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RNST: Precise Localization Based on Trilateration for Indoor Sensor Networks
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Author(s): Guangjie Han (Hohai University, China), Wen Shen (Hohai University, China), Chuan Zhu (Hohai University, China), Lei Shu (Osaka University, Japan)and Joel J.P.C. Rodrigues (Instituto de Telecomunicações, University of Beira Interior, Portugal)
Copyright: 2012
Pages: 28
Source title:
Advancements in Distributed Computing and Internet Technologies: Trends and Issues
Source Author(s)/Editor(s): Al-Sakib Khan Pathan (International Islamic University Malaysia (IIUM), Malaysia), Mukaddim Pathan (Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia)and Hae Young Lee (Electronics and Telecommunications Research Institut (ETRI), South Korea)
DOI: 10.4018/978-1-61350-110-8.ch011
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Abstract
The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as Two-Phase Positioning (TPP) algorithm, are too complicated to be implemented and they can not provide the real-time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose Reference Node Selection algorithm based on Trilateration (RNST), which can provide real-time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real-time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real-time localization problem of the mobile nodes in indoor sensor networks.
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