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Efficient and Accurate Localization for Mobile Wireless Sensor Networks Based on Compressive Sensing
Qiang Zhang, Jiangwen Wan, Kefu Yi, Tianyue Bao and Donghao Wang

The issue of node localization in Mobile Wireless Sensor Networks (MWSNs) has received significant attentions. In spite of obtaining good localization accuracy, existing Sequential Monte Carlo-based (SMC-based) localization algorithms either suffer from low sampling efficiency or require high beacon density to achieve low localization errors. This paper proposes a novel compressive sensing localization algorithm (CSL), which formulates the localization problem as a sparse signal recovery problem. In particular, CSL introduces a dynamic grid-based representation technique to discretize the possible node location area into small grids and employs the measured signal strength to provide localization. Instead of translating the signal strength into distances, CSL exploits the sparse structure of signal strength measurements to locate the target. Through continuously adjusting the total grids number, the proposed algorithm can handle the whole computation cost elegantly. Extensive simulation results show that compared with SMC-based methods, CSL can greatly improve the localization efficiency while achieving similar or better localization accuracy.

Keywords: Mobile wireless sensor networks; node localization; compressive sensing; sparse signal recovery; grid-based representation; signal strength

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