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Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

2015/03/18 by Gemma Morral, Morral, Gemma, Pascal Bianchi +1
Computer Science · Engineering · Mathematics · #Distributed #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Parallel #Target Tracking and Data Fusion in Sensor Networks #and Cluster Computing (cs.DC) #cs.DC #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1503.05298

32 pages, 5 figures, 1 table

arxiv created 2015/03/18 · openalex publication_date 2015/03/18 · arxiv updated 2015/03/19 · openalex created_date 2022/10/15 · openalex updated_date 2026/07/28

Abstract

The present work considers the localization problem in wireless sensor networks formed by fixed nodes. Each node seeks to estimate its own position based on noisy measurements of the relative distance to other nodes. In a centralized batch mode, positions can be retrieved (up to a rigid transformation) by applying Principal Component Analysis (PCA) on a so-called similarity matrix built from the relative distances. In this paper, we propose a distributed on-line algorithm allowing each node to estimate its own position based on limited exchange of information in the network. Our framework encompasses the case of sporadic measurements and random link failures. We prove the consistency of our algorithm in the case of fixed sensors. Finally, we provide numerical and experimental results from both simulated and real data. Simulations issued to real data are conducted on a wireless sensor network testbed.

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