MCL and ACO Based QoS Routing Algorithm for WSN Used in Power Line Monitoring

Abstract:

This paper presents a new cross-layer QoS routing algorithm for wireless sensor networks. Basing on the principle of cross-layer design, the algorithm adopts delay, nodes’ load and link quality as QoS metrics. The QoS routing metrics are regarded as heuristics correction factors in ant colony algorithm (ACA). The ants are divided into a number of different populations. Through the interaction of pheromone between multi populations, the routing algorithm searches for the feasible paths in parallel and updates the pheromone in time. To overcome the slow convergence of ant colony algorithm, membership cloud model (MCL) is used to control the randomness of the ants. The simulation results demonstrate that the routing algorithm can guarantee the real time, reliability and robustness of wireless sensor networks. It can also achieve the network load balancing.

• Periodical: Applied Mechanics and Materials (Volumes 672-674)
• Pages: 2033-2036
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.672-674.2033
• Online since: October 2014
• Authors: Li Fen Li*
• Keywords: Multiple Ant Colonies Algorithm, QoS Routing, Wireless Sensor Network (WSN)

References

[1] K. Romer, F. Mattern. The Design Space of Wireless Sensor Networks. IEEE Wireless Communications 11 (6) (2004) 54-61.

DOI: 10.1109/mwc.2004.1368897

[2] Ian F A kyildiz, Tommaso M elodia, Kaushik R. Chow dhury. survey on wireless multi-media sensor networks. Computer Net works, 2007, 9212960.

[3] Sohrabi K, Gao J, A ilaw adhi V, Pottie G. Protocols for self-oganization of a wireless sensor network . IEEE Personal Comm Magazine . Oct . 2000, 7 (5) : 16-17.

DOI: 10.1109/98.878532

[4] M. Dorigo, L. Gambardella, M. Birattari, A. Martinoli, R. Poli, T. Stutzle. Ant Colony Optimization and Swarm Intelligence. 5th International Workshop, ANTS 2006, Vol. 4150 of LNCS, Springer, Brus-sels, Belgium, (2006).

DOI: 10.1007/11839088

[5] L. M. Gambardella, M. Dorigo. Solving symmetric and asymmetric TSPs by ant colonies. International Conference on Evolutionary Computation, 1996, pp.622-627.

DOI: 10.1109/icec.1996.542672

[6] L. Gambardella, E. Taillard, M. Dorigo. Ant Colonies for the QAP, Journal of the Operational Research Society 50 (1999) 167-176.

DOI: 10.1057/palgrave.jors.2600676

[7] He Peng. Study on routing and topology control techniques in mobile Ad Hoc networks. Xidian University, (2007).

[8] Xia Hongbin. Research on intelligent computation method with application to network optimization and prediction. Jiangnan Univercity, (2009).