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

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

Efficient on-demand multi-node charging techniques for wireless sensor networks

This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-node power transfer through a mobile charger (MC).The proposed solution, called On-demand Multi-node Charging (OMC), features an original threshold-based tour launching (TTL) strategy, using request grouping, and a path planning algorithm based on minimizing the number of stopping points in the charging tour. Contrary to existing solutions, which focus on shortening the charging delays, OMC groups incoming charging requests and optimizes the charging tour and the mobile charger energy consumption. Although slightly increasing the waiting time before nodes are charged, this allows taking advantage of multiple simultaneous charges and also reduces node failures. At the tour planning level, a new modeling approach is used. It leverages simultaneous energy transfer to multiple nodes by maximizing the number of sensors that are charged at each stop. Given its NP-hardness, tour planning is approximated through a clique partitioning problem, which is solved using a lightweight heuristic approach. The proposed schemes are evaluated in offline and on-demand scenarios and compared against relevant state-of-the-art protocols. The results in the offline scenario show that the path planning strategy reduces the number of stops and the energy consumed by the mobile charger, compared to existing offline solutions. This is with further reduction in time complexity, due to the simple heuristics that are used. The results in the on-demand scenario confirm the effectiveness of the path planning strategy. More importantly, they show the impact of path planning, TTL and multi-node charging on the efficiency of handling the requests, in a way that reduces node failures and the mobile charger energy expenditure

Control and optimization approaches for power management in energy-aware battery-powered systems

Thesis (Ph.D.)--Boston UniversityThis dissertation is devoted to the power management of energy-aware battery-powered systems (BPSs). Thanks to the popularization of wireless and mobile devices, BPSs are increasingly and widely used. However, the development of BPSs is hindered by the short lifetime of batteries and limited accessibility to charging sources. The first part of this dissertation focuses on the power management of BPSs based on an analytical non-ideal battery model, the Kinetic Battery Model (KBM). How to control discharge and recharge processes of the BPS to optimize the system performance is investigated. Problems for single-battery systems and multi-battery systems are studied. In the single-battery case, the calculus of variations approach gives analytical solutions to the cases with both fully and partially available rechargeability. The results are consistent with the ones derived under a different non-ideal battery model, demonstrating the validity of the solution to the general non-ideal battery systems. In the multi-battery systems, in order to maximize the minimum terminal residual energy among all batteries, the similar methodology is employed to show an optimal policy making equal terminal energy values of all batteries as long as such a policy is feasible, which simplifies the derivations of the solution. Furthermore, the KBM is introduced into a routing problem for lifetime maximization in wireless sensor networks (WSNs). The solution not only preserves the properties of the problem based on an ideal battery model but also shows the applicability of the KBM to large network problems. The second part of the dissertation is focused on BPV systems. First, the energy-aware behavior of electric vehicles (EVs) is studied by addressing two motion control problems of an EV, (a) cruising range maximization and (b) traveling time minimization, based on an EV power consumption model. Approximate controller structures are proposed such that the original optimal control problems are transformed into nonlinear parametric optimization problems, which are much easier to solve. Finally, motivated by the significant role of recharging in BPVs, the vehicle routing problem with energy constraints is investigated. Optimal routes and recharging times at charging stations are sought to minimize the total elapsed time for vehicles to reach the destination. For a single vehicle, a mixed-integer nonlinear programming (MINLP) problem is formulated. A decomposition method is proposed to transform the MINLP problem into two simpler problems respectively for the two types of decision variables. Based on this, a multi-vehicle routing problem is studied using a flow model, where traffic congestion effects are considered are included. Similar approaches to the single vehicle case decompose the coupling of the decision variables, thus making the problem easier to solve

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

A Sensor Network System for Monitoring Short-Term Construction Work Zones

Safety hazards encountered near construction work zones are high, both in number and in the kind. There is a need to monitor traffic in such construction zones in order to improve driver and vehicle safetyIn the past traffic monitoring systems were built with high cost equipment such as inductive plates, video cameras etc. These solutions are too cost{prohibitive and invasive to be used in the large. Wireless sensor networks provide an opportunity space that can be used to address this problem. This thesis specifically targets temporary or short-term construction work zones. We present the design and implementation of a sensor network system targeted at monitoring the flow of traffic through these temporary construction work zones. As opposed to long-term work zones which are common on highways, short-term or temporary work zones remain active for a few hours or a few days at most. As such, instrumenting temporary work zones with monitoring equipment similar to those used in long-term work zones is not practical. Yet, these temporary work zones present an important problem in terms of crashes occurring in and around them. The design for this sensornet-based system for monitoring traffic is (a) inexpensive, (b) rapidly deployable, (c) requires minimal maintenance and (d) non-invasive. In this thesis we present our experiences in building this system, and testing this system in live work zones in the Greater Cleveland are

Mobile ad hoc networks in transportation data collection and dissemination

The field of transportation is rapidly changing with new opportunities for systems solutions and emerging technologies. The global economic impact of congestion and accidents are significant. Improved means are needed to solve them. Combined with the increasing numbers of vehicles on the road, the net economic impact is measured in the many billions of dollars. Promising methodologies explored in this thesis include the use of the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANET). Interconnecting vehicles using Dedicated Short Range Communication technology (DSRC) brings many benefits. Integrating DSRC into roadway vehicles offers the promise of reducing the problems of congestion and accidents; however, it comes with risks such as loss of connectivity due to power outages as well as controlling and managing loading in such networks. Energy consumption of vehicle communication equipment is a crucial factor in high availability sensor networks. Sending critical emergency messaged through linked vehicles requires that there always be energy and communication reserves. Two algorithms are described. The first controls energy consumption to guarantee an energy reserve for sending alert signals. The second exploits Long Term Evolution (LTE) to guarantee a reliable communication path