Cost Sensitive based Support Vector Machine for Energy Efficient Clustering and Routing

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).