Abstract
Nodes in the Wireless sensor network (WSN) have the limited power, memory and battery capacity. In this work, we propose an efficient routing algorithm called Cluster Based Wireless Sensor Network using Ant Colony Optimization (CBACO). The Cluster Heads (CH) are selected based on the cost derived from node parameters remaining energy, number of neighbours and distance to base station. The weighted average method is used to compute cost. The routing processes are established in two levels. The cluster member to cluster head data transmission is handled at level one and in the second level path finding process between cluster head to base station handled by Ant Colony Optimization (ACO) method which is the biologically inspired optimization technique. All the cluster head nodes are participate in the second level inter-cluster routing operation. The performance of the CBACO algorithm in terms of delay is minimized by using the clustering method and ACO method. The efficiency of the proposed algorithm is analysed by compare with existing routing algorithm which uses LEACH and ACO method. The results indicate that our proposed work achieves low energy consumption and high throughput.
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References
Abbasi, A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Comput. Commun. 30(14–15), 2826–2841 (2007)
Salehpour, A.-A., Mirmobin, B., Afzali-Kusha, A., Mohammadi, S.: An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization. IEEE-2008 (2008)
Nayyar, A., Singh, R.: Ant Colony Optimization (ACO) based routing protocols for wireless sensor networks (WSN): a survey. (IJACSA). Int. J. Adv. Comput. Sci. Appl. 8(2), 148–155 (2017)
Amiri, E., Keshavarz, H., Alizadeh, M., Zamani, M., Khodadadi, T.: Energy efficient routing in wireless sensor networks based on fuzzy ant colony optimization. Int. J. Distrib. Sens. Netw. 2014, 17. http://dx.doi.org/10.1155/2014/768936,Article ID 768936, Hindawi Publishing Corporation
Akyildiz, F., Su, W., Sankarasubramaniam, Y., Cyirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)
Chen, G., Guo, T. D., Yang, W.G., Zhao, T.: An improved ant based routing protocol in Wireless Sensor Networks. In: Proceedings of International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 1–7, November 2006
DiCaro, G., Dorigo, M.: AntNet: distributed stigmergetic control for communications networks. J. Artif. Intell. Res. (JAIR) 9, 317–365 (1998)
Kim, J.-Y., Sharma, T., Brijesh Kumar, G.S.T., Berry, K., Lee, W.-H.: Intercluster ant colony optimization algorithm for wireless sensor network in dense environment. Int. J. Distrib. Sens. Netw. 2014, 10. http://dx.doi.org/10.1155/2014/457402, Article ID 457402, Hindawi Publishing Corporation
Sohraby, K., Minoli, D., Znati, T.: Wireless Sensor Networks: Technology, Protocols, and Applications. Wiley & Sons Inc., New York (2007)
Juan, L., Chen, S., Chao, Z.: Ant system based anycast routing in wireless sensor networks. In: Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2007), pp. 2420–2423, September 2007
Dorigo, M., Caro, G.D.: The Ant Colony Optimization Metaheuristic, 1st edn. McGraw-Hill, London (1999)
Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)
Okdem, S., Karaboga, D.: Routing in wireless sensor net-works using an ant colony optimization (ACO) router chip. Sensors 9(2), 909–921 (2009)
Okdem, S., Karaboga, D.: Routing in wireless sensor networks using an Ant Colony Optimization (ACO) Router Chip. Sensors 9, 909–921 (2009). https://doi.org/10.3390/s90200909
Gupta, V., Sharma, S.K.: Cluster head selection using modified ACO. In: Das, K.N., Deep, K., Pant, M., Bansal, J.C., Nagar, A. (eds.) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. AISC, vol. 335, pp. 11–20. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2217-0_2
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy- efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (HICSS), pp. 3005–3014, January 2000
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Rajasekaran, A., Nagarajan, V. (2019). Cluster-Based Wireless Sensor Networks Using Ant Colony Optimization. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_5
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