Networking Protocols For Energy Harvesting Wireless Sensor Networks

Ph.DDOCTOR OF PHILOSOPH

A Viability Approach For Management Of IEEE 802.15.4 Wireless Sensor Node Performance

The long-term use of wireless sensors node while guaranteeing a good Quality of Services (QoS) is a major challenge in wireless sensor networks. Most of the relevant solutions which exist are proposed under Mac layer level but they use an optimization technique which requires a regular update of parameters and leads to unnecessary energy consumptiom which reduces the expected liftime and QoS. So in order to adress this issue, we propose in this paper, an adaptive management of wireless sensor node resources to meet application requirements in terms of energy consumption, reliability and delay. To do this, we have used the theory of viability, which is an approach that allows controling the evolution of a system in a set of desirable states. Here we have proposed an enhanced analytical model of sensor node’s energy dynamic, and we control it based on both Mac layer parameters of the IEEE 802.15.4 standard and the packet sampling frequency. The simulation results have shown that the proposed model is more accurate and efficient as a node can send more information without violating energy, reliability and delay constraints

Analyzing and Modelling Energy Harvesting Wireless Sensor Networks

Projecte final de carrera fet en col.laboració amb Northeastern University.English: Energy harvesting is envisaged as an enabling technology to meet the growing energy demands of the 21st century. The current state of the art allows tapping into several physical and naturally existing sources, such as solar, wind, vibration, RF scavenging, among others. However, there is a lack of theoretical models that can predict future consumption and residual availability of energy in a sensor node equipped with multiple boards that harvest a particular source or even simultaneously operate on different types of sources. In this thesis, we propose MAKERS, a Markov model based method to capture the energy states of such a multi-harvesting board sensor. MAKERS allows detailed predictions of the (i) probability of a node failing to detect an event owing to lack of energy, as well as the (ii) average time before this happens. Compared to previous work in this area, our model has a simpler closed form expression, it is not limited to a sensor having a single-harvesting board, and finally, it considers a more realistic harvesting model. Monte-Carlo simulation results reveal a close fit between the closed form expression in MAKERS and observed values, thereby verifying the accuracy of our approach. We later revise the model in order to relax the first constraints and move into a more realistic environment. Using some of this modifications, we conducted a set of experiments to analyze the proposed model. Results measuring the average time before running out of energy in real cases show a good agreement with theoretical predictions. The work presented here pretends to be the first step on modeling multiple-source energy harvesting nodes within the wireless sensor networks field.Castellano: La recolección de energía se concibe como una tecnología apta para satisfacer las crecientes demandas de energía del siglo XXI. El estado actual de la técnica permite aprovechar varias fuentes de energía existentes, físicas y naturales, tales como la energía solar, la eólica, la vibración, las ondas de radiofrecuencia, entre otras. Sin embargo, hay una falta de modelos teóricos que puedan predecir el consumo futuro y la disponibilidad de la energía residual en un sensor equipado con varias tarjetas que recolecten de una fuente determinada, o incluso al mismo tiempo operen sobre distintos tipos de fuentes de energía. En esta tesis, proponemos MAKERS, un modelo basado en los métodos de los procesos de Markov para capturar el estado de energía de un sensor con una tarjeta de multi-cosecha de energía. MAKERS permite predicciones detalladas de la probabilidad (i) de un nodo de no detectar un evento debido a la falta de energía, así como del tiempo (ii) promedio antes de que esto suceda. En comparación con trabajos anteriores en este ámbito, nuestro modelo tiene una expresión más simple, no se limita a un sensor que tiene una tarjeta de una sola fuente de energía, y, por último, considera un modelo más realista de la cosecha. Los resultados de las simulaciones Monte-Carlo revelan un ajuste perfecto entre la expresión de MAKERS y los valores observados, verificando la exactitud de nuestro enfoque. Más tarde, revisamos el modelo con el fin de relajar las primeras restricciones y pasar a un entorno más realista. Usando algunas de estas modificaciones, se realizó una serie de experimentos para analizar el modelo propuesto. Los resultados de la medición del tiempo medio antes de quedarse sin energía en casos reales se corresponden con las predicciones teóricas. El trabajo que aquí se presenta pretende ser el primer paso en el modelado de sensores alimentados por múltiples fuentes de la energía en el campo de las redes de sensores inalámbricos.Català: La recollita d'energia es concep com una tecnologia apta per satisfer les creixents demandes d'energia del segle XXI. L'estat actual de la tècnica permet aprofitar diverses fonts d'energia existents, físiques i naturals, com ara l'energia solar, l'eòlica, la vibració, les ones de radiofreqüència, entre altres. No obstant, hi ha una manca de models teòrics que puguin predir el consum futur i la disponibilitat de l'energia residual en un sensor equipat amb diverses targetes que recol·lecten d'una font determinada, o fins i tot a la vegada operin sobre diferents tipus de fonts d'energia. En aquesta tesi, proposem MAKERS, un model basat en els mètodes dels processos de Markov per capturar l'estat d'energia d'un sensor amb una targeta de multi-collita d'energia. MAKERS permet prediccions detallades de la probabilitat (i) d'un node de no detectar un esdeveniment a causa de la falta d'energia, així com del temps (ii) de mitjana abans que això passi. En comparació amb treballs anteriors en aquest àmbit, el nostre model té una expressió més simple, no es limita a un sensor que té una targeta d'una sola font d'energia, i, finalment, considera un model més realista de la recollita. Els resultats de les simulacions Monte-Carlo revelen un ajust perfecte entre l'expressió de MAKERS i els valors observats, verificant l'exactitud del nostre enfocament. Més tard, revisem el model per tal de relaxar les primeres restriccions i passar a un entorn més realista. Utilitzant algunes d'aquestes modificacions, es va realitzar una sèrie d'experiments per analitzar el model proposat. Els resultats de les mesures del temps mig abans de quedar-se sense energia en casos reals es corresponen amb les prediccions teòriques. El treball que aquí es presenta pretén ser el primer pas en el modelatge de sensors alimentats per múltiples fonts de l'energia en el camp de les xarxes de sensors sense fils

Design and control approaches for energy harvesting wireless sensor networks

Wireless Networks are monitoring infrastructures composed of sensing (measuring), computing, and communication devices used to observe, supervise and monitor environmental phenomena. Energy Harvesting Wireless Sensor Networks (EH-WSN) have the additional feature to save energy from the environment in order to ensure long life autonomy of the entire network, without ideally the human intervention over long periods of time. The present work is aimed to address some of the most significant limitations of the actual EH-WSN, making a step forward the perpetual operation of EH-WSN. In this dissertation, design methodology and management policies are proposed to improve EH-WSN in terms of application performances, traffic congestion and energy efficiency. The study explicitly targets to energy-efficient affordable ways to develop more reliable and trustworthy EH-WSN, capable to ensure long life and desired performances. The presentation is organized into two macro sections, or Parts: the first one is dedicated to design the main EH-WSN hardware and software parameters that affect the energy efficiency of a sensor node, while in the second part three dynamic control strategies are proposed to outperform the EH-WSN in terms of energy efficiency, traffic congestion and application requirements

Fast design space exploration of vibration-based energy harvesting wireless sensors

An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25s and the average RSM estimation error is less than 16.5

Energy harvesting system design and optimization for wireless sensor networks

Wireless sensor networks (WSN) are becoming widely adopted for many applications including complicated tasks like building energy management. However, one major concern for WSN technologies is the short lifetime and high maintenance cost due to the limited battery energy. One of the solutions is to scavenge ambient energy, which is then rectified to power the WSN. The objective of this thesis was to investigate the feasibility of an ultra-low energy consumption power management system suitable for harvesting sub-mW photovoltaic and thermoelectric energy to power WSNs. To achieve this goal, energy harvesting system architectures have been analyzed. Detailed analysis of energy storage units (ESU) have led to an innovative ESU solution for the target applications. Battery-less, long-lifetime ESU and its associated power management circuitry, including fast-charge circuit, self-start circuit, output voltage regulation circuit and hybrid ESU, using a combination of super-capacitor and thin film battery, were developed to achieve continuous operation of energy harvester. Low start-up voltage DC/DC converters have been developed for 1mW level thermoelectric energy harvesting. The novel method of altering thermoelectric generator (TEG) configuration in order to match impedance has been verified in this work. Novel maximum power point tracking (MPPT) circuits, exploring the fractional open circuit voltage method, were particularly developed to suit the sub-1mW photovoltaic energy harvesting applications. The MPPT energy model has been developed and verified against both SPICE simulation and implemented prototypes. Both indoor light and thermoelectric energy harvesting methods proposed in this thesis have been implemented into prototype devices. The improved indoor light energy harvester prototype demonstrates 81% MPPT conversion efficiency with 0.5mW input power. This important improvement makes light energy harvesting from small energy sources (i.e. credit card size solar panel in 500lux indoor lighting conditions) a feasible approach. The 50mm × 54mm thermoelectric energy harvester prototype generates 0.95mW when placed on a 60oC heat source with 28% conversion efficiency. Both prototypes can be used to continuously power WSN for building energy management applications in typical office building environment. In addition to the hardware development, a comprehensive system energy model has been developed. This system energy model not only can be used to predict the available and consumed energy based on real-world ambient conditions, but also can be employed to optimize the system design and configuration. This energy model has been verified by indoor photovoltaic energy harvesting system prototypes in long-term deployed experiments

Optimal power control in green wireless sensor networks with wireless energy harvesting, wake-up radio and transmission control

Wireless sensor networks (WSNs) are autonomous networks of spatially distributed sensor nodes which are capable of wirelessly communicating with each other in a multi-hop fashion. Among different metrics, network lifetime and utility and energy consumption in terms of carbon footprint are key parameters that determine the performance of such a network and entail a sophisticated design at different abstraction levels. In this paper, wireless energy harvesting (WEH), wake-up radio (WUR) scheme and error control coding (ECC) are investigated as enabling solutions to enhance the performance of WSNs while reducing its carbon footprint. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR and ECC, is formulated and solved using distributed dual subgradient algorithm based on Lagrange multiplier method. It is discussed and verified through simulation results to show how the proposed solutions improve network utility, prolong the lifetime and pave the way for a greener WSN by reducing its carbon footprint

SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE

Chapter 8: Energy Harvesting Technologies: Thick-Film Piezoelectric Microgenerato