Distributed mobile wireless sensor node localisation using RSSI-aided Monte Carlo method

Abstract

Localisation, also known as positioning, is a key issue in mobile wireless sensor networks. There are different positioning algorithms for low-cost sensor nodes in the literature; but most of them are focused on the basic idealised scenario of the free-space radio propagation model. In this paper, a new algorithm is proposed based on Monte Carlo localisation for positioning mobile wireless sensor nodes in the more challenging scenario of the shadowing radio path loss propagation model. The received signal strength indicator (RSSI) is integrated into the Monte Carlo algorithm as an undemanding method of distance estimation. Besides, multilateration based on the concept of radical axes and the use of Least Squares is also proposed to increase the number of localised nodes. The key difference with previous works comes from an extension of the concept of neighbourhood of nodes which is more suitable for shadowing channels. The proposed algorithms show an improvement in the localisation precision compared with other works in the literature.