Academic literature on the topic 'Wireless Sensor Network,Energy Harvesting,LoRaWAN'

Une grande partie des nouvelles générations d'objets connectés ne pourra se développer que s'il est possible de les rendre entièrement autonomes sur le plan énergétique. Même si l'utilisation de batteries ou de piles résout une partie de ce problème en assurant une autonomie qui peut-être importante avec des coûts relativement faibles, elle introduit non seulement des contraintes de maintenance incompatibles avec certaines applications, mais aussi des problèmes de pollution. La récupération de l'énergie thermique, mécanique, électromagnétique, solaire ou éolienne est une solution très prometteuse. Dans ce cas, la vie de l'objet connecté peut-être prolongée. Cependant, l'énergie récupérée dépend fortement des conditions au voisinage du dispositif et peut donc varier de façon périodique ou aléatoire. Il parait alors important d'adapter le système (mesure et transmission de données) aux contraintes de la récupération d'énergie. L'objectif de la thèse est de proposer une solution de capteur autonome basée sur un système de récupération et de gestion multisources d'énergies (solaire et éolienne) et pouvant-être mis en oeuvre dans différentes classes d'applications IoT. On s'intéresse, dans un premier temps, à la modélisation de la consommation du noeud capteur. Ensuite, on modélise le système de récupération multisources. Puis, on se focalise sur le management de puissance du système autonome. Ce management est basé sur des prédictions de l'énergie disponible et de celle consommée. Enfin, le travail de modélisation et d'optimisation est validé par des expérimentations afin d’avoir un démonstrateur de Capteur Communicant Autonome en Énergie pour les applications IoT<br>Researchers aim to develop entirely autonomous sensors. By ensuring an important autonomy, the use of batteries solves part of the energy problem with relatively low costs. However, batteries introduce different problems such as maintenance and environmental pollution. Harvesting thermal, mechanical, electromagnetic, solar or wind energy present in the environment is an attractive solution. Using harvested energy from their surroundings, wireless sensor nodes can significantly increase their typical lifetime. Nevertheless, the harvested energy depends on the surrounding conditions of the device and can vary periodically or randomly. It seems important to adapt the system (measurement and data transmission) to the harvesting energy constraints. The thesis objective is to provide an autonomous sensor solution based on a multisources energy harvesting and management system (solar and wind energies), which can be used in different IoT applications. First, we are interested in modeling and optimizing the sensor node energy consumption. Then, the multiple harvesting system is modeled according to the energy needs of the sensor node. Besides, we focus on the power management of the autonomous system. This management part is based on predictions of both available and consumed energies. Finally, the proposed modeling and optimization studies are validated with experimental works in order to develop an Autonomous Communicating Sensor platform for IoT applications

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each.

Master of Science<br>Department of Electrical and Computer Engineering<br>William B. Kuhn and Balasubramaniam Natarajan<br>This thesis presents an energy harvesting wireless sensor network (EHWSN) architecture customized for use within a space suit. The contribution of this research spans both physical (PHY) layer energy harvesting transceiver design and appropriate medium access control (MAC) layer solutions. The EHWSN architecture consists of a star topology with two types of transceiver nodes: a powered Gateway Radio (GR) node and multiple energy harvesting (EH) Bio-Sensor Radio (BSR) nodes. A GR node works as a central controller to receive data from BSR nodes and manages the EHWSN via command packets; low power BSR nodes work to obtain biological signals, packetize the data and transmit it to the GR node. To demonstrate the feasibility of an EHWSN at the PHY layer, a representative BSR node is designed and implemented. The BSR node is powered by a thermal energy harvesting system (TEHS) which exploits the difference between the temperatures of a space suit's cooling garment and the astronaut's body. It is shown that through appropriate control of the duty-cycle in transmission and receiving modes, it is possible for the transceiver to operate with less than 1mW power generated by the TEHS. A super capacitor, energy storage of TEHS, acts as an energy buffer between TEHS and power consuming units (processing units and transceiver radio). The super capacitor charges when a BSR node is in sleep mode and discharges when the node is active. The node switches from sleep mode to active mode whenever the super capacitor is fully charged. A voltage level monitor detects the system's energy level by measuring voltage across the super capacitor. Since the power generated by the TEHS is extremely low(less than 1mW) and a BSR node consumes relatively high power (approximately 250mW) during active mode, a BSR node must work under an extremely low duty cycle (approximately 0.4%). This ultra-low duty cycle complicates MAC layer design because a BSR node must sleep for more than 99.6% of overall operation time. Another challenge for MAC layer design is the inability to predict when the BSR node awakens from sleep mode due to unpredictability of the harvested energy. Therefore, two feasible MAC layer designs, CSA (carrier sense ALOHA based)-MAC and GRI (gateway radio initialized)-MAC, are proposed in this thesis.

The agricultural sector is an emerging application area for Wireless Sensor Networks (WSNs). This requires sensor nodes to be deployed in the outdoor environment so as to monitor pertinent natural features, such as soil condition or pest infestation. Limited energy supply and subsequent battery replacement are common issues for these agricultural sensor nodes. One possible solution is to use energy harvesting, where the ambient energy is extracted and converted into usable electrical form to energise the wireless sensors. The work presented in this thesis investigates the feasibility of using Radio Frequency (RF) energy harvesting for a specific application; that is powering a generic class of wireless ground-level, agricultural sensor networks operating in an outdoor environment. The investigation was primarily undertaken through a literature study of the subject. The first part of the thesis examines several energy harvesting/ wireless energy transfer techniques, which may be applicable to power the targeted agricultural WSN nodes. The key advantages and limitations of each technique are identified, and the rationale is being given for selecting far-field RF energy harvesting as the investigated technique. It is then followed by a theoretical-based system analysis, which seeks to identify all relevant design parameters, and to quantify their impact on the system performance. An RF link budget analysis was also included to examine the feasibility of using RF energy harvesting to power an exemplar WSN node - Zyrox2 Bait Station. The second part of the thesis focuses on the design of two energy harvesting antennas. The first design is an air-substrate-based folded shorted patch antenna (FSPA) with a solid ground plane, while the second design is a similar FSPA structure with four pairs of slot embedded into its ground plane. Both antennas were simulated, fabricated and tested inside an anechoic chamber, and in their actual operating environment - an outdoor field. In addition, a power harvester circuit, built using the commercially available off-the-shelf components, was tested in the laboratory using an RF signal generator source. The results from both the laboratory and field trial were analysed. The measurement techniques used were reviewed, along with some comments on how to improve them. Further work on the RF energy harvester, particularly on the improvement of the antenna design must be carried out before the feasibility and viable implementations for this application can be definitively ascertained.

Massive deployment of wireless IoT (Internet of Things) devices makes replacement or recharge of batteries expensive and impractical for some applications. Energy harvesting is a promising solution, and various designs are proposed to harvest power from ambient resources including thermal, vibrational, solar, wind, and RF sources. Among these ambient resources, AC powerlines are a stable energy source in an urban environment. Many researchers investigated methods to exploit this stable source of energy to power wireless IoT devices. The proposed circuit aims to harvest energy from AC powerlines with a wide input range of from 10 to 50 A. The proposed system includes a wake-up circuit and is capable of cold-start. A buck-boost converter operating in DCM is adopted for impedance matching, where the impedance is rather independent of the operation conditions. So, the proposed system can be applied to various types of wireless sensor nodes with different internal impedances. Experimental results show that the proposed system achieves an efficiency of 80.99% under the powerline current of 50 A.<br>M.S.<br>Nowadays, with the magnificent growth of IoT devices, a reliable, and efficient energy supply system becomes more and more important, because, for some applications, battery replacement is very expensive and sometimes even impossible. At this time, a well-designed self-contained energy harvesting system is a good solution. The energy harvesting system can extend the service life of the IoT devices and reduce the frequency of charging or checking the device. In this work, the proposed circuit aims to harvest energy from the AC power lines, and the harvested power intends to power wireless sensor nodes (WSNs). By utilizing the efficient and self-contained EH system, WSNs can be used to monitor the temperature, pressure, noise level and humidity etc. The proposed energy harvesting circuit was implemented with discrete components on a printed circuit board (PCB). Under a power line current of 50 A @ 50 Hz, the proposed energy harvesting circuit can harvest 156.6 mW, with a peak efficiency of 80.99 %.

The operation of Energy Harvesting Wireless Sensor Networks (EHWSNs) is a very lively area of research. This is due to the increasing inclination toward green systems, in order to reduce the energy consumption of human activities at large and to the desire of designing networks that can last unattended indefinitely (see, e.g., the nodes employed in Wireless Sensor Networks, WSNs). Notably, despite recent technological advances, batteries are expected to last for less than ten years for many applications and their replacement is often prohibitively expensive. This problem is particularly severe for urban sensing applications, think of, e.g., sensors placed below the street level to sense the presence of cars in parking lots, where the installation of new power cables is impractical. Other examples include body sensor networks or WSNs deployed in remote geographic areas. In contrast, EHWNs powered by energy scavenging devices (renewable power) provide potentially maintenance-free perpetual network operation, which is particularly appealing, especially for highly pervasive Internet of Things. Lossy temporal compression has been widely recognized as key for Energy Constrained Wireless Sensor Networks (WSN), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. The first part of this thesis deals with the evaluation of a number of lossy compression methods from the literature, and the analysis of their performance in terms of compression efficiency, computational complexity and energy consumption. Specifically, as a first step, a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and Wavelet-based models is carried out, by looking at their performance as a function of relevant signal statistics. After that, closed form expressions for their overall energy consumption and signal representation accuracy are obtained through numerical fittings. Lastly, the benefits that lossy compression methods bring about in interference-limited multi-hop networks are evaluated. In this scenario the channel access is a source of inefficiency due to collisions and transmission scheduling. The results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay and increased reliability performance. The subsequent part of the thesis copes with the problem of energy management for EHWSNs where sensor batteries are recharged via the energy harvested through a solar panel and sensors can choose to compress data before transmission. A scenario where a single node communicates with a single receiver is considered. The task of the node is to periodically sense some physical signal and report the measurements to the receiver (sink). We assume that this task is delay tolerant, i.e., the sensor can store a certain number of measurements in the memory buffer and send one or more packets of data after some time. Since most physical signals exhibit strong temporal correlation, the data in the buffer can often be compressed by means of a lossy compression method in order to reduce the amount of data to be sent. Lossy compression schemes allow us to select the compression ratio and trade some accuracy in the data reconstruction at the receiver for more energy savings at the transmitter. Specifically, our objective is to obtain the policy, i.e., the set of decision rules that describe the node behavior, that jointly maximizes throughput and reconstruction fidelity at the sink while meeting some predefined energy constraints, e.g., the battery charge level should never go below a guard threshold. To obtain this policy, the system is modeled as a Constrained Markov Decision Process (CMDP), and solved through Lagrangian Relaxation and Value Iteration Algorithm. The optimal policies are then compared with heuristic policies in different energy budget scenarios. Moreover the impact of the delay on the knowledge of the Channel State Information is investigated. Two more parts of this thesis deal with the development of models for the generation of space-time correlated signals and for the description of the energy harvested by outdoor photovoltaic panels. The former are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering or compression algorithms. The latter are especially useful in the investigation and in the optimization of EHWSNs. These models will be presented at the beginning and then intensively used for the analysis and the performance evaluation of the schemes that are treated in the remainder of the thesis.<br>Quello delle Energy Harvesting Wireless Sensor Networks (EHWSNs) è attualmente un campo di ricerca molto attivo. Ciò ò principalmente dovuto al crescente interesse dimostrato verso i sistemi "green", con l'obiettivo di ridurre il consumo energetico delle attività umane in generale e il desiderio di progettare reti autosufficienti che possono durare indefinitamente (si pensi, ad esempio, ai nodi impiegati in reti di sensori wireless , WSNs). In particolare, nonostante i recenti progressi tecnologici, per molte applicazioni le batterie si dimostrano durare meno di dieci anni e il costo per la loro sostituzione è spesso proibitivo. Questo problema è particolarmente grave per le applicazioni di rilevamento urbano, si pensi ad esempio ad uno scenario in cui dei sensori sono posizionati al di sotto del manto stradale per il rilevamento della presenza di auto nei parcheggi, dove l'installazione di nuovi cavi di alimentazione o la sostituizione delle batterie non sono praticabili . Altri esempi includono le "body sensor networks" o le reti di sensori distribuite in aree geografiche remote o inaccessibili. Al contrario, EHWSNs alimentate da dispositivi di energy scavenging (energia rinnovabile) possono dare vita a reti perpetue e potenzialmente esenti da manutenzione, che sono particolarmente attraenti, soprattutto per il nuovo concetto altamente pervasivo di Internet of Things. La compressione temporale con perdite (lossy temporal compression) è ampiamente riconosciuta come componente fondamentale per il funzionamento delle reti di sensori con energia limitata, dove la ricostruzione imperfetta del segnale al punto di raccolta è spesso accettabile, fino ad un certo limite massimo sulla tolleranza di errore. Una parte di questa tesi tratta la valutazione prestazionale di un significativo numero di metodi di compressione con perdita tratti dalla letteratura, e l'analisi delle loro prestazioni in termini di efficienza di compressione, complessità computazionale e consumo energetico. In dettaglio, come primo passo, viene proposta una valutazione delle prestazioni di sistemi di compressione esistenti e nuovi, tra cui: modelli lineari, autoregressivi, basati su FFT/DCT e Wavelet, individuando le loro prestazioni in funzione delle statistiche dei segnali rilevanti. Dopo di che, attraverso interpolazione numerica, verranno derivate delle espressioni in forma chiusa per il consumo globale di energia e la precisione di rappresentazione del segnale. Infine, verranno valutati i benefici che i metodi di compressione con perdita possono portare in reti multi-hop con interferenze limitate. In questo scenario il canale di accesso diventa fonte di inefficienza attraverso collisioni e metodo di accesso al mezzo. I risultati rivelano che le tecniche basate su DCT sono la scelta migliore in termini di efficienza di compressione, ma non risultano efficienti in termini di consumo energetico. Al contrario, metodi lineari possono dar luce a notevoli risparmi in termini di dispendio energetico, e al tempo stesso, portare a rapporti di compressione soddisfacenti, ritardi di rete ridotti e migliore affidabilità. La parte successiva di questa tesi affronta il problema della gestione energetica per EHWSNs nelle quali le batterie dei nodi sensore vengono ricaricate attraverso l'energia raccolta da un pannello solare e sensori possono scegliere di comprimere i dati prima della trasmissione. A tal fine viene considerato uno scenario in cui un singolo nodo comunica con un singolo ricevitore. L'attività del nodo è quella di campionare periodicamente qualche segnale fisico e riportare le misurazioni al ricevitore (sink). Tale attività viene assunta essere tollerante al ritardo, ovvero, il sensore può memorizzare un certo numero di misurazioni nel buffer di memoria e inviare uno o più pacchetti di dati aggregati dopo un certo tempo. Poichè la maggior parte dei segnali fisici manifestano una forte correlazione temporale, i dati nel buffer possono eventualmente essere compressi mediante un metodo di compressione con perdita al fine di ridurre la quantità di dati da inviare. Attraverso metodi di compressione con perdita di dati che permettono di selezionare il rapporto di compressione è possibile scambiare un po' di accuratezza nella ricostruzione dei dati al ricevitore per ottenere maggiori risparmi di energia al trasmettitore. In dettaglio, l'obiettivo di questa parte della tesi è quello di ottenere la politica, cioè l'insieme di regole decisionali che descrivono il comportamento del nodo sensore, che massimizza il throughput unitamente alla fedeltà di ricostruzione al punto di raccolta soddisfacendo al tempo stesso alcuni vincoli energetici predefiniti, ad esempio, il livello di carica della batteria non deve mai scendere al di sotto di una soglia di guardia. Per ottenere la politica ottima, il sistema verrà modellato attraverso un Processo Decisonale di Markov Vincolato (Constrained Markov Decision Process, CMDP), e risolto attraverso il rilassamento lagrangiano e l'algoritmo di Value Iteration. Le politiche ottimali verranno poi confrontate con alcune politiche euristiche in diversi scenari di bilancio energetico. Verrà inoltre studiato l'impatto del ritardo sulla conoscenza dello stato del canale. Due ulteriori parti di questa tesi riguardano lo sviluppo di modelli per la generazione di segnali correlati nello spazio e nel tempo, e per la descrizione statistica dell'energia raccolta da pannelli fotovoltaici esterni. I primi sono utili per testare l'efficacia di algoritmi di raccolta dati e possono venire impiegati nella progettazione di accurati strumenti di simulazione per reti di sensori. Inoltre possono venire impiegati come modelli di riferimento per dimostrare risultati teorici per algoritmi di raccolta dati o di compressione. Gli ultimi sono particolarmente utili per lo studio e l'ottimizzazione delle EHWSNs. Entrambi i modelli verranno introdotti nella parte iniziale della tesi e successivamente utilizzati per tutto il corpo centrale della stessa.

Low-frequency vibrations typically occur in many practical structures and systems when in use, for example, in aerospaces and industrial machines. Piezoelectric materials feature compactness, lightweight, high integration potential, and permit to transduce mechanical energy from vibrations into electrical energy. Because of their properties, piezoelectric materials have been receiving growing interest during the last decades as potential vibration- harvested energy generators for the proliferating number of embeddable wireless sensor systems in applications such as structural health monitoring (SHM). The basic idea behind piezoelectric energy harvesting (PEH) powered architectures, or energy harvesting (EH) more in general, is to develop truly “fit and forget” solutions that allow reducing physical installations and burdens to maintenance over battery-powered systems. However, due to the low mechanical energy available under low-frequency conditions and the relatively high power consumption of wireless sensor nodes, PEH from low-frequency vibrations is a challenge that needs to be addressed for the majority of the practical cases. Simply saying, the energy harvested from low-frequency vibrations is not high enough to power wireless sensor nodes or the power consumption of the wireless sensor nodes is higher than the harvested energy. This represents a main barrier to the widespread use of PEH technology at the current state of the development, despite the advantages it may offer. The main contribution of this research work concerns the proposal of a novel EH circuitry, which is based on a whole-system approach, in order to develop enhanced PEH powered wireless sensor nodes, hence to compensate the existing mismatch between harvested and demanded energy. By whole-system approach, it is meant that this work develops an integrated system-of-systems rather than a single EH unit, thus getting closer to the industrial need of a ready- to-use energy-autonomous solution for wireless sensor applications such as SHM. To achieve so, this work introduces: Novel passive interfaces in connection with the piezoelectric harvester that permit to extract more energy from it (i.e., a complex conjugate impedance matching (CCIM) interface, which uses a PC permalloy toroidal coil to achieve a large inductive reactance with a centimetre- scaled size at low frequency; and interfaces for resonant PEH applications, which exploit the harvester‟s displacement to achieve a mechanical amplification of the input force, a magnetic and a mechanical activation of a synchronised switching harvesting on inductor (SSHI) mechanism). A novel power management approach, which permits to minimise the power consumption for conditioning the transduced signal and optimises the flow of the harvested energy towards a custom-developed wireless sensor communication node (WSCN) through a dedicated energy-aware interface (EAI); where the EAI is based on a voltage sensing device across a capacitive energy storage. Theoretical and experimental analyses of the developed systems are carried in connection with resistive loads and the WSCN under excitations of low frequency and strain/acceleration levels typical of two potential energy- autonomous applications, that are: 1) wireless condition monitoring of commercial aircraft wings through non-resonant PEH based on Macro-Fibre Composite (MFC) material bonded to aluminium and composite substrates; and wireless condition monitoring of large industrial machinery through resonant PEH based on a cantilever structure. shown that under similar testing conditions the developed systems feature a performance in comparison with other architectures reported in the literature or currently available on the market. Power levels up to 12.16 mW and 116.6 µW were respectively measured across an optimal resistive load of 66 277 kΩ for an implemented non-resonant MFC energy harvester on aluminium substrate and a resonant cantilever-based structure when no interfaces were added into the circuits. When the WSCN was connected to the harvesters in place of the resistive loads, data transmissions as fast as 0.4 and s were also respectively measured. By use of the implemented passive interfaces, a maximum power enhancement of around 95% and 452% was achieved in the two tested cases and faster data transmissions obtained with a maximum percentage improvement around 36% and 73%, respectively. By the use of the EAI in connection with the WSCN, results have also shown that the overall system‟s power consumption is as low as a few microwatts during non- active modes of operation (i.e., before the WSCN starts data acquisition and transmission to a base station). Through the introduction of the developed interfaces, this research work takes a whole-system approach and brings about the capability to continuously power wireless sensor nodes entirely from vibration-harvested energy in time intervals of a few seconds or fractions of a second once they have been firstly activated. Therefore, such an approach has potential to be used for real-world energy- autonomous applications of SHM.

In recent year, wireless sensor networks have gradually become an indispensable part of people's daily lives. Energy consumption and energy harvesting play an important role in these systems. In outdoor, there is no doubt that solar energy is more suitable to powering the wireless sensor nodes. Although the energy consumption of these systems has been greatly reduced and the lifetime of sensor nodes also be improved through the larger capacity of supercapacitor or larger size of solar panel. But it will generate another kind of squander, how to choose a suitable solar panel and supercapacitor is appearance in our view. In this paper, I optimized the solar energy harvesting system from two aspects of capacity of supercapacitor and size of solar panel. The objective of this thesis has shown that as small solar panel and supercapacitor as possible for a given load of these systems under low consumption condition. Here, I establish the simulation in Simulink of Matlab, and build a low-power consumption; high-security solar energy harvesting hardware system for monitoring environment in Sundsvall, Sweden. Through the comparison between the simulation and real monitor to verify the feasibility