An Energy Aware Routing Based on Swarm Intelligence for Wireless Sensor Networks

Article Preview

Abstract:

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the ant colony based meta-heuristic, with a novel variation of reinforcement learning for sensor networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that compared with the traditional EEABR algorithm can obviously improve adaptability and reduce the average energy consumption effectively.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 706-708)

Pages:

635-638

Citation:

Online since:

June 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] I. Mohammad, M. Imad, Handbook of Sensor Networks, CRC Press: London, 2005; pp.117-140.

Google Scholar

[2] J. Raka, T. Milan, and S. Dana, Ant colony optimization applied to minimum weight dominating set problem, in Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation Catania, Italy: World Scientific and Engineering Academy and Society (WSEAS), 2010.

Google Scholar

[3] K. Matrouk and B. Landfeldt, Rett-gen: A globally efficient routing protocol for wireless sensor networks by equalising sensor energy and avoiding energy holes, Ad Hoc Networks, 2009, no. 3, p.514 – 536.

DOI: 10.1016/j.adhoc.2008.07.002

Google Scholar

[4] G. Singh, S. Das, S. Gosavi, S. Pujar, Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks, In Recent Developments in Biologically Inspired Computing; de Castro, L.N., Von Zuben, F.J., Eds.; Idea Group Publishing: Hershey, PA, USA, 2004; pp.181-206.

DOI: 10.4018/978-1-59140-312-8.ch008

Google Scholar

[5] Y. Zhang, L. Kuhn, M. Fromherz, Improvements on Ant Routing for Sensor Networks, In Ants 2004, Workshop on Ant Colony Optimization and Swarm Intelligence, 2004; pp.154-165.

DOI: 10.1007/978-3-540-28646-2_14

Google Scholar

[6] T. Camilo; C. Carreto, J. Silva; F. Boavida, An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. In Proceedings of the Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium, 4–7 September 2006; p.49–59.

DOI: 10.1007/11839088_5

Google Scholar