Cost Sensitive based Support Vector Machine for Energy Efficient Clustering and Routing
subject: cluster head selection; cost sensitive; routing; support vector machine; wireless sensor networks
description: Wireless Sensor Networks (WSNs) are ideal for applications requiring rapid deployment, as they operate without the need for a pre-existing network infrastructure. WSNs have constrained capacity to manage large volumes of data, and processing, transmitting as well as receiving this data consumes substantial energy. On account of their constrained power usage and bandwidth, sensors are unable to transmit each gathered information to a Base Station (BS) or sink for analysis. Thus, this research proposes the Cost Sensitive based Support Vector Machine (CS-SVM) approach for the energy efficient clustering and routing. The proposed approach minimizes an energy consumption at data transmission and clustering, extending network lifetime through effectively selects CHs according to energy levels as well as node distribution. The CH selection considers the three important fitness functions such as distance from sink to CH, distance from CH to sensor nodes and residual energy. Then, the routing considers the two important fitness functions. The various performance indices are considered to validate the effectiveness of proposed method. The simulation outcomes proven that the proposed CS-SVM approach attains the better energy consumption of 8.32J as compared to the previous approach named Distributed Energy-efficient two-hop-based Clustering and Routing (DECR). 2025 IEEE.
creator: Muvva, Vijaya Bhaskar Reddy; Habelalmateen, Mohammed I.; Shridhar, V.; Pradeep, V.; Manohar, M.
source: 4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833153366-3;
language: English
type: Conference paper
coverage: Muvva V.B.R., University of Technology and Applied Sciences, College of Computing and Information Sciences, Department of Information Technology, Shinas, Oman; Habelalmateen M.I., The Islamic University, College of Technical Engineering, Department of Computer Technical Engineering, Najaf, Iraq; Shridhar V., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Pradeep V., Alva's Institute of Engineering and Technology, Department of Information Science and Engineering, Mangalore, India; Manohar M., Christ (Deemed to be University), Department of Computer Science and Engineering, Bangalore, India