Title: Particle swarm optimisation based on Monte Carlo localisation for mobile sensor network

Authors: Chuan Xin Zhao; Ru Chuan Wang

Addresses: Department of Computer, Anhui Normal University, No. 1, East Beijing Road, Wuhu, 24100, China; School of Computer Science and Technology, Soochow University, No. 1, Shizi Street, Suzhou City, Suzhou, 215006, China. ' College of Computer, Nanjing University of Post and Telecommunications, No. 66, Xinmofang Road, Nanjing, 210003, China

Abstract: Monte Carlo localisation algorithms have been proposed for mobile sensor networks which indicate a new direction for localisation in sensor networks. In this article, we propose an improved Monte Carlo localisation algorithm based on particle swarm optimisation in order to solve the problem of sample scarcity for localisation. In the new scheme, we draw samples from box which constructed by neighbour anchors| radio ranges overlap, then use particle swarm optimisation to produce better samples by incorporating the newest measurement. Hybrid method reduces resample process and improves location accuracy. Simulation results show that proposed localisation is more accurate and effective for most condition than Monte Carlo location and Monte Carlo localisation boxed, MCB.

Keywords: Monte Carlo localisation; particle swarm optimisation; PSO; mobile sensor networks; MSNs; mobile networks; simulation.

DOI: 10.1504/IJMIC.2011.043146

International Journal of Modelling, Identification and Control, 2011 Vol.14 No.4, pp.242 - 249

Published online: 21 Mar 2015 *

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