Abstract
Wireless Sensor Networks (WSNs) are a special type of networks deployed in different geographical regions for capturing the important information. WSNs consist of low energy devices called Sensor Nodes (SNs) which are capable of sensing and transferring the gathered information to remote controller called as Base Stations (BSs). Because these devices are generally deployed in unattended environment and are limited in communication and computing power, so it is not always possible to recharge or replace the batteries for these devices. The SNs are supposed to have self healing and built in intelligence to operate independently. Keeping view of the above, in this paper, we propose a new Efficient Learning Automata based Cell Clustering Algorithm (ELACCA) for WSNs. Compared to the earlier approaches, we have taken size of the cell of the area under investigation in rhombus shape rather than the square. The selection of cluster head (CH) is performed by different levels using the participation ratio of the nodes in respective CH. Using the defined participation ratio, a cut off on number of nodes in a particular CH is also computed. Moreover, by varying the angle from base of the cell to its sides, the numbers of CHs formed are also calculated. Using these values, the communication among different CHs is maintained. The performance of the proposed scheme is validated using the extensive simulation with respect to various parameters such as connectivity, coverage and packet delivery ratio. The results obtained show that the proposed scheme is better than the existing schemes with respect to these metrics.
Similar content being viewed by others
References
Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd international conference on embedded networked sensor systems, SenSys ’04. ACM, New York, NY, USA, pp. 95–107.
Alberola, R. D. P., & Pesch, D. (2012). Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks. Ad Hoc Networks, 10, 664–679.
Kumar, D., Trilok, C., & Patel, A. R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.
Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14–15), 2842–2852.
Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load balanced clustering algorithm for wireless sensor networks. Computer Communications, 31(4), 750–759.
Fuad, B., & Awan, I. (2011). Adaptive decentralized re-clustering algorithm for wireless sensor networks. Journal of Computer and System Sciences, 77(2), 282–292.
Zhang, Y., Li, K., Gu, H., & Yang, D. (2012). Adaptive split and merge clustering algorithm for wireless sensor networks. Procedia Engineering, 29, 3547–3551.
Khalil, E. A., & Attea, B. A. (2011). Energy aware evolutionary algorithm for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, 1(4), 195–203.
Jin, Y., Jo, J. Y., Wang, L., Kim, Y., & Yang, X. (2008). ECCRA: An energy efficient coverage and connectivity preserving routing algorithm under border effects in wireless sensor networks. Computer Communications, 31, 2398–2407.
Lin, C., Wu, G., Xia, F., Li, M., Yao, L., & Pei, Z. (2012). Energy efficient ant colony algorithms for data aggregation in wireless sensor networks. Journal of Computer and System Sciences, 78(6), 1686–1702.
Esnaashari, M., & Meybodi, M. R. (2011). A cellular learning automata based deployment strategy for mobile wireless sensor networks. Journal of Parallel and Distributed Computing, 71(7), 988–1001.
Esnaashari, M., & Meybodi, M. R. (2010). A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Computer Networks, 54(14), 2410–2438.
Torkestani, A. J., & Meybodi, R. M. (2010). Mobility-based multicast routing algorithm for wireless mobile ad-hoc networks: A learning automata approach. Computer Communication, 33(6), 721–735.
Torkestani, A. J., & Meybodi, R. M. (2011). Learning automata based algorithms for solving stochastic minimum spanning tree problem. Applied Soft Computing, 11(6), 4064–4077.
Torkestani, A. J., & Meybodi, R. M. (2010). An intelligent backbone formation algorithm for wireless adhoc networks based upon distributed learning automata. Computer Networks, 54(5), 826–843.
Chatzichristofis, S. A., Dimitris, M. A., Sirakoulis, G. C., & Boutalis, Y. S. (2010). A novel cellular automata based technique for visual multimedia content encryption. Optics Communications, 283(21), 4250–4260.
Liu, Z., Zheng, Q., Xue, L., & Guan, X. (2012). A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Generation Computer Systems, 28, 780–790.
Aioffi, W. M., Valle, C. A., Mateus, G. R., & Cunha, A. S. D. (2011). Balancing message delivery latency and network lifetime through an integrated model for clustering and routing in wireless sensor networks. Computer Networks, 55, 2803–2820.
Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183, 117–131.
Marcelloni, F., & Vecchio, M. (2010). Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Information Sciences, 180(10), 1924–1941.
Ting, C. K., & Liao, C. C. (2010). A memetic algorithm for extending wireless sensor network lifetime. Information Sciences, 180(24), 4818–4833.
Bandyopadhyay, S., & Coyle, E. J. (2013). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proceedings of IEEE computer and communications societies (INFOCOM), pp. 1713–1723.
Wang, Y., Wu, H., Nelavelli, R., & Tzeng, N. F. (2006). Balance based energy-efficient communication protocols for wireless sensor networks. In Proceedings of IEEE international conference workshops on distributed computing systems.
Alippi, C., Camplani, R., & Roveri, M. (2009). An adaptive LLC-based and hierarchical power-aware routing algorithm. IEEE Transactions on Instrumentation and Measurement, 58(9), 3347–3357.
Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft computing, 12(7), 1950–1957.
Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with non uniform node distribution. International Journal of Electronics and Communications, 66, 54–61.
Sarkar, P., & Saha, A. (2011). Security enhanced communication in wireless sensor networks using Reed–Muller codes and partially balanced incomplete block designs. Journal of Convergence, 2(1), 23–30.
Pan, R., Xu, G., Fu, B., Dolog, P., Wang, Z., & Leginus, M. (2012). Improving recommendations by the clustering of tag neighbours. Journal of Convergence, 3(1), 13–20.
Silas, S., Ezra, K., & Rajsingh, E.B. (2012). A novel fault tolerant service selection framework for pervasive computing. Human-centric Computing and Information Sciences, 2:5. doi:10.1186/2192-1962-2-5.
Dhurandher, S. K., Obaidat, M. S., & Gupta, M. (2012). An acoustic communication based AQUA-GLOMO simulator for underwater networks. Human-centric Computing and Information Sciences, 2:3, 2–14.
Narendra K. S., & Thathachar, M. A. L. (1980). On the behavior of a learning automaton in a changing environment with application to telephone traffic routing. IEEE Transactions on Systems, Man, and Cybernetics, SMC, l0(5), 262–269.
Najim, K., & Poznyak, A. S. (1996). Multimodal searching technique based on learning automata with continuous input and changing number of actions. IEEE Transactions on Systems, Man, and Cybernetics-Part B. Cybernetics, 26(4), 666–673.
Luo, H., Luo, J., Liu, Y., & Das, S. K. (2009). Adaptive data fusion for energy efficient routing in wireless sensor networks. IEEE Transactions on computers, 24(5), 345–359.
The Network Simulator NS-2. http://www.isi.edu/nsnam/ns/.
Chandrakasan, A. P., Smith, A. C., & Heinzelman, W. B. (2004). An application specific protocol architecture for wireless micro sensor networks. IEEE Transaction on Wireless Communications, 1(4), 660–669.
Chamam, A., & Pierre, S. (2010). A distributed energy-efficient clustering protocol for wireless sensor networks. Computers & Electrical Engineering, 36(2), 303–312.
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.
Acknowledgments
This work was supported by the new faculty research program 2013 of Kookmin University in Korea.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kumar, N., Kim, J. ELACCA: Efficient Learning Automata Based Cell Clustering Algorithm for Wireless Sensor Networks. Wireless Pers Commun 73, 1495–1512 (2013). https://doi.org/10.1007/s11277-013-1262-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-013-1262-1