Localization refinement for wireless sensor networks

Authors:
Jiangwen Wan;Ning Yu;Renjian Feng;Yinfeng Wu;Changming Su
Affiliations:
Sensory Lab, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics (BUAA), 37 Xueyuan Road, Haidian District, Beijing, China;Sensory Lab, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics (BUAA), 37 Xueyuan Road, Haidian District, Beijing, China;Sensory Lab, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics (BUAA), 37 Xueyuan Road, Haidian District, Beijing, China;Sensory Lab, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics (BUAA), 37 Xueyuan Road, Haidian District, Beijing, China;Sensory Lab, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics (BUAA), 37 Xueyuan Road, Haidian District, Beijing, China
Venue:
Computer Communications
Year:
2009

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Abstract

Being a key supporting technology, localization is of significance to the research and implement of other attracting applications in wireless sensor networks. The positioning accuracy and energy consumption are two major indicators of localization performance. In this paper, we are concerned with the problem of optimization for coordinates calculation in localization process, regardless of the specific underlying localization mechanism. In order to improve accuracy and avoid extra energy consumption, we develop three schemes based on least squares and multilateration: Taylor-LS, WLS and CTLS. Moreover, we develop a generalized Cramer-Rao lower bound on the localization errors in multihop scenario to help us theoretically analyze the performance of our localization refinement methods. We evaluate the proposed methods via simulations under various environmental settings. Overall, the simulation results validate the practicality of our proposed localization refinement methods, and show that they outperform multilateration at a certain extent under a wide range of conditions and have no extra communication overhead.