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

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular Technolog

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

Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

Wireless sensor networks (WSNs), built from many battery-operated sensor nodes are distributed in the environment for monitoring and data acquisition. Subsequent to the deployment of sensor nodes, the most challenging and daunting task is to enhance the energy resources for the lifetime performance of the entire WSN. In this study, we have attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSNs. Initially, a wireless portable charging device (WPCD) is assumed which periodically travels on our proposed routing path among the nodes of the WSN to decrease their charge cycle time and recharge them with the help of wireless power transfer (WPT). Further, a scheduling scheme is proposed which creates clusters of WSNs. These clusters elect a cluster head among them based on the residual energy, buffer size, and distance of the head from each node of the cluster. The cluster head performs all data routing duties for all its member nodes to conserve the energy supposed to be consumed by member nodes. Furthermore, we compare our technique with the available literature by simulation, and the results showed a significant increase in the vacation time of the nodes of WSNs

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

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes’ power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN.We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values

Delay-Constrained Mobile Energy Charging in Wireless Sensor Networks

为了延长无线传感网的生存期,基于可充电的移动设备,研究设计了一种无线传感网中移动式能量补充的方法,移动节点可以在为传感器节点补充能量的同时收集数; 据.首先,通过将无线传感器网络监测区域分割为大小相同的子区域,该子区域内的节点组成一个簇;其次,以一个簇内的总能量为计算依据,设计移动节点的路径; 生成算法以确定能量高效的移动路线;最后,使用10种不同的随机网络拓扑图进行了仿真实验,以节点移动速度和时延为限制条件分别得到了对比数据.结果表明; ,本文提出的算法与NJNP( nearest-job-next with preemption)算法相比在时延相同的条件下( 800; s),生存期提升了6 000 s左右,在节点速度5 m/s条件下生存期提升了将近14 000; s.证明本文所提方法有效地提高了充电效率,延长了网络的生存期,可用于大规模的无线传感器网络.In order to prolong the lifetime of wireless sensor networks by using; energy-rechargeable mobile devices,this paper designs a mobile energy; replenishment method wherein a mobile element gathers data and recharges; sensors simultaneously. Firstly,the whole sensor network is divided into; several sub-regions equally and the sensors in each sub-region are; formed into a cluster. Secondly, considering the energy in a whole; cluster,the mobility path is designed to find the energy-efficient; mobile trace of the mobile element. Finally,in the simulation; experiment,we used ten different random network topologies to show the; comparisons with extensive simulation experiments under different; velocities and deadlines. The results indicate that the proposed; algorithm increases lifetime by approximately 6 000 s compared with; Nearest-Job-Next with Pre-emption( NJNP) under the deadline of 800 s.; Moreover,the proposed algorithm increases lifetime by approximately 14; 000 s compared with NJNP at velocity of 5 m/s. Thus,the proposed; algorithm can improve recharging efficiency and prolong the lifetime of; wireless sensor networks,which can be used in large-scale sensor; networks.国家自然科学基金资助项目; 福建省高等学校杰出青年科研人才培育计划资助项

Improving sensor network performance with wireless energy transfer

Through recent technology advances in the field of wireless energy transmission Wireless Rechargeable Sensor Networks have emerged. In this new paradigm for wireless sensor networks a mobile entity called mobile charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimise the trade-offs of several critical aspects of the charging process such as: a) the trajectory of the charger; b) the different charging policies; c) the impact of the ratio of the energy the Mobile Charger may deliver to the sensors over the total available energy in the network. In the light of these optimisations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a Greedy protocol, a clustering protocol and an energy balancing protocol. Finally, we propose a mobile charging protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties