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

A Multicharger Cooperative Energy Provision Algorithm Based on Density Clustering in the Industrial Internet of Things

Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIoT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable. - 2014 IEEE.Manuscript received May 17, 2019; revised June 12, 2019; accepted July 9, 2019. Date of publication July 15, 2019; date of current version October 8, 2019. This work was supported in part by the National Key Research and Development Program under Grant 2017YFE0125300, in part by the National Natural Science Foundation of China-Guangdong Joint Fund under Grant U1801264, and in part by the Jiangsu Key Research and Development Program under Grant BE2019648. (Corresponding author: Guangjie Han.) G. Han, J. Wu, H. Wang, and J. Adu Ansere are with the Department of Information and Communication Systems, Hohai University, Changzhou 213022, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).Scopu