Design of Node Power Management in WSN Based on Ant Colony Algorithm

被引：1

作者：

Li Hongsheng ^{[1
]}

Liu Sumin ^{[2
]}

Hu Bing ^{[2
]}

机构：

[1] Wuhan Univ Technol, Dept Automat, Wuhan 430070, Hubei, Peoples R China

[2] Wuhan Univ Technol, Mech &Automat, Wuhan, Peoples R China

来源：

NSWCTC 2009: INTERNATIONAL CONFERENCE ON NETWORKS SECURITY, WIRELESS COMMUNICATIONS AND TRUSTED COMPUTING, VOL 1, PROCEEDINGS | 2009年

关键词：

wireless sensor networks; node power management; ant colony algorithm; network routing; OPTIMIZATION;

D O I：

10.1109/NSWCTC.2009.212

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Appropriate node management of networks in the limited resources of the wireless sensor network for effectively control the energy of networks is one of the key points in Research on wireless sensor networks. Using of ant colony algorithm's characteristics, self-organized, adaptive and dynamic optimization, this paper proposed a new power management which can reduce the node power and re-establish of a new node routing links, to enhance the viability of the entire network, improve the life of the network. Simulation results show that the ant colony algorithm can be applied to node power management of wireless sensor network a good way with obvious energy-saving.

引用

页码：739 / +

页数：3

共 7 条

[1] The hyper-cube framework for ant colony optimization [J].

Blum, C ;

Dorigo, M .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (02) :1161-1172

[2] Ant system: Optimization by a colony of cooperating agents [J].

Dorigo, M ;

Maniezzo, V ;

Colorni, A .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01) :29-41

[3] A modified ant colony optimization algorithm modeled on tabu-search methods [J].

Ho, SL ;

Yang, SY ;

Ni, GZ ;

Machado, JM .

IEEE TRANSACTIONS ON MAGNETICS, 2006, 42 (04) :1195-1198

[4]

KUBISCH M, 2003, P IEEE WCNC 2003 NEW

[5]

PERING T, 1998, P POW DRIV MICR WORK

[6] Dynamic power management in wireless sensor networks [J].

Sinha, A ;

Chandrakasan, A .

IEEE DESIGN & TEST OF COMPUTERS, 2001, 18 (02) :62-74

[7] A short convergence proof for a class of ant colony optimization algorithms [J].

Stützle, T ;

Dorigo, M .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (04) :358-365

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