|Table of Contents|

[1] Huang Ru, Zhu Jie, Xu Guanghui,. Energy-efficient mechanismbased on ACO for the coverage problem in sensor networks [J]. Journal of Southeast University (English Edition), 2007, 23 (2): 255-260. [doi:10.3969/j.issn.1003-7985.2007.02.021]
Copy

Energy-efficient mechanismbased on ACO for the coverage problem in sensor networks()
基于蚁群优化算法的传感器网络能量有效性覆盖机制

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
23
Issue:
2007 2
Page:
255-260
Research Field:
Computer Science and Engineering
Publishing date:
2007-06-30

Info

Title:
Energy-efficient mechanismbased on ACO for the coverage problem in sensor networks
基于蚁群优化算法的传感器网络能量有效性覆盖机制
Author(s):
Huang Ru1, Zhu Jie1, Xu Guanghui2
1Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China
2Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210016, China
黄如1, 朱杰1, 徐光辉2
1上海交通大学电子工程系, 上海 200240; 2解放军理工大学通信工程学院, 南京 210016
Keywords:
sensor networks;  coverage problem;  ant colony optimization(ACO);  energy-efficiency
传感器网络;  覆盖问题;  蚁群优化;  能量有效性
PACS:
TP393.01
DOI:
10.3969/j.issn.1003-7985.2007.02.021
Abstract:
An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm(ACO)is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage-efficiency and a longer lifetime is obtained.
提出了一种解决无线传感器网络覆盖问题的能量有效性启发式机制.该机制在节能的前提下, 实现了对目标监控区域的完全覆盖, 且覆盖精度与目标的重要性级别成正比关系.机制的实现运用了蚁群优化算法, 算法的设计过程采用了新颖的启发式因子构造方法和基于评价函数的全局信息素更新规则, 由此, 人工蚂蚁被赋予了对目标监控区域的覆盖状况和对传感器网络区域能量状况的自适应感知能力, 并通过增加优化解集中节点上的信息素量, 加速求取最优解的收敛过程.最后, 蚁群在迭代优化的基础上构建出解决无线传感器网络覆盖问题的健壮优化解, 该优化解能够在能量有效性的基础上具备良好的覆盖有效性和较长的生命周期.

References:

[1] Cardei L M, Du D Z.Improving wireless sensor network lifetime through power aware organization [J].Journal of Wireless Networks, 2005, 11(3):333-340.
[2] Hsin C, Liu M.Network coverage using low duty-cycled sensors:random and coordinated sleep algorithms [C]//Proceedings of the Third International Symposium on Information Processing in Sensor Networks.California, USA, 2004:433-442.
[3] Ye F, Zhong G, Lu S, et al.PEAS:a robust energy conserving protocol for long-lived sensor networks [C]//Proceedings of the 10th IEEE International Conference on Network Protocols.Paris, France, 2002:200-201.
[4] Huang C, Tseng Y.The coverage problem in a wireless sensor network [J].Journal of Mobile Networks and Applications, 2005, 10(4):519-528.
[5] Abrams Z, Goel A, Plotkin S.Set k-cover algorithms for energy efficient monitoring in wireless sensor networks [C]//Proceedings of the Third International Symposium on Information Processing in Sensor Networks.California, USA, 2004:424-432.
[6] Parpinelli R S, Lopes H S, Freitas A.Data mining with an ant colony optimization algorithm [J].IEEE Trans on Evolutionary Computation, 2002, 6(4):321-332.
[7] Dorigo M, Maniezzo V, Colorni A.The ant system:optimization by a colony of cooperating agents [J].IEEE Transactions on Systems, Man and Cybernetics:Part B, 1996, 26(1):29-41.
[8] Dorigo M, Gambardella L M.Ant colony system:a cooperative learning approach to the traveling salesman problem [J].IEEE Trans on Evolutionary Computation, 1997, 1(1):53-66.
[9] Zecchin Aaron C, Simpson Angus R, Maier Holger R.Parametric study for an ant algorithm applied to water distribution system optimization [J].IEEE Trans on Evolutionary Computation, 2005, 9(2):175-191.
[10] Meguerdichian S, Koushanfar F.Exposure in wireless ad-hoc sensor networks [C]//Proceedings of the 7th Annual International Conference on Mobile Computing and Networking.Rome, Italy, 2001:139-150.
[11] Bullnheimer B, Hartl R F, Strauss C.A new rank-based version of the ant system:a computational study [J].Central European Journal for Operations Research and Economics, 1999, 7(1):25-38.
[12] Dorigo M, Caro G D.Ant colony optimization:a new metaheuristic [C]//Proceedings of IEEE Congress on Evolutionary Computation.Washington, DC, USA, 1999:1470-1477.

Memo

Memo:
Biographies: Huang Ru(1975—), male, graduate;Zhu Jie(corresponding author), male, doctor, professor, zhujie@sjtu.edu.cn.
Last Update: 2007-06-20