基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配

周杰; 刘元安; 吴帆; 张洪光; 俎云霄

doi:10.7498/aps.60.090504

摘要

提出了一种基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配方法,该方法运用混沌序列和并行遗传算法来动态调整传感器网络节点的探测目标及通信时隙等参数,对资源分配方式进行跨层整体优化.在多目标无线传感器网络环境下,将本文方法与传统的随机分配方法、动态规划方法、T-MAC协议及S-MAC协议等资源分配算法进行了仿真比较.仿真结果表明,本文提出的混沌并行遗传算法具有通信时延小,目标检测成功率高等优点,在降低了无线传感器网络功率消耗的同时提高了对目标检测的实时性.

关键词:

Abstract

A chaotic parallel genetic algorithm for the allocation of a multi-objective cross-layer wireless sensor network resource is provided, in which chaotic sequence and parallel genetic algorithm are used to dynamically adjust target selection, communication time slots and other parameters for optimizing the global cross-layer resource allocation. Simulations are conducted to compare the chaotic parallel genetic algorithm method with random allocation algorithm, dynamic programming algorithm, T-MAC protocol and the S-MAC protocol separalely. The simulation results show that the chaotic parallel genetic algorithm has a small communication delay and high success rate of target detection, which reduces the power consumption and improves the real-time characteristic of wireless sensor network.

Keywords:

作者及机构信息

1.
北京邮电大学电子工程学院,北京 100876

基金项目: 国家高技术研究发展计划(863计划)(批准号:2008AA012211),国家自然科学基金青年科学基金项目(批准号:61003279)和国家自然科学基金面上项目(批准号:60973111)资助的课题.

Authors and contacts

1.
School of Electronic Engineering, Beijng University of Posts and Telecommunications, Beijing 100876, China

参考文献

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施引文献

[1]	Mukhopadhyay S, Schurgers C, Panigrahi D, Dey S 2009 IEEE Transactions on Mobile Computing 8 528
[2]	Boukerche A, Samarah S 2009 IEEE Transactions on Vehicular Technology 58 4426
[3]	Du J Z, Shi W S 2008 IEEE Transactions on Vehicular Technology 57 3723
[4]	Raymond D R, Marchany R C, Brownfield M I, Midkiff S F 2009 IEEE Transactions on Vehicular Technology 58 367
[5]	Woonsik L, Minh N, Verma A, Hwang L 2009 IEEE Transactions on Wireless Communications 8 4375
[6]	Merhi Z, Elgamel M, Bayoumi M 2009 IEEE Transactions on Mobile Computing 8 1690
[7]	Boukerche A, Samarah S 2008 IEEE Transactions on Parallel and Distributed Systems 19 865
[8]	Chen F X, Zhang W D 2007 Chin. Phys. 16 937
[9]	Wang F Q, Liu C X 2007 Chin. Phys. 16 946
[10]	Xiao Y Z, Xu W 2007 Chin. Phys. 16 1597
[11]	Wu W, Cui B T 2007 Chin. Phys. 16 1889
[12]	Kwan-Wu C 2009 IEEE Transactions on Consumer Electronics 55 1898
[13]	Akyildiz I F, Melodia T, Chowdhury K R 2008 Proceedings of the IEEE 96 1588
[14]	Kulkarni S, Iyer A, Rosenberg C 2006 IEEE/ACM Transactions on Networking 14 793
[15]	Shah-Mansouri V, Wong V 2009 IEEE Transactions on Wireless Communications 9 1924
[16]	Wang Xin, Wang Di, Zhuang H Q, Morgera S D 2010 IEEE Journal on Selected Areas in Communications 28 1063
[17]	Yan H, Wei P, Xiao X C 2009 Chin. Phys. B 18 3287
[18]	Luo X H 2009 Chin. Phys. B 18 3304
[19]	Peng J H, Yu H J 2007 Acta Phys. Sin.56 4353 (in Chinese)[彭建华、于洪洁 2007 56 4353]
[20]	Liu X W, Huang Q Z, Gao X, Shao S Q 2007 Chin. Phys. 16 2272
[21]	Sun Z K, Xu W, Yang X L 2007 Chin. Phys. 16 3226
[22]	Zhang J S 2007 Chin. Phys. 16 352
[23]	Wang G Y, Zheng Y, Liu J B 2007 Acta Phys. Sin.56 3113(in Chinese)[王光义、郑艳、刘敬彪 2007 56 3113]
[24]	Liu Y Z, Jiang C S, Lin C S, Jiang Y M 2007 Chin. Phys. 16 660
[25]	Gao B J, Lu J A 2007 Chin. Phys. 16 666
[26]	Wang X Y, Meng J 2009 Acta Phys. Sin.58 3780 (in Chinese)[王兴元、孟娟 2009 58 3780]
[27]	Wang G Y, Liu J B, Zheng X 2007 Chin. Phys. 16 2278
[28]	Xu Y, Zhang J X, Xu X, Zhou H 2007 Chin. Phys. 16 2285
[29]	Fan L, Xia G Q, Wu Z M 2009 Acta Phys. Sin.58 989(in Chinese)[樊利、夏光琼、吴正茂 2009 58 989]
[30]	Zhou P 2007 Chin. Phys. 16 1263
[31]	Lu J J, Liu C X 2007 Chin. Phys. 16 1586
[32]	Hu J B, Han Y, Zhao L D 2009 Acta Phys. Sin.58 2235 (in Chinese)[胡建兵、韩焱、赵灵冬 2009 58 2235]
[33]	Li G H 2007 Chin. Phys. 16 2608
[34]	Wang S, Cai L, Li Q, Wu G 2007 Chin. Phys. 16 2631
[35]	Gao J H, Xie L L, Peng J H 2009 Acta Phys. Sin.58 5218 (in Chinese)[高继华、谢玲玲、彭建华 2009 58 5218]
[36]	Wu Z M, Xie J Y 2007 Chin. Phys. 16 1901
[37]	Song Y Z 2007 Chin. Phys. 16 1918

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