according to the available harvested energy. Another issue addressed in this thesis when considering a multi-hop EH-WSN is the eect of wake-up interval variations to the global QoS. Due to its low harvested energy, a relay node is impractical to synchronize with a transmitter if its wake-up interval regularly changes, therefore degrading the global QoS. A new power manager, named Wake-up Variation Reduction power manager (WVR-PM) is proposed to reduce the variations of the wake-up interval. By using WVR-PM, the throughput of a multi-hop EH-WSN can be improved up to 59% compare to state-of-the-art PMs while the average consumed energy for one successful communication is reduced by 45%.

Résumé:

La