Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Modelling the Positional and Orientation Sensitivity of Inductively Coupled Sensors for Industrial IoT Applications

As the Internet of Things (IoT) sector continually expands there is a growing abstraction between physical objects and the data associated with them. At the same time, emerging Industrial-IoT applications rely upon diverse and robust hardware sensing interfaces in order to deliver high quality data. In this paper, the fundamental limitations associated with inductive proximity sensing interfaces are considered in terms of positional and orientation sensitivity and a triaxial approach is proposed that enables arbitrary source-sensor positioning. A matrix transformation model based on the field coupling equations is applied to a number of candidate configurations assessed according their relative source-sensor coverage and graphical visualization of coupling quality. Particular attention is paid to the recombination of tri-sensor outputs involving direct-summation, rectifysummation, best-coil and root-mean-square methods. Of these, the rectify-summation method was observed to provide favorable performance, exceeding 70% coverage for practical cases, thus far exceeding that of traditional co-planar arrangements

Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

Ubiquitous Computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives towards the objective of developing seamless, interoperable and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting and power transmission using computing devices. Radio Frequency (RF) based Energy Management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RFID Sensor Networks (RSN) are Energy Harvesting (EH) and Energy Transfer (ET). These techniques enable the dynamic energy level maintenance and optimisation as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

UAV trajectory optimisation in smart cities using modified A* algorithm combined with Haversine and Vincenty formulas

It is anticipated that the backbone of Smart Cities concerning automation and networking will be formed by Unmanned Aerial Vehicles in the imminent future. Therefore, our research focuses on developing advanced microcontrollers embedded with Artificial Intelligence techniques for self-governing Unmanned Aerial Vehicles. The main objective of this research was to enable full automation for the execution of flight paths with non-trivial sequences that will be performed with centimetre-level accuracy. Also, by utilising dynamic flight plans and trajectories, we aim to secure autonomous aviation based on norms, with control loops and fundamental constraints. More specifically, we evolved a novel algorithmic technique for trajectory optimisation, which deploys a modification to the A* search algorithm, implemented by the Haversine formula and enhances accuracy using Vincenty’s formula. Furthermore, realistic values for trajectory optimisation and obstacle avoidance were found through the implementation of a simulative investigation. The outcomes of our methodology indicate that the safety constraints associated with the integration of Unmanned Aerial Vehicles in the urban environment can be significantly mitigated. Consequently, their effectiveness will be increased in realising their diverse operations and capabilities

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

Propelling design evolution: using a scientifically driven design process to incrementally advance architecture

This thesis explores the influence of design evolution within the professions of architecture, industrial design, and engineering. Design evolution is a process that can be accelerated through collaboration between various groups linked to the development and production of designed objects. Working as interdisciplinary professionals, I support the incremental advancement of contemporary products in order to encourage positive human progress. As a case study, I investigate standard electrical systems used in architectural construction. It is apparent that for nearly 100 years the same basic electrical systems and components have been installed in buildings throughout the world. To promote design evolution in the field of architecture, I implement a scientific design approach to incrementally advance the designs of two century-old electrical components, the single-pole switch and the duplex outlet receptacle. The goal of this thesis is to investigate how analytical design methods can reveal incremental advances that yield more appropriately designed products

Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

Undeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futuro

Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

Premi Extraordinari de Doctorat, promoció 2016-2017. Àmbit d’Enginyeria de les TICUndeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futurosAward-winningPostprint (published version