Abstract
The routing for Wireless Sensor Networks (WSNs) is a key and hard problem, and it is a research topic in the field of WSN applications. Based on Ant Colony Optimization (ACO), this paper proposes a novel adaptive intelligent routing scheme for WSNs. Following the proposed scheme, a high performance routing algorithm for WSNs is designed. The proposed routing scheme is very different from the existing ACO based routing schema for WSNs. On one hand, in the proposed scheme, the search range for an ant to select its next-hop node is limited to a subset of the set of the neighbors of the current node. On the other hand, by fusing the residual energy and the global and local location information of nodes, the new probability transition rules for an ant to select its next-hop node are defined. Compared with other ACO based routing algorithms for WSNs, the proposed routing algorithm has a better network performance on aspects of energy consumption, energy efficiency, and packet delivery latency.
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References
Akyildiz, I.F., Su, W., Sankkarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Journal of Computer Networks 38(4), 393–424 (2002)
Al-karaki, J.N., Kamal, A.E.: Routing techniques in wireless sensor networks: a survey. IEEE Wireless Communications 11(6), 6–28 (2004)
Iyengar, S.S., Wu, H.-C., Balakrishnan, N., Changand, S.Y.: Biologically inspired cooperative routing for wireless mobile sensor networks. IEEE Systems Journal 1(1), 29–37 (2007)
Aghaei, R.G., Rahman, M.A., Gueaieb, W., Saddik, A.E.: Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks. In: 2007 IEEE Instrumentation and Measurement Technology, pp. 1–6. IEEE Press, New York (2007)
Dorigo, M., et al.: Ant system optimation: a colony of cooperating agents. IEEE Transactions on System, Man, Cybernetics Part B. 26(1), 29–41 (1996)
Dorigo, M., Gambadella, L.M.: Ant colony system: a cooperative learning approach to the tranveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Zhang, Y., Kuhn, L.D., Fromherz, M.P.J.: Improvements on ant routing for sensor networks. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 154–165. Springer, Heidelberg (2004)
Shen, C., Jaikaeo, C.: Ad hoc multicast routing algorithm with swarm intelligence. Journal of Mobile Netwotks and Applications 10(1,2), 47–59 (2005)
Akkaya, K., Younis, M.: A survey on routing protocols for wireless sensor networks. Journal of Ad Hoc Networks 3(3), 325–349 (2005)
Caro, G.D., Dorigo, M.: AntNet: distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)
Dorigo, M., et al.: Special section on ant colony optimization. IEEE Transactions on Evolutionary Computation 6(4), 317–319 (2002)
Chakrabarty, K., Iyengar, S.S.: Scalable infrastructure for distributed sensor networks. Springer, Heidelberg (2005)
Stuetzle, T., Dorigo, M.: A short convergence proof for a class of ACO algorithms. IEEE Transactions on Evolutionary Computation 6(4), 358–365 (2002)
Schoonderwoerd, R., Holland, O., Bruten, J., Rothkrantz, L.: Ant-based load balancing in telecommunications networks. Adaptive Behavior 5(2), 169–207 (1996)
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Wang, X., Li, Q., Xiong, N., Pan, Y. (2008). Ant Colony Optimization-Based Location-Aware Routing for Wireless Sensor Networks. In: Li, Y., Huynh, D.T., Das, S.K., Du, DZ. (eds) Wireless Algorithms, Systems, and Applications. WASA 2008. Lecture Notes in Computer Science, vol 5258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88582-5_13
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DOI: https://doi.org/10.1007/978-3-540-88582-5_13
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