An Effective Heuristic DSR

Article Preview

Abstract:

In this paper, we come up with an optimal DSR based on ant colony algorithm to improve the operational efficiency of wireless sensor network. Firstly we introduce the concepts of virtual grid and effective information propagation radius, then accord to the sensor’s translational speed and the rest energy to estimate the sensors’ persistence of life; besides, depend on the radius of effective information propagation we set the size of virtual grid and use this virtual grid as the unit to divide clusters. Finally, we realize the goal of steady information translation and effective energy usage. Under the same simulation environment, we compared the running results of DSR, LEACH and HDSR, the results illustrate that HDSR is not only able to improve the efficiency of information transportation but also equipoise the energy cost, it effectively increases the robustness of WSN.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

645-651

Citation:

Online since:

June 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] FU Hua, HAN SHuang. Optimal Sensor Node Distribution Based on the New Quantum Genetic Algorithm[J]. Chinese Journal of Sensors and Actuators, 2008, 21(7): 1259-1263.

Google Scholar

[2] XU Wei-ke. Improvement of LEACH Protocol Clustering Mechanisms[J]. Computer and Modernization, 2010, 183(6): 72-79.

Google Scholar

[3] LIU Qing, WANG Pei-kang. Secure and Energy-Efficient Clustered Routing Protocol for Wireless Sensor Networks[J]. Computer Simulation, 2009, 26(4): 167-171.

Google Scholar

[4] GAN Yong, ZHANG Li, LI Rui-chang, QIAO Yin-hua. Design and implementation of Multi-Local LEACH routing algorithm[J]. Zhengzhou Univ. ofLight Ind, 2010, 3(2): 1-5.

Google Scholar

[5] ZHAO Fu-qiang; SUN Xue-mei. Dynamic Source Routing Protocol Based on Cluster[J]. Computer Simulation, 2006, 23(7): 119-121.

Google Scholar

[6] XIA Xian-qing. An Ant Colony Optimization[J]. Software Guide, 2010, 9(8): 65-66.

Google Scholar

[7] Dorigo M. Optimization, learning and natural algorithms. Ph.D. Thesis Dipartimento di Elettronica, Polilecnico di Milano, Italy, (1992).

Google Scholar

[8] Colorni A. Dorigo M., Maniezzo V. Distributed optimization by ant colonies [A], Proc. First European Conf. Artificial Life[C]. Pans, France: Elsevier, 1991: 134-142.

Google Scholar

[9] Colorni A. Dorigo M., Maniezzo V. An investigation of some properties of an ant algorithm[A]. Proc. of Parallel Problem Solving from Nature ( PPSN ) [C]. France: Elsevier, 1992: 509-520.

Google Scholar

[10] Colorni A. ,Dorigo M. ,Maniezzo V. Trubian M. ,Belgian J . Ant system for job shop scheduling[J ]. Operations. Research. Statistics and Computer. Science, 1994, 34(1): 39-53.

Google Scholar

[11] Zhang Xiao. Research on Dynamic Trust Model Based on Ant Colony Algorithm[J]. Computer & Digital Engineering, 2010, 38(8): 93-96.

Google Scholar

[12] Chen Xiaoliang, Ma Hengbing. The application of Adaptive Ant Colony Algorithm in TSP[J]. FUJIAN COMPUTER, 2010, 8: 109-111.

Google Scholar