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|Title:||A self-adaptive particle swarm optimization based multiple source localization algorithm in binary sensor networks||Authors:||Cheng, L
|Issue Date:||2015||Publisher:||Hindawi Publishing Corporation||Source:||International journal of distributed sensor networks, 2015, v. 2015, 487978 How to cite?||Journal:||International journal of distributed sensor networks||Abstract:||With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.||URI:||http://hdl.handle.net/10397/43640||ISSN:||1550-1329||EISSN:||1550-1477||DOI:||10.1155/2015/487978||Rights:||Copyright © 2015 Long Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Cheng, L., Wang, Y., & Li, S. (2015). A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks. International Journal of Distributed Sensor Networks is available at https://doi.org/10.1155/2015/487978
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