Indoor localization techniques for wireless sensor networks

Abstract : In this thesis, the author focused on RSSI based localization algorithms for indoor applications in wireless sensor networks. Firstly, from the observation of RSSI behavior based on an experimental localization system, an experimental RSSI channel model is deduced, which is consistent to the popular lognormal shadowing path loss model. Secondly, this thesis proposes three indoor localization algorithms based on multilateration and averaged RSSI. In these algorithms, the measured distances are weighted according to their assumed accuracy. Lastly, a RSSI based parameter tracking strategy for constrained position localization is proposed. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. Quantitative criteria are provided to guarantee the efficiency of the proposed tracking strategy by providing a tradeoff between the constraint resolution and parameter variation. The simulation results show a good behavior of the proposed tracking strategy in presence of space-time variation of the propagation channel. Compared with the existing RSSI based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. In addition, experimental tracking test is performed to validate the effectiveness of the proposed tracking strategy.
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Jinze Du. Indoor localization techniques for wireless sensor networks. Electronics. UNIVERSITE DE NANTES, 2018. English. ⟨tel-01709236⟩

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