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Article type: Research Article
Authors: Thangaraj, K.a | Sakthivel, M.b | Balasamy, K.c; * | Suganyadevi, S.d
Affiliations: [a] Department of Information Technology, Sona College of Technology, Salem, Tamilnadu, India | [b] Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India | [c] Department of Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamilnadu, India | [d] Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
Correspondence: [*] Corresponding author. K. Balasamy, Department of Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Sathyamangalam, Erode. E-mail: [email protected].
Abstract: There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network’s or an application’s parameters can’t be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning’s capacity to generalize helps us comprehend the metaheuristic algorithm’s behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption.
Keywords: Cluster head, routing, wireless sensor networks, machine learning, reliability, security
DOI: 10.3233/JIFS-223242
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7415-7428, 2023
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