Energy-efficient clustering protocol for WSN based on improved black widow optimization and fuzzy logic
S. T. Sheriba () and
D. Hevin Rajesh
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S. T. Sheriba: Anna University
D. Hevin Rajesh: St. Xavier’s Catholic College of Engineering
Telecommunication Systems: Modelling, Analysis, Design and Management, 2021, vol. 77, issue 1, No 12, 213-230
Abstract:
Abstract Wireless sensor networks is one of the important parts in modern-day communication that employing low-cost sensor devices with different environmental and physical parameters. The communication path between the base station and sensor nodes are built with the help of an efficient routing protocol. In the past years, the existing protocols met few difficulties in terms of higher computational complexity, poor cluster head selection performance, higher energy consumption, expensive in cluster head selection, scalability management, and uneven load distribution, and so on. In this paper, we proposed BM-BWO with fuzzy logic based HEED protocol (BMBWFL-HEED). In BMBWFL-HEED, we use the combination of the boosted mutation based black widow optimization (BM-BWO) algorithm with HEED protocol to select the higher residual energy. Particularly, the mutation phase of the Black Widow Optimization (BWO) algorithm is improved with the help of direction average strategy (BM-BWO). The fuzzy logic system selects the most relevant and optima cluster heads. Different kinds of experimental analysis, benchmark functions are applied to evaluate the performance of proposed BMBWFL-HEED protocol and it is compared with some existing algorithms like ICFL -HEED, HEED, and ICHB-HEED. In the case of residual energy, a variation of energy consumption and the number of cluster head formation for both homogeneous and heterogeneous environments. The proposed BMBWFL-HEED method demonstrates optimal performance output among all other methods.
Keywords: Wireless sensor network; Cluster head; Fuzzy logic; Boosted mutation based black widow optimization and residual energy (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)
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DOI: 10.1007/s11235-021-00751-8
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