Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

Efficient Energy Supply Using Mobile Charger for Solar-Powered Wireless Sensor Networks

An energy-harvesting wireless sensor network mitigates the energy shortage problems of existing battery-based wireless sensors; however, its hotspot area sensor nodes still experience 3 blackouts, thereby reducing network connectivity. Techniques that transfer energy directly to sensor nodes using wireless power transfer (WPT) have been studied in recent years to address this issue. In this paper, we propose a technique that uses a drone (quadcopter), which is a type of unmanned aerial vehicle (UAV), as a mobile sink. The drone selects and manages anchor nodes that aggregate data temporarily, collects data by visiting the anchor nodes to mitigate the hotspot issue, and then prevents blackouts by supplying energy to low-energy nodes, thereby improving network connectivity. The anchor nodes are carefully selected after considering the energy capacity of the drone, the size of the network, the amount of collected data, and the energy consumed by the nodes to increase the network’s energy efficiency. Furthermore, energy is transferred from the drone to the anchor nodes to support their energy consumption. In our study, this method reduced the blackouts of sensor nodes, including anchor nodes, in hotspot regions, and increased network connectivity, thereby improving the amount of data gathered by the mobile sink.

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network

Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limited traveling distance and speed, not every node that needs to be charged may be serviced in time. Thus, in such scenario, how to make a route plan for the mobile charger to determine which nodes should be charged first is a critical issue related to the network’s Quality of Service (QoS). In this paper, we propose a mobile charger’s scheduling algorithm to mitigate the data loss of network by considering the node’s criticality in connectivity and energy. First, we introduce a novel metric named criticality index to measure node’s connectivity contribution, which is computed as a summation of node’s neighbor dissimilarity. Furthermore, to reflect the node’s charging demand, an indicator called energy criticality is adopted to weight the criticality index, which is a normalized ratio of the node’s consumed energy to its total energy. Then, we formulate an optimization problem with the objective of maximizing total weighted criticality indexes of nodes to construct a charging tour, subject to the mobile charger’s traveling distance constraint. Due to the NP-hardness of the problem, a heuristic algorithm is proposed to solve it. The heuristic algorithm includes three steps, which is spanning tree growing, tour construction and tour improvement. Finally, we compare the proposed algorithm to the state-of-art scheduling algorithms. The obtained results demonstrate that the proposed algorithm is a promising one.