Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Version 1 : Received: 25 September 2020 / Approved: 26 September 2020 / Online: 26 September 2020 (12:18:41 CEST)

How to cite: Gong, C.; Guo, C.; Xu, H.; Zhou, C.; Yuan, X. Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks. Preprints 2020, 2020090626 (doi: 10.20944/preprints202009.0626.v1). Gong, C.; Guo, C.; Xu, H.; Zhou, C.; Yuan, X. Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks. Preprints 2020, 2020090626 (doi: 10.20944/preprints202009.0626.v1).

Abstract

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

Subject Areas

Wireless Rechargeable Sensor Network; Coverage Optimization; Virtual Force; Particle Swarm Optimization; Queuing Game

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.