Abstract
Clustering has been accepted as one of the most efficient techniques for conserving energy of wireless sensor networks (WSNs). However, in a two-tiered cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving data from their member sensor nodes, aggregating them and transmitting that data to the base station (BS). Therefore, proper selection of CHs and optimal formation of clusters play a crucial role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient CH selection and energy balanced cluster formation algorithms, which are based on novel chemical reaction optimization technique (nCRO), we jointly called these algorithms as novel CRO based energy efficient clustering algorithms (nCRO-ECA). These algorithms are developed with efficient schemes of molecular structure encoding and potential energy functions. For the energy efficiency, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes in the CH selection phase. In the cluster formation phase, we consider various distance and energy parameters. The algorithm is tested extensively on various scenarios of WSNs by varying number of sensor nodes and CHs. The results are compared with original CRO based algorithm, namely CRO-ECA and some existing algorithms to demonstrate the superiority of the proposed algorithm in terms of energy consumption, network lifetime, packets received by the BS and convergence rate.
Similar content being viewed by others
References
Akyildiz, I. F., Weilian, S., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communication Magazine, 40(8), 102–114.
Abbasi, A. H., & Mohamad, Y. A. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.
Guru, S. M., Halgamuge, S. K., & Fernando, S. (2005). Particle swarm optimisers for cluster formation in wireless sensor networks. In Proceedings of international conference on intelligent sensors sensor networks and information processing (pp. 319–324).
Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.
Lam, A. Y. S., & Li, V. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14(3), 381–399.
Xu, J., Lam, A., & Li, V. O. (2011). Chemical reaction optimization for task scheduling in grid computing. IEEE Transactions on Parallel and Distributed Systems, 22(10), 1624–1631.
Lam, A., Li, V. O., & Yu, J. J. (2013). Power-controlled cognitive radio spectrum allocation with chemical reaction optimization. IEEE Transactions on Wireless Communications, 12(7), 3180–3190.
Lam, A. Y., & Li, V. O. (2012). Chemical reaction optimization: A tutorial. Memetic Computing, 4(1), 3–17.
Atkins, P., & de Paula, J. (2010). Physical chemistry (9th ed.). Oxford, UK: Oxford University Press (Part1 and Part 3).
Oxtoby, D. W., Gill, H. P., & Campion, A. (2012). Principles of modern chemistry (7th ed.). United States of America: Cengage Learning (Unit 3 and Unit 5).
Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.
Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.
Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In Sensor, mesh and ad hoc communications and networks (SECON), 2011 8th annual IEEE communications society conference on (pp. 46–54).
Liu, X. A. (2012). Survey on clustering routing protocols in wireless sensor networks. Sensors, 12(8), 11113–11153. doi:10.3390/s120811113.
Heinzleman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences.
Liu, X. Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., & Wu, M. Y. (2014). CDC: Compressive data collection for wireless sensor networks.
Hu, S., Han, J., Wei, X., & Chen, Z. (2015). A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks. Wireless Networks, 21(1), 57–65.
Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power efficient gathering in sensor information systems. In Proceedings of IEEE aerospace conference (Vol. 3, pp. 1125–1130).
Younis, O., & Fahmy, S. (2004). HEED: Hybrid energy efficient distributed clustering approach for Ad Hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.
Yanjun, Y., Qing, C., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking’s, 23(3), 810–823.
Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. Communications Magazine, IEEE, 51(7), 107–113.
Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.
Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In IEEE INFOCOMM (Vol. 3, pp. 1713–1723).
Banerjee, S., & Khuller, S. (2001). A clustering scheme for hierarchical control in wireless networks. In Proceedings of IEEE INFOCOMM (Vol. 2, pp. 1028–1037).
Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3), 45.
Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In Mobile ad-hoc and sensor systems (MASS), 2013 IEEE 10th international conference on (pp. 182–190).
Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 31(14), 3451–3459.
Rao, P. C. S., Banka, H., & Jana, P. K. (2015). PSO-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks. In Proceedings of the second international conference on computer and communication technologies (pp. 605–616). Springer India.
Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.
Dvir, A., & Vasilakos, A. V. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.
Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.
Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.
Phan, D. H., Suzuki, J., Omura, S., Oba, K., & Vasilakos, A. (2014). Multiobjective communication optimization for cloud-integrated body sensor networks. In Cluster, cloud and grid computing (CCGrid), 2014 14th IEEE/ACM international symposium on (pp. 685–693).
Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D. Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.
Rahimi, M. R., Ren, J., Liu, C. H., Vasilakos, A. V., & Venkatasubramanian, N. (2014). Mobile cloud computing: A survey, state of art and future directions. Mobile Networks and Applications, 19(2), 133–143.
Lin, J., Xiong, N., Vasilakos, A. V., Chen, G., & Guo, W. (2011). Evolutionary game-based data aggregation model for wireless sensor networks. IET Communications, 5(12), 1691–1697.
Logambigai, R., & Kannan, A. Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 1–13.
Guo, W., Xiong, N., Vasilakos, A. V., Chen, G., & Yu, C. (2012). Distributed k–connected fault–tolerant topology control algorithms with PSO in future autonomic sensor systems. International Journal of Sensor Networks, 12(1), 53–62.
Guo, W., Park, J. H., Yang, L. T., Vasilakos, A. V., Xiong, N., & Chen, G. (2011). Design and analysis of a MST-based topology control scheme with PSO for wireless sensor networks. In Services computing conference (APSCC), 2011 IEEE Asia-Pacific (pp. 360–367).
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Tillet, J., Rao, R., & Sachin, F. (2002). Cluster head identification in adhoc sensor networks using particle swarm optimization. In IEEE international conference on personal wireless communications (pp. 201–205).
Abbas, K., Abedini, S. M., Faraneh, Z., & Al-Haddad, S. A. R. (2013). Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network. Journal of Basic and Applied Scientific Research, 3(3), 694–703.
Enan, A., Bara, A., & Attea, A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, 1(4), 195–203.
Latiff, N. M. A., Tsemenidis, C. C., & Sheriff, B. S. (2007). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In Proceedings of 18th annual IEEE international symposium on personal, indoor and mobile radio communications (pp. 1–5).
Buddha, S., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences, 2(1), 2–13.
Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.
Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.
Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(6), 1093–1102.
Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. Network and Service Management, IEEE Transactions on, 11(3), 417–430.
Acampora, G., Gaeta, M., Loia, V., & Vasilakos, A. V. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5(2), 8.
Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–38.
Xu, J., Liu, W., Lang, F., Zhang, Y., & Wang, C. (2010). Distance measurement model based on RSSI in WSN. Wireless Sensor Networks, 2(8), 606–611.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Srinivasa Rao, P.C., Banka, H. Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wireless Netw 23, 433–452 (2017). https://doi.org/10.1007/s11276-015-1156-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-015-1156-0