Yes / Existing works on energy harvesting wireless systems often assume a constant conversion efficiency for the energy harvester. In practice, the conversion efficiency often varies with the input power. In this work, based on a review of existing energy harvesters in the literature, a heuristic expression for the conversion efficiency as a function of the input power is derived by curve fitting. Using this function, two example energy harvesters are used to analyze the realistic performances of wireless relaying and wireless energy transfer. Numerical results show that the realistic performances of the wireless systems could be considerably different from what predicted by the existing analysis.

Over the past fifteen years, advances in Micro-Electro-Mechanical Systems (MEMS) technology have enabled rapid development of wireless sensor networks (WSNs). A WSN consists of a large number of sensor nodes that are typically powered by batteries. Each sensor node collects useful information from its environment, and forwards this data to a base station through wireless communications. Applications of WSNs include environmental monitoring, industrial monitoring, agriculture, smart home monitoring, military surveillance, to name a few. Due to battery constraint at each sensor node, a fundamental challenge for a WSN is its limited operational lifetime -- the amount of time that the network can remain operational before some or all of the sensor nodes run out of battery. To conserve energy and prolong the lifetime of a WSN, there have been active research efforts across all network layers. Although these efforts help conserve energy usage and prolong network lifetime to some extent, energy and lifetime remain fundamental bottlenecks and are the key factors that hinder the wide-scale deployment of WSNs. This dissertation addresses the energy problem of a WSN by exploiting a recent breakthrough in wireless energy transfer (WET) technology. This breakthrough WET technology is based on the so-called magnetic resonant coupling (MRC), which allows electric energy to be transferred from a source coil to a receive coil without any plugs or wires. The advantages of MRC are high energy transfer efficiency even under omni-direction, not requiring line-of-sight (LOS), and being robust against environmental conditions. Inspired by this enabling WET technology, this dissertation focuses on applying MRC to a WSN and on studying how to optimally use this technology to address lifetime problem for a WSN. The goal is to fundamentally remove lifetime bottleneck for a WSN. The main contributions of this dissertation are summarized as follows: 1. Single-node Charging for a Sparse WSN. We first investigate how MRC can be applied to a WSN so as to remove the lifetime performance bottleneck in a WSN, i.e., allowing a WSN to remain operational forever. We consider the scenario of a mobile wireless charging vehicle (WCV) periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions for a sensor node to maintain its renewable energy cycle. We study an optimization problem, with the objective of maximizing the ratio of the WCV's vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and uncover a number of important properties. Subsequently, we develop a near-optimal solution by a piecewise linear approximation technique and prove its performance guarantee. This first study shows that network lifetime bottleneck can be fundamentally resolved by WET. 2. Multi-node Charging for a Dense WSN. We next exploit recent advances in MRC that allows multiple sensor nodes to be charged at the same time, and show how MRC with multi-node charging capability can address the scalability problem associated with the single-node charging technology. We consider a WCV that periodically travels inside a WSN and can charge multiple sensor nodes simultaneously. Based on the charging range of the WCV, we propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. We pursue a formal optimization framework by jointly optimizing the traveling path of the WCV, flow routing among the sensor nodes, and the charging time with each hexagonal cell. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy. Through numerical results, we demonstrate that our solution can indeed address the scalability problem for WET in a dense WSN. 3. Bundling Mobile Base Station and Wireless Energy Transfer: The Pre-planned Path Case. Our aforementioned work is based on the assumption that the location of base station is fixed and known in the WSN. On the other hand, it has been recognized that a mobile base station (MBS) can offer significant advantages over a fixed one. But employing two separate vehicles, one for WET and one for MBS, could be expensive and hard to manage. So a natural question to ask is: can we bundle WET and MBS on the same vehicle? This is the focus of this study. Here, our goal is to minimize energy consumption of the entire system while ensuring that none of the sensor nodes runs out of energy. To simplify the problem, we assume that the path for the vehicle is given a priori. We develop a mathematical model for this problem. Instead of studying the general problem formulation (called CoP-t), which is time-dependent, we show that it is sufficient to study a special subproblem (called CoP-s), which only involves space-dependent variables. Subsequently, we develop a provable near-optimal solution to CoP-s with the development of several novel techniques including discretizing a continuous path into a finite number of segments and representing each segment with worst-case energy bounds. 4. Bundling Mobile Base Station and Wireless Energy Transfer: The Unconstrained Path Case. Based on our experience for the pre-planned path case, we further study the problem where the traveling path of the WCV (also carrying the MBS) can be unconstrained. That is, we study an optimization problem that jointly optimizes the traveling path, stopping points, charging schedule, and flow routing. For this problem, we propose a two-step solution. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem. This dissertation offers the first systematic investigation on how WET (in particular, the MRC technology) can be exploited to address lifetime bottleneck of a WSN. It lays the foundation of exploring WET for WSNs and other energy-constrained wireless networks. On the mathematical side, we have developed or applied a number of powerful techniques such as piecewise linear approximation, RLT, time-space transformation, discretization, and logical point representation that may be applicable to address a broad class of optimization problems in wireless networks. We expect that this dissertation will open up new research directions on many interesting networking problems that can take advantage of the WET technology. / Ph. D.

Failure of a machine can lead to production downtime and significant financial losses. Condition monitoring is implemented to avoid such downtime and devices can be used to collect data used for monitoring machine health. Vibration data is the most common type of data used for predicting machine failure. To reduce the need for hazardous cables, such devices are often battery-operated, but this can decrease monitoring device lifespans to less than 3 years, if non-rechargeable batteries are used. This thesis first proposes a design framework for implementing radio frequency energy harvesting (RFEH) at a network level. All of the necessary inputs and parameters to ensure the successful implementation of RFEH for a wireless sensor network are explored. A second design framework is then proposed for using RFEH as a source of energy to power devices for condition monitoring. This includes a power analysis of all device components, as well as the design details for an implementation of wireless power transfer using a wireless transmitter and receiver. A comparison of different types of energy sources for the device is given, followed by a case study, using commercially-available components. A simulation is used to analyze the trade-offs for different values of RFEH parameters, trading off the total cost of implementation with the system's lifetime, based on total energy consumed.

This report is about how to design and fabricated a wireless energy transfer system which is printed on flexible photo paper. That is a technology used to print conducting tracks on paper, or even entire circuit system. The circuit of wireless energy transmission is half bridge converter with spiral coil as the inductance which are etching in primary side and printing in secondary side.The procedure of fabrication will be introduced. While realizing the feasible simulation circuit, the optimal transmission energy system components are mounted according to the requirements. While looking for the best efficiency, it’s also neccessary to consider the appropriate size of the system. In the end of this report there will be some analysis which is aimed to identify where the largest electrical losses are located. Compared with ordinary PCB circuit board, printed circuit on paper makes the whole system very flexible and portable. When the primary side as close with secondary side, The efficiency is almost 72% while the 60Ω as the load. The output power is 10.68w. On the other hand, the ink of printed circuit on paper has high resistivity, which affects the efficiency of radio power system. However, for different paper substrates, the efficiency of wireless charging system will not be affected.

Šiandien neįsivaizduojame savo gyvenimo be daugybės elektrą naudojančių prietaisų, kurie su elektros šaltiniu sujungti elektros laidais. Daugybė besipainiojančių laidų tikrai nepadaro mūsų gyvenimo lengvesnio ir patogesnio. Dar labiau komplikuoja situacijas, kai neįmanoma panaudoti laidų ir prisijungti prie energijos šaltinio. Visi šie nepatogumai skatina ieškoti išeities, kuri galėtų būti energijos perdavimas be laidų. Bevielės elektros idėja buvo iškelta jau prieš daugiau kaip šimtą metų, tai buvo išradėjo Nikola Tesla mintis. Darbui nagrinėti pagamintas bevielės energijos perdavimo demonstracinis modelis. Modelyje nagrinėjama bevielės energijos perdavimo sistema, veikimo principas, siunčiamos energijos nuotolis, perduodamos energijos efektyvumas bei gaunami nuostoliai. / Today can not imagine our life without many electricity using devices are connected to a source of electrical power lines. Numerous trailing wires really does not make our lives easier and more comfortable. Even more complicated situations when there are no opportunities to use wire and to connect to a power source. All these inconveniences are promoting to search the solution wich could be the transfer of energy without wires. Wireless energy idea has been raised for more than a hundred years ago. The thought was inventor inventored by Nikola Tesla. Analyzing this work there was made a demonstration model of wireless power transmission. In this model there is analyzing such things as wireless transmission system, working principal, transmitted energy distance and obtained losses.

Energy harvesting can be used to consume the potential power of the surrounding environment. This harvesting can be done in different ways, some common energy harvesting modalities are vibrations, heat differences, solar power, and RF energy. In this Master Thesis, these different methods for harvesting energy are studied and the one that is the most suitable for an environment inside an embankment dam is further explored. If some energy harvesting modalities can operate well in that environment then it might be possible to monitor the embankment dam from the inside. The hope is to create an energy harvesting platform equipped with some suitable sensors which can be placed inside an embankment dam and collect data for a longer duration of time. Considering how an embankment dam is structured, it was concluded that the best possible energy harvesting method is wireless ultra-high frequency radio signals. An RF energy harvesting platform was created and tested, both in a laboratory and buried underground, mimicking the environment inside an embankment dam. These tests were measured and the results showed some promise that it is possible to use this energy harvesting method to power a sensor platform underground.

This research has its basis in developments within the field of inductive powering and wireless power transfer, WPT, and more specifically one the branch within this field, which is called magnetic resonance coupling. This principle enables efficient power transfer from a transmitting unit to a receiving unit at a distance of some times the unit diameter. The developments within magnetic resonant coupling are together with the possibilities and challenges of today’s smart textile industry the starting point to investigate a novel textile-based product concept for WPT by combining both technologies. Multiple textile samples, consisting of cotton and electrically conductive copper yarns, were produced by weaving technique, additional assembling of electronic components were performed manually and several measurements were carried out to investigate the sample characteristics and the sample performance in terms of power transfer. The produced samples showed to behave similarly to conventional inductors and were able to transfer power over some distance.

Afin d'augmenter l'efficacité et la sécurité du trafic routier, de nouveaux concepts et technologies ont été développés depuis 1992 en Europe pour les applications RTTT (Road Traffic & Transport Telematics). Ces applications utilisent les équipements DSRC qui supportent les transmissions à courte distance à 5.8GHz. Vues la fiabilité et le succès de cette technologie, l'utilisation de ces équipements est ensuite étendue aux ETC (Electronic Toll Collection) ou Télépéage et aussi dans une multitude d'autres domaines d'application comme la gestion des flottes, le transport public et la gestion des parkings. Le système DSRC se compose d'un émetteur/récepteur (lecteur) et des transpondeurs (badges). En toute logique, l'approche industrielle oriente les développements vers la technologie de transpondeur semi passif qui, pour réémettre un signal utilise le signal transmis par l'émetteur–récepteur, effectue une modulation de phase d'une sous porteuse fréquentielle encodant ainsi les données à transmettre. Cette conception évite l'utilisation des oscillateurs locaux, comme dans les transpondeurs actifs, pour générer l'onde Radio Fréquence (RF). Ceci permet de produire des transpondeurs relativement à faible coût et de petite taille. Cependant ce concept nécessite quand même une batterie au Lithium pour assurer le fonctionnement du transpondeur pour une durée de 4 à 6 ans et ce malgré les progrès des technologies de circuits intégrés à faible consommation. Au fur et à mesure de l'expansion de ces équipements, il s'avère qu'avec les années la quantité des batteries au lithium à détruire deviendrait un problème crucial pour l'environnement. Aujourd'hui, la conception d'un transpondeur DSRC complètement autonome n'est pas faisable, car la quantité d'énergie nécessaire s'avère encore élevée (mode actif 8 mA/3.6 V). Néanmoins, la réduction de la consommation électrique du transpondeur, permet au moins doubler la durée de vie de la batterie et pourrait être un bon point de départ pour améliorer la protection de l'environnement.Dans cette thèse, nous proposons un nouveau transpondeur DSRC avec un diagramme d'état original qui réduit considérablement la consommation énergétique. Après validation d'un nouvel état de fonctionnement en mode très faible consommation d'énergie, nous avons étudié la possibilité de recharger la batterie du transpondeur à travers de la récupération d'énergie sans fil. Le bilan de liaison énergétique DSRC a été réalisé afin d'estimer la quantité d'énergie disponible quand une voiture avec un transpondeur passe à sous un système de péage. Toutefois, le bilan énergétique à 5.8 GHz présente une faible densité d'énergie RF, puisque la voiture ne reste pas assez sur le lobe de l'antenne DSRC afin de procéder à la récupération d'énergie. Par conséquent, nous avons alors exploré une autre fréquence ISM, le 2.45 GHz dans laquelle la présence d'émetteurs est bien plus grande. Dans le chapitre de récupération d'énergie sans fil nous présentons la conception et l'optimisation d'un nouveau récupérateur d'énergie RF. Après avoir démontré qu'une charge RF-DC optimale est nécessaire afin d'atteindre une haute efficacité de conversion RF-DC. Plusieurs redresseurs et rectennas ont été conçus pour valider les études numériques. Parmi, les résultats présentés dans cette thèse les rendement de conversion obtenus sont à l'état de l'art de la récupération d'énergie sans fil pour une très faible densité de puissance disponible. / To increase the efficiency and safety of the road traffic, new concepts and technologies have been developed in Europe since 1992 for RTTT applications (Road Traffic & Transport Telematics). These applications use the Dedicated Short Range Communications (DSRC) devices at 5.8 GHz (ISM band). In view of the reliability and success of this technology, the use of such equipment is thus extended to the EFC (Electronic Fee Collection) or e-toll and also in many other application areas such as fleet management, public transport and parking management. Due to the broad applications, these equipments are subject to various standards CEN/TC 278, CEN ENV (EN) 12253, ETSI, etc.... The DSRC system consists in a transceiver (reader) and transponders (tags). Industrial approaches are oriented to semi-passive transponder technology, which uses the same signal sent by the reader to retransmit, performing a frequency shift and encoding data to be transmitted. This design avoids the use of the local oscillators to generate the RF wave, as in active transponders, and save electrical energy of batteries. This allows the development of relatively low cost and small size transponders. Despite advances in integrated low-power circuits technology, this concept still requires a lithium battery to operate the transponder for a period of 4-6 years. However, with the expansion of these facilities, it appears that over the years the amount of lithium to destroy has become a crucial problem for the environment. Nowadays designing a completely autonomous DSRC transponder is not feasible, since the amount of energy required is still high (8 mA/3.6 V active mode). Nevertheless, reducing the transponder electrical power consumption, as a solution to at least double the battery life, could be a good start point to improve environment protection.In this thesis we propose a new DSRC transponder with an original statechart that considerably reduces the power consumption. After validation of the new low-power consumption mode, we studied the possibility to recharge the battery of the transponder by means of Wireless Energy Harvesting. The DSRC Toll Collection RF link budget was carried out in order to estimate the amount of energy available when a car with a transponder passes through a toll system. However, RF link budget at 5.8 GHz presents a low power density, since the car does not stay enough on the DSRC antenna's field to proceed to energy harvesting. Therefore we explored another ISM frequency, the 2.45 GHz. Thus the Wireless Energy Harvesting chapter aims to further the state of the art through the design and optimization of a novel RF harvesting board design. We demonstrated that an optimum RF-DC load is required in order to achieve high RF-DC conversion efficiency. Several rectifiers and rectennas were prototyped in order to validate the numerical studies. Finally, the results obtained in this thesis are in the forefront of the State-of-the-Art of Wireless Energy Harvesting for very low available power density.

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

Wireless sensor networks (WSN) are one of the most rapidly evolving technical areas right now. They can be used in a lot of different applications, environmental monitoring and health applications being two examples. The sensors can be placed in hazardous and remote environments since there is no need for cabling or manual maintenance. One of the biggest problems and constraints of today's WSNs is the limited energy capacity of the sensor nodes. Eventually they will be power-depleted, and the network will be dead. A solution to this can be wireless energy transfer technology which makes it possible to recharge sensor nodes with the help of a mobile charger and prolong the lifetime of networks.  This thesis aims to investigate how the charging completion time can be reduced by considering that the charger can charge while moving and by controlling its velocity. The charging completion time is the time from when the charger starts charging the first node until it returns to that starting point.  For this, a two-dimensional WSN was defined that contains sensor nodes and a mobile charger. Anchor nodes, which the charger travels between were defined by choosing the nodes with most neighbors within a defined charging radius. The traveling salesman problem were used to find a path that the charger travels along. A simulation environment was developed in Python to execute tests.  The results show that the charging while moving approach with controlled velocity could reduce the charging completion time with up to 20%. It was also ascertained that this approach works better in dense networks than in sparse. / Trådlösa sensornätverk är ett av de snabbast växande tekniska områdena just nu. De har många olika användningsområden, miljöövervakning och olika hälsotillämpningar är två exempel. Sensorerna kan placeras i farliga och avlägsna miljöer eftersom det inte finns något behov av kablar eller manuellt underhåll. Ett av de största problemen och begränsningarna på dagens trådlösa nätverk är den begränsade energikapaciteten hos sensornoderna. Slutligen kommer de att bli tömda på ström och nätverket kommer att dö. En lösning på detta kan vara trådlös strömöverföring vilket gör det möjligt att ladda sensorerna med hjälp av en mobil laddare och därmed förlänga livstiden på nätverket.  Syftet med detta examensarbete är att undersöka hur slutförandetiden för laddningen kan reduceras i betraktande av att laddaren kan ladda när den rör sig samt att reglera laddaren hastighet. Slutförandetiden för laddningen är den tid det tar från att laddaren börjar ladda den första sensor-noden tills att den kommer tillbaka till punkten den startade på.  För att göra detta definierades ett två-dimensionellt trådlöst sensornätverk som bestod av sensornoder och en mobil laddare. Ankarnoder, vilka laddaren rörde sig emellan, definierades genom att hitta de noder med flest antal grannar inom en bestämd laddningsradie. Handelsresandeproblemet användes för att bestämma rutten som laddaren färdas längs. En simuleringsmiljö utvecklades i Python för att utföra testerna i.  Resultaten visar att med laddaren som laddade när den rörde på sig samt hade reglerad hastighet kunde slutförande-tiden för laddning reduceras med upp till 20%. Det kunde även konstateras att detta tillvägagångssätt fungerar bättre i täta nätverk än i glesa.