Distributed RSS-based Localization in Wireless Sensor Networks Using Convex Relaxation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper we address the sensor localization problem in large-scale wireless sensor networks (WSNs) by using the received signal strength (RSS) measurements. Finding the maximum likelihood (ML) estimate involves solving a non-convex optimization problem, thus making the search for the globally optimal solution hard. Based on the second-order cone programming (SOCP) relaxation, two methods which solve the localization problem in a completely distributed manner are proposed. Computer simulations show that the proposed approaches work well in various scenarios, and efficiently solve the localization problem.
Original languageUnknown
Title of host publicationComputing, Networking and Communications (ICNC), 2014 International Conference on
Pages853 - 857
DOIs
Publication statusPublished - 1 Jan 2014
EventInternational Conference on Computing, Networking and Communications (ICNC), 2014 -
Duration: 1 Jan 2014 → …

Conference

ConferenceInternational Conference on Computing, Networking and Communications (ICNC), 2014
Period1/01/14 → …

Cite this

Beko, M., & Dinis, R. M. H. D. M. (2014). Distributed RSS-based Localization in Wireless Sensor Networks Using Convex Relaxation. In Computing, Networking and Communications (ICNC), 2014 International Conference on (pp. 853 - 857) https://doi.org/10.1109/ICCNC.2014.6785449