aspg-3-2696-1791419823 20261008003703 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 2 Deepak Dasaratha Rao Indian Institute of Technology, Patna, India Bala Dhandayuthapani V. Department of IT, College of Computing and Information Sciences, University of Technology and Applied Sciences, Shinas campus, Oman Ch. Subbalakshmi Professor, Department of Computer Science & Engineering, Guru Nanak Instructions Technical Campus (Autonomous), Ibrahimpatnam, Ranga Reddy district, Hyderabad - 501506, Telagana State, India Murlidhar Prasad Singh Assistant Professor, Department of, C.S.& E., B. P. Mandal College of Engineering, Madhepura, Bihar, India Prashant Kumar Shukla Associate Professor (Research), Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522302, Andhra Pradesh, India Shraddha V. Pandit Professor, Dept.of Artificial Intelligence and Data Science, PES Modern College of Engineering, Shivajinagar, Pune-411005, India Recently, wireless sensor networks on several challenging topics have piqued researchers’ attention. Maximising a network's lifetime requires just the right combination of cluster size and number of nodes. Data transmission from nodes to cluster leaders is energy intensive, even for a modest number of clusters. If there are several clusters, many leaders will be chosen, and many nodes will rely on long-distance transmission to communicate with the home base. Therefore, in order to maximise efficiency, it is necessary to strike a balance between these two factors. WSN's major challenge is improving its energy efficiency. This is because their energy consumption defines their lifespan, and it is difficult, if not impossible, to recharge their batteries. Therefore, it is crucial to develop algorithms that consume as little energy as possible in order to maximise the network's potential. The perfect clusters are essential for the longevity of the network. Therefore, an algorithm called statistical centre energy efficient clustering approach (SEECA) is presented to increase the network's lifetime while decreasing its energy consumption. The experimental findings show that the proposed methodology SCEECA outperforms the LEACH method by a wide margin, with gains of 32% in Residual energy, 16% in Network Lifetime, and 12% in Throughput. 2024 187 195 2696 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150217 https://www.americaspg.com/journal/3/article/2696 https://www.americaspg.com/storage/21713895703.pdf