Energy-efficient localization for mobile sensor networks based on RSS and historical information

Yongtao Zhang, Lingguo Cui, Senchun Chai

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

5 Citations (Scopus)

Abstract

Existing localization algorithms for mobile sensor networks are mostly based on the Sequential Monte Carlo (SMC) localization method. However, these methods usually suffer from low sampling efficiency. So some algorithms try to reduce the sampling area by employing position relationship with neighbor common nodes. But such algorithms need high communication cost and significantly shorten the network life time. In order to solve the energy problem and improve localization accuracy, in this paper, we proposed a new energy-efficient algorithm based on Received Signal Strength (RSS), distance and direction of the moving anchor nodes and Monte Carlo localization (MCL), so the algorithm is called RDMCL. RDMCL derives three methods based on the number of nodes' one-hop neighbor anchors to build a more effective sampling area. Simulation results show that our algorithm indeed improve localization accuracy and save energy cost of network.

Original languageEnglish
Title of host publicationProceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5246-5251
Number of pages6
ISBN (Electronic)9781479970179
DOIs
Publication statusPublished - 17 Jul 2015
Event27th Chinese Control and Decision Conference, CCDC 2015 - Qingdao, China
Duration: 23 May 201525 May 2015

Publication series

NameProceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015

Conference

Conference27th Chinese Control and Decision Conference, CCDC 2015
Country/TerritoryChina
CityQingdao
Period23/05/1525/05/15

Keywords

  • Mobile sensor networks
  • localization
  • sequential monte carlo

Fingerprint

Dive into the research topics of 'Energy-efficient localization for mobile sensor networks based on RSS and historical information'. Together they form a unique fingerprint.

Cite this