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RSSI-based Localization Algorithms using Spatial Diversity in Wireless Sensor Networks

Abstract : Many localization algorithms in Wireless Sensor Networks (WSNs) are based on received signal strength indication (RSSI). Although they present some advantages in terms of complexity and energy consumption, RSSI values, especially in indoor environments, are very unstable due to fading induced by shadowing effect and multipath propagation. In this paper, we propose a comparative study of RSSI-based localization algorithms using spatial diversity in WSNs. We consider different kinds of single / multiple antenna systems: Single Input Single Output (SISO) system, Single Input Multiple Output (SIMO) system, Multiple Input Single Output (MISO) system and Multiple Input Multiple Output (MIMO) system. We focus on the well known trilateration and multilateration localization algorithms to evaluate and compare different antenna systems. Exploiting spatial diversity by using multiple antenna systems improve significantly the accuracy of the location estimation. We use three diversity combining techniques at the receiver: Maximal Ratio Combiner (MRC), Equal Gain Combining (EGC) and Selection Combining (SC). The obtained results show that the localization performance in terms of position accuracy is improved when using multiple antennas. Specifically, using multiple antennas at the both sides present better performance than using multiple antennas at the transmitter as well as the receiver side. We also conclude that MRC diversity combining technique outperforms EGC that as well outperforms SC.
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Contributor : Abderrezak Rachedi Connect in order to contact the contributor
Submitted on : Thursday, July 17, 2014 - 2:29:23 PM
Last modification on : Saturday, January 15, 2022 - 3:56:18 AM
Long-term archiving on: : Monday, November 17, 2014 - 4:25:07 PM


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Safa Hamdoun, Abderrezak Rachedi, Abderrahim Benslimane. RSSI-based Localization Algorithms using Spatial Diversity in Wireless Sensor Networks. IJAHUC - International Journal of Ad Hoc and Ubiquitous Computing, Inderscience, 2015, 19 (3-4), pp.157-167. ⟨10.1504/IJAHUC.2015.070592⟩. ⟨hal-00977319⟩



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