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The Journal of Supercomputing

Erschienen in:

01.07.2014

A localization algorithm for large scale mobile wireless sensor networks: a learning approach

verfasst von: Samira Afzal, Hamid Beigy

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2014

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Abstract

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm.

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Titel: A localization algorithm for large scale mobile wireless sensor networks: a learning approach
verfasst von: Samira Afzal
Hamid Beigy
Publikationsdatum: 01.07.2014
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 1/2014
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1129-6

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