Improving sensing coverage of wireless sensor networks by employing mobile robots

Jize Li*, Kejie Li, Wei Zhu

*Corresponding author for this work

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

36 Citations (Scopus)

Abstract

To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
PublisherIEEE Computer Society
Pages899-903
Number of pages5
ISBN (Print)9781424417582
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Robotics and Biomimetics, ROBIO - Yalong Bay, Sanya, China
Duration: 15 Dec 200718 Dec 2007

Publication series

Name2007 IEEE International Conference on Robotics and Biomimetics, ROBIO

Conference

Conference2007 IEEE International Conference on Robotics and Biomimetics, ROBIO
Country/TerritoryChina
CityYalong Bay, Sanya
Period15/12/0718/12/07

Keywords

  • Mobile robot
  • Node density
  • Particle swarm genetic optimization
  • Sensing coverage
  • Wireless sensor networks

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