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Published in: Cognitive Neurodynamics 2/2015

01-04-2015 | Research Article

An improved localization algorithm based on genetic algorithm in wireless sensor networks

Authors: Bo Peng, Lei Li

Published in: Cognitive Neurodynamics | Issue 2/2015

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Abstract

Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

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Metadata
Title
An improved localization algorithm based on genetic algorithm in wireless sensor networks
Authors
Bo Peng
Lei Li
Publication date
01-04-2015
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 2/2015
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-014-9324-y

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