TY - JOUR ID - 73274 TI - Hybrid Bio-Inspired Clustering Algorithm for Energy Efficient Wireless Sensor Networks JO - Journal of Information Technology Management JA - JITM LA - en SN - AU - Barzin, Amirhossein AU - Sadeghieh, Ahmad AU - Khademi Zare, Hassan AU - Honarvar, Mahboobeh AD - PhD Candidate, Industrial Engineering, Azadi Pardis of Yazd University, Yazd University, Yazd, Iran. AD - Professor, Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Daneshgah Blvd., Safayieh, PO Box: 89195-741, Yazd, Iran. AD - Assistant Professor of Industrial Engineering, Faculty of Industrial Engineering, Yazd University, Daneshgah Blvd., Safayieh, PO Box: 89195-741, Yazd, Iran Y1 - 2019 PY - 2019 VL - 11 IS - 1 SP - 76 EP - 101 KW - Wireless Sensor Networks KW - Clustering KW - Bio-inspired Algorithm KW - Firefly Algorithm KW - Shuffled Frog Leaping Algorithm DO - 10.22059/jitm.2019.280639.2354 N2 - 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. UR - https://jitm.ut.ac.ir/article_73274.html L1 - https://jitm.ut.ac.ir/article_73274_14bdd0f20bb4cf57904ddcc56d18fee0.pdf ER -