| 引用本文： |
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李洪兵,余成波,陈 强,等.基于BWAS的无线传感器网络静态分簇路由算法[J].电讯技术，2010，50（4）： - . [点击复制]
- LI Hong-bing,YU Cheng-bo,CHEN Qiang,et al.Static Clustering Routing Algorithm Based on Best-Worst Ant System for Wireless Sensor Networks[J].，2010，50（4）： - . [点击复制]
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| 摘要: |
| 为提高路径搜索效率，避免动态分簇较多的能量消耗，提出了基于最优-最差蚂蚁系统(BWAS
)的无线传感器
网络静态分簇路由算法。BWAS是对蚁群算法的改进，在路径搜寻过程中评价出最优最差蚂蚁
，引入奖惩机制，加快了路径搜索速度。通过无线传感器网络静态分簇、簇内动态选举簇头
，在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径，能减少路径寻优
能量消耗，实现均衡能量管理，延长网络寿命，且具有较强的鲁棒性。通过与基于BWAS的
动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较，证实了本算法的有效性。 |
| 关键词: 无线传感器网络 路由协议；BWAS算法 静态分簇 |
| DOI： |
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| 基金项目:重庆市自然科学基金重点资助项目（CSTC2007BA2023） |
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| Static Clustering Routing Algorithm Based on Best-Worst Ant System for Wireless Sensor Networks |
| LI Hong-bing,YU Cheng-bo,CHEN Qiang,RAN Yong |
| (Technology Research Institute of Remote Test and Control,Chongqing University，Chongqing 400050,China;2.Chongqing Three Gorges University,Chongq
ing 404000,China;University of Electronic Science and Technology of China,Chengdu 610054,China) |
| Abstract: |
| In order to improve the efficiency of path searching and avoid the mor
e energy consumption in dynamic clustering style,a new BWAS (best-worst ant sys
tem)-based static clustering routing algorithm for wireless sensor networks(WSNs
) is p
resented in this paper. The BWAS algorithm improves the ant colony algorithm by
evaluating the best and worst ants during the path searching process. As a resul
t it has speeded up the path searching due to introducing the reward-punishment
mechanism to guide the search. Through the static clustering in the beginning an
d later dynamic electing cluster heads in each cluster in WSNs, using BWAS-based
method to find the optimal multi-hop path from cluster-head nodes to sink node,
the energy consumption can be reduced and balanced. It also can extend the servi
ce
life and has strong robustness.Comparison with the dynamic clustering style
and ant colony-based style confirms the effectiveness of the algorithm. |
| Key words: wireless sensor network(WSN) routing protocol BWAS algorithm static clustering |
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