Intelligent linear collaborative beamforming for multi-objective radiation beam pattern in wireless sensor networks
Collaborative beamforming (CB) in wireless sensor networks (WSNs) promises improvement of communication performance and energy efficiency. The random distribution sensor nodes location within WSNs can introduce random beampattern mostly in the sidelobe region. In addition, higher energy consumption...
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| Main Authors: | , , , |
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| 格式: | Article |
| 出版: |
Institute of Advanced Engineering and Science
2014
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/59803/ http://ijeecs.iaescore.com/index.php/IJEECS/article/view/3881 |
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| 總結: | Collaborative beamforming (CB) in wireless sensor networks (WSNs) promises improvement of communication performance and energy efficiency. The random distribution sensor nodes location within WSNs can introduce random beampattern mostly in the sidelobe region. In addition, higher energy consumption can occur as the randomness permits the generation of high peaks in radiation beampattern performance. Therefore, selecting a suitable spatial sensor node distribution is a challenge especially for WSNs. Collaborative sensor nodes in random deployment which performs as linear antenna array (LAA) can influence the radiation beampattern. However, it leads to the degradation of LAA and WSNs performances. Hence, an optimum algorithm for implementing CB method should be designed that takes into consideration not only the beampattern performance, but also the geometrical location of selected active nodes which cooperatively form an array antenna. In this article, a new algorithm known as intelligent linear sensor node array (ILSA) is presented. It is developed through the application of the proposed hybrid least square improved particle swarm optimization (HLPSO) algorithm. The newly proposed ILSA is constructed by means of collaborative nodes selection. The size of side lobe level (SLL) can vary significantly with desired multi-objectives. Simulation results obtained showed significant improved performance of the radiation beampattern. Thus, this motivates for exploiting the newly-developed optimum method in node geometrical location strategies of WSNs. |
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