Meta-optimization for charger deployment in wireless rechargeable sensor networks

Jehn Ruey Jiang, Yen Chung Chen, Chung Hsien Tsai, Zong Syun Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes the genetic particle swarm optimization charger deployment (GPSOCD) algorithm to deploy as few chargers as possible to fulfill the charging demands of sensors of a wireless rechargeable sensor network (WRSN) to maintain the WRSN sustainability. The proposed GPSOCD algorithm is a meta-optimization algorithm since it uses the genetic algorithm (GA) to encode and optimize the parameters of a particle swarm optimization charger deployment (PSOCD) algorithm. The PSOCD algorithm in turn relies on the particle swarm optimization (PSO) concept for optimizing the charger deployment with the parameters generated by the GA. Simulations are conducted for the performance evaluation of the GPSOCD algorithm. The simulation results show that GPSOCD outperforms other related algorithms.

Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-188
Number of pages4
ISBN (Electronic)9781728193335
DOIs
StatePublished - 21 Aug 2020
Event3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 - Kaohsiung, Taiwan
Duration: 21 Aug 202023 Aug 2020

Publication series

NameProceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020

Conference

Conference3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020
Country/TerritoryTaiwan
CityKaohsiung
Period21/08/2023/08/20

Keywords

  • Genetic algorithm
  • Meta-optimization
  • Particle swarm optimization
  • Wireless rechargeable sensor network

Fingerprint

Dive into the research topics of 'Meta-optimization for charger deployment in wireless rechargeable sensor networks'. Together they form a unique fingerprint.

Cite this