Charger and receiver deployment with delay constraint in mobile wireless rechargeable sensor networks

• Published in: Ad hoc networks Vol. 126; p. 102756
• Main Authors: Yao, Haiqing, Zheng, Chaoqun, Fu, Xiuwen, Yang, Yongsheng, Ungurean, Ioan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Charger and receiver deployment; Delay constraint; Greedy search; Mobile wireless rechargeable sensor networks; Particle swarm optimization; Charger and receiver deployment; Delay constraint; Greedy search; Mobile wireless rechargeable sensor networks; Particle swarm optimization
• ISSN: 1570-8705; 1570-8713
• DOI: 10.1016/j.adhoc.2021.102756

Wireless Rechargeable Sensor Networks (WRSNs) powered by radio-frequency radiation have been discussed a lot for their sustainability and convenience, etc. One focus is how to rationally select the quantity and location of chargers to improve charging efficiency and deployment budget. This paper considers a novel problem of WRSNs in a two-dimensional space with static task points and directed paths, deploying the minimum number of wireless chargers and receivers for mobile nodes, subject to the non-overtime stay probability requirement in all Task Points (TPs). This problem is practical in delay sensitive tasks. Considering the interaction effect between the deployment of chargers and receivers, we conclude two progressive problems P1 and P2. P1 solves the deployment of chargers, subject to a preset constant as the non-overtime stay probability in all TPs. By relaxing the constraint of fixed wireless link reliability in P1, P2 solves the deployment of chargers and receivers, subject to a preset constant as the non-overtime stay probability in all TPs. Both P1 and P2 are proved to be NP-hard. We propose greedy search and particle swarm optimization based solutions to approximately solve P1 and P2, and analyze the approximation rate and time complexity of these solutions. Finally, results of extensive simulations show that the Particle Swarm Optimization (PSO) based solutions can always deploy less number of chargers and receivers, and thus is more cost-effective compared with the greedy search based solutions.