An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency

Deepak Dasaratha Rao^1,*, Bala Dhandayuthapani V.², C. Subbalakshmi³, Murlidhar Prasad Singh⁴, Prashant Kumar Shukla⁵, Shraddha V. Pandit⁶

¹Indian Institute of Technology, Patna, India

²Department of IT, College of Computing and Information Sciences, University of Technology and Applied Sciences, Shinas campus, Oman

³Professor, Department of Computer Science & Engineering, Guru Nanak Instructions Technical Campus (Autonomous), Ibrahimpatnam, Ranga Reddy district, Hyderabad - 501506, Telagana State, India

⁴Assistant Professor, Department of, C.S.& E., B. P. Mandal College of Engineering, Madhepura, Bihar, India,

⁵Associate Professor (Research), Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522302, Andhra Pradesh, India

⁶ Professor, Dept.of Artificial Intelligence and Data Science, PES Modern College of Engineering,   Shivajinagar, Pune-411005, India

Emails: deepakrao@ieee.org; bala.veerasamy@utas.edu.om; subbalakshmichatti@gniindia.org;  singhmurlidhar@gmail.com; prashantshukla2005@kluniversity.in; shraddha.pandit@moderncoe.edu.in

Abstract

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.

Keywords: Fusion Analysis, WSN; SCEECA; Throughput; LEACH.