A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research

Optimal harvest-use-store design for delay-constrained energy harvesting wireless communications

Recent advances in energy harvesting (EH) technology have motivated the adoption of rechargeable mobile devices for communications. In this paper, we consider a point-to-point (P2P) wireless communication system in which an EH transmitter with a non-ideal rechargeable battery is required to send a given fixed number of bits to the receiver before they expire according to a preset delay constraint. Due to the possible energy loss in the storage process, the harvest-use-and-store (HUS) architecture is adopted. We characterize the properties of the optimal solutions, for additive white Gaussian channels (AWGNs) and then block-fading channels, that maximize the energy efficiency (i.e., battery residual) subject to a given rate requirement. Interestingly, it is shown that the optimal solution has a water-filling interpretation with double thresholds and that both thresholds are monotonic. Based on this, we investigate the optimal double-threshold based allocation policy and devise an algorithm to achieve the solution. Numerical results are provided to validate the theoretical analysis and to compare the optimal solutions with existing schemes

Power Management Strategies in Energy-Harvesting Wireless Sensor Networks

Power management strategies are extremely important in Wireless Sensor Networks (WSNs). The objective is to make the nodes operate as long as possible. In the same context, in this article, our aim is to provide the optimal transmission power to maximize the network lifetime using the Orthogonal Multiple Access Channel (OMAC) in Harvesting System (HS). We consider that the nodes have direct communication with a Fusion Center (FC) with causal Channel Side Information (CSI) at the sender and receiver.We begin the analysis by considering a single transmitter node powered by a rechargeable battery with limited capacity energy. Afterward, we generalize the analysis with M transmitter nodes. In both cases, the transmitters are able to harvest energy from nature.Eventually, we show the viability of our approach in simulations results

A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network

A wireless rechargeable sensor network (WRSN) enables charging of rechargeable sensor nodes (RSN) wirelessly through a mobile charging vehicle (MCV). Most existing works choose the MCV’s stop point (SP) at random, the cluster’s center, or the cluster head position, all without exploring the demand from RSNs. It results in a long charging delay, a low charging throughput, frequent MCV trips, and more dead nodes. To overcome these issues, this paper proposes a hybrid metaheuristic algorithm for stop point selection (HMA-SPS) that combines the techniques of the dragonfly algorithm (DA), firefly algorithm (FA), and gray wolf optimization (GWO) algorithms. Using FA and GWO techniques, DA predicts an ideal SP using the run-time metrics of RSNs, such as energy, delay, distance, and trust factors. The simulated results demonstrate faster convergence with low delay and highlight that more RSNs can be recharged with fewer MCV visits, further enhancing energy utilization, throughput, network lifetime, and trust factor

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

WIRELESS SENSOR BASED MAXIMIZING SENSOR LIFETIME USING ADAPTIVE ALGORITHM

Remote energy move innovation dependent on dazzling reverberating coupling has arisen as a cheerful innovation for remote sensor organizations, by on condition that controllable yet successive energy to sensors. The utilization of a portable charger to remotely charge sensors in a battery-powered sensor organization so the amount of sensor lifetimes is boost even as the go on an outing distance of the versatile charger is limit. Differentiating existing investigations that implicit a versatile charger should charge a sensor to its full energy ability prior to moving to charge the following sensor, we here accept that every sensor can be halfway charged so more sensors can be charged before their energy exhaustions. Under this new energy charging model, we initially plan two novel enhancement issues of booking a portable charger to charge a bunch of sensors, with the goals to augment the amount of sensor lifetimes and to limit the movement distance of the versatile charger while accomplishing the greatest amount of sensor lifetimes, individually. We at that point propose effective calculations for the issues. We at long last gauge the introduction of the proposed calculations through investigational reenactments. Proliferation results make clear that the proposed calculations are very guarantee. Particularly, the normal energy termination length per sensor by the proposed calculation for amplifying the amount of sensor lifetimes is just 9% of that by the best in class calculation while the movement distance of the portable charger constantly proposed calculation is just about from 1% to 15% longer than that by the cutting edge benchmark

Communicating Using an Energy Harvesting Transmitter: Optimum Policies Under Energy Storage Losses

In this paper, short-term throughput optimal power allocation policies are derived for an energy harvesting transmitter with energy storage losses. In particular, the energy harvesting transmitter is equipped with a battery that loses a fraction of its stored energy. Both single user, i.e. one transmitter-one receiver, and the broadcast channel, i.e., one transmitter-multiple receiver settings are considered, initially with an infinite capacity battery. It is shown that the optimal policies for these models are threshold policies. Specifically, storing energy when harvested power is above an upper threshold, retrieving energy when harvested power is below a lower threshold, and transmitting with the harvested energy in between is shown to maximize the weighted sum-rate. It is observed that the two thresholds are related through the storage efficiency of the battery, and are nondecreasing during the transmission. The results are then extended to the case with finite battery capacity, where it is shown that a similar double-threshold structure arises but the thresholds are no longer monotonic. A dynamic program that yields an optimal online power allocation is derived, and is shown to have a similar double-threshold structure. A simpler online policy is proposed and observed to perform close to the optimal policy.Comment: Submitted to IEEE Transactions on Wireless Communications, August 201

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs