An efficient routing protocol for coherent energy using mayfly optimization algorithm in heterogeneous wireless sensor networks. Issue 1 (17th February 2023)

• Record Type: Journal Article
• Authors: Jasmine Lizy, Pathrose; Chenthalir Indra, Natarasan
• Is Part Of: Cognitive computation and systems. Volume 5:Issue 1(2023)
• Page Start: 30
• Page End: 41
• Publication Date: 2023-02-17
• Subjects: computer vision -- learning (artificial intelligence) -- machine learning -- machine vision; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12074
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: Heterogeneous wireless sensor network (WSN) is used in infrastructure monitoring, animal monitoring, smart building and environmental monitoring. Several research has been done to detect energy efficiency concerns, but none of them has been successful. In this proposed model, the clusters are formed using K‐means clustering algorithm for the sensor nodes. MOA is used in combination with an energy efficient routing protocol to select the CH. Sub‐clusters are formed because of the nodes that cannot survive with the CH. To transfer the data from CH to the base station, each and every sub‐cluster selects a separate CH. When the energy level of the CH becomes low, the selection process of CH will start again and a new CH will be selected according to the fitness evaluation of the efficient routing protocol. The performance of the mobile sensor nodes is determined using the residual energy, network lifetime, number of data packets sent to the sink and number of dead nodes. The proposed model performed better than the existing techniques such as LEACH, LEACH‐PSO and SEECP. Simulation results illustrate that the proposed model enhances the life time of networks, nodes' energy consumption rate and exhaustion ratio for every node by more than 4.12%, 5.76% and 6.18%.