Abstract
Wireless sensor network should decrease the power costs of redundancy information and delay time. The technology of data aggregation can be adopted. A routing algorithm for data aggregation based on ant colony algorithm (ACAR) is presented. The main idea of this algorithm is optimization of data aggregation route by some cooperation agents called ants using the three heuristic factors about energy, distant and aggregation gain. For realizing data aggregation by positive feedback of the ants, the nodes of wireless sensor networks should not maintain the global information. The algorithm is a distributed routing algorithm and realizes data aggregation trade-off in energy and delay. The analysis and the experimental results show that the algorithm is efficient.
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References
Sankarasubramaniam, Y., Akyildiz, I.F., Su, W., Cayirci, E.: Wireless Sensor Networks: A Survey. Computer Networks, 393–422 (2002)
Krishnamachari, B., et al.: The Impact of Data Aggregation In Wireless Sensor Networks. In: The 22nd International Conference on Distributed Computing Systems Workshops (ICDCSW 2002), Los Alamitos, pp. 1–11 (2002)
Hill, J., Szewczyk, R., Woo, A., et al.: System Architecture Directions For Networked Sensors. In: 9th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-IX), New York, NY, USA, pp. 93–104 (2000)
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In: Proceedings of IEEE HICSS (2000), pp. 3005–3015. IEEE Computer Society Press, Los Alamitos (2000)
Manjeshwar, A., Agrawal, D.P.: TEEN: A Routing Protocol For Enhanced Efficiency in Wireless Sensor Networks. In: Proc. 15th Int’l Parallel and Distributed Processing Symp. (IPDPS 2001), SanFrancisco, CA, pp. 2009–2015 (2001)
Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed Diffusion: A Scalable and Robust Communication Paradigm For Sensor Networks. In: ACM / IEEE International Conference on Mobile Computing and Net2 works (MobiCom 2000), Boston, Massachusetts, pp. 56–67 (2000)
Handy, M.J., Haase, M., Timmermann, D.: Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection. In: Proc. of the 4th IEEE Conf. on Mobile and Wireless Communications Networks, pp. 368–372. IEEE Communications Society, Stockholm (2002)
Lindsey, S., Raghavendra, C.S.: Pegasis: Power-Efficient Gathering in Sensor Information Systems. In: Proc. IEEE Aerospace Conference, pp. 1125–1130. IEEE Computer Society Press, Los Alamitos (2002)
Qi, H., Iyengar, S.S., Chakrabarty, K.: Multi-Resolution Data Integration Using Mobile Agents in Distributed Sensor Networks. IEEE Trans. Systems, Man, and Cybernetics Part C: Applications and Rev., 383–391 (2001)
Lange, D.B., Oshima, M.: Seven Good Reasons for Mobile Agents. Communications of the ACM, 88–89 (1999)
Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed Optimization By Ant Colonies. In: Proceeding of the 1st European Conference on Artificial Life, pp. 134–142 (1991)
Dorigo, M.: Optimization, Learning and Natural Algorithm. Ph.D. Thesis, Department of Electronics, Politecnico diMilano, Italy (1992)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: An Auto catalytic Optimizing Process. Technical Report No. 91-016 Revised, Politecnico di Milano, Italy (1991)
Stützle, T., Dorigo, M.: ACO Algorithms for the Traveling Salesman Problem. In: Miettinen, K., Makela, M., Neittaanmaki, P., Periaux, J. (eds.) Evolutionary Algorithms in Engineering and Computer Science, pp. 163–183. Wiley, Chichester (1999)
Stützle, T., Grün, A., Linke, S., Rüttger, M.: A Comparison of Nature Inspired Heuristics on The Traveling Salesman Problem. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN VI. LNCS, vol. 1917, pp. 661–670. Springer, Heidelberg (2000)
NRL’s Sensor Network Extension to Ns-2, http://nrlsensorsim.pf.itd.nrl.navy.mil
The Network Simulator - ns-2, http://www.isi.edu/nsnam/ns/
Ant-like Mobile Agents NS2 Patch, http://www.item.ntnu.no/~wittner/ns/index.html
Ye, Z.W., Zheng, Z.B.: Research on The Configuration of Parameter α, β, ρ in Ant Algorithm Exemplified by TSP. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 2106–2111 (2003)
Gambardella, L.M., Dorigo, M.: Ant-Q: A Reinforcement Learning Approach To The Traveling Salesman Problem. In: Proceedings of the 12th International Conference on Machine Learning, pp. 252–260 (1995)
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Ye, N., Shao, J., Wang, R., Wang, Z. (2007). Colony Algorithm for Wireless Sensor Networks Adaptive Data Aggregation Routing Schema. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_28
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DOI: https://doi.org/10.1007/978-3-540-74769-7_28
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