A Collaborative and Predictive Localization Algorithm for Wireless Sensor Networks

• Yuan Liu,
• Junjie Chen,

Vol. 11, No. 7, July 30, 2017
10.3837/tiis.2017.07.010, Download Paper (Free):

• Wireless Sensor Networks
• localization algorithms
• non-localizable problem
• Gaussian mixture model
• collaborative and predictive schemes

Abstract

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

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