Hybrid Bio-Inspired Clustering Algorithm for Energy Efficient Wireless Sensor Networks

Document Type : Research Paper


1 PhD Candidate, Industrial Engineering, Azadi Pardis of Yazd University, Yazd University, Yazd, Iran.

2 Professor, Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Daneshgah Blvd., Safayieh, PO Box: 89195-741, Yazd, Iran.

3 Assistant Professor of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Daneshgah Blvd., Safayieh, PO Box: 89195-741, Yazd, Iran


In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.


Abba Ari, A., Yenke, B.O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence-based approach.  Journal of Network and Computer Applications, 69, 77-97.
Abbasi, A.A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks”, Computer Communications, Vol. 30, 2826–2841.
Al-Ghazzali, T (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8, 541–564.
Al-Ghazzali, T. (2009). Metaheuristics: from design to implementation. Chichester: John Wiley and Sons Inc.
Amgoth, T., & Jana, P. K. (2015). Energy-aware routing algorithms for wireless sensor networks. Computers & Electrical Engineering, 41, 357-367.
Anandamurugan, S., & Abirami T. (2017). Antipredator adaptation shuffled frog leap algorithm to improve network lifetime in wireless sensor network. Wireless Personal Communications, 94, 2031–2042.
Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks, Applied Soft Computing, 13(4), 1741-1749.
Barzin, A., Sadeghieh, A., Khademi Zareh, H., & Honarvar, M. (2019). Hybrid swarm intelligence-based clustering algorithm for energy management in wireless sensor networks. Journal of Industrial and Systems Engineering, 12(3), 78-106.
 Butenko, V., Nazarenko, A., Sarian, V., Sushchenko, N., & Lutokhin, A. (2014). Applications of wireless sensor networks in next generation networks, international telecommunication union, Telecommunication Standardization Sector of ITU-T, Series Y. 2000: Ngn-Awsn (2014-02).
Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5, 1-38.
Eusuff, H., Lansey, M., & Pasha F. (2006). Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2), 129-154.
Fanian, F., & Kuchaki Rafsanjani, M. (2019). Cluster-based routing protocols in wireless sensor networks: A survey based on methodology. Journal of Network and Computer Applications, 142, 111-142
Fanian, F., & Rafsanjani, M.K. (2018). Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm. Applied Soft Computing, 71, 568-590.
Fister, I., Fister, I., Yang, X., & Brest, J. (2013). A Comprehensive Review of firefly Algorithms, Swarm and Evolutionary Computation, 13, 34-46.
Gupta, G., & Jha, S. (2018). Integrated Clustering and routing protocol for wireless sensor networks using cuckoo and harmony search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101-109.
Hamzeloei, F., & KhalilyDermany, M. (2016). A topsis based cluster head selection for wireless sensor network. Procedia Computer Science, 98, 8-15.
Hanifi, A., Taghva, M., Haghi, R.H., & Feizi, K. (2018). Clustering for reduction of energy consumption in wireless sensor networks by AHP method. Journal of Information Systems and Telecommunication, 6(1), 9-17.
Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless micro sensor networks. In Proceeding of the Hawaii International Conference on Systems Science, Vol. 8.
Heinzelman, W., Chandrakasan, A. & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communication, 1(4).
Jabeur, N. (2016). A firefly-inspired micro and macro clustering approach for wireless sensor networks. The Seventh International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN).
Kassan, S., Gaber, J., & Lorenz P. (2018). Game theory based distributed clustering approach to maximize wireless sensors network lifetime. Journal of Network and Computer Applications, 123, 80-88.
Khorsandi, A., Alimardani A., Vahidi B., &. Hosseinian S.H. (2011). Hybridshuffled frog leaping algorithm and nelder–mead simplex search for optimal reactive power dispatch. IET Generation Transmission & Distribution, 5(2).
Ko, A., Lau, Y.K., & Sham P.S. (2008). Application of distributed wireless sensor network on humanitarian search and rescue systems. In Proceeding of the Second International Conference on Future Generation Communication and Networking, 02, 328-333.
Minaie, A., & Sanati-Mehrizy, A. (2013). Application of wireless sensor networks in health care system. In proceeding of the 120th ASEE annual conference & exposition.
Mo, Y., Ma, Y., & Zheng, Q. (2013). Optimal choice of parameters for firefly algorithm. IEEE, Fourth International Conference on Digital Manufacturing & Automation, Qingdao, 887-89.
Mukhdeep, S. M., & Singh, S.B. (2016). Firefly algorithm based clustering technique for wireless sensor networks. Wispnet Conference, IEEE Press.
Oladimeji, M.O., Turkey, M., & Dudley, S. (2017). HACH: Heuristic algorithm for clustering hierarchy protocol in wireless sensor networks. Applied Soft Computing, 55, 452-461.
Pantoni, R.P., & Brandão, D. (2013). A gradient based routing scheme for street lighting wireless sensor networks.  Journal of Network and Computer Applications, 36(1), 77-90.
Pratyay, K., & Prasanta, K.J. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127-140.
Ran, G., Zhang, H., & Gong, S. H. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information and Computational Science, 7, 767-775.
Shokouhifar, M., & Jalali, A. (2015). A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU - Electronics and Communications, 69, 432-441.
Singh, M.P., & Singh, S. (2017). Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Engineering Applications of Artificial Intelligence, 53, 142-152.
Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks technology, protocols, and applications. John Wiley & Sons Ltd.
Tripathi, M., Gaur, M.S., Laxmi, V., & Battula, R.B. (2013). Energy efficient LEACH-C protocol for wireless sensor networks. Third International Conference on Computational Intelligence and Information Technology (CIIT 2013).
Wang, L., & Gong, Y. (2013). Convergence and parameters analysis of shuffled frog leaping algorithm. International Conference on Artificial Intelligence and Software Engineering (ICAISE).
Xunli, F.A, & Feiefi, D.U. (2015). Shuffled frog leaping algorithm based unequal clustering strategy for wireless sensor networks. International Journal of Applied Mathematics and Information Sciences, 9, 1415–1426.
Yang, X. Sh. (2010). Nature-inspired metaheuristic algorithms. Luniver Press.
Zahedi, Z.M., Akbari, R., Shokouhifar, M., Safaei, F., & Jalali, A. (2016). Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Systems With Applications, 55, 313-328.
Zenga, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing,41, 135-147.
Zhang, L., Liu, L., Yang, X. Sh., & Dai, Y. (2016). A novel hybrid firefly algorithm for global optimization. PLOS ONE, 11(9), e0163230.