Congestion Control in Wireless Sensor Networks based on Support Vector Machine, Grey Wolf Optimization and Differential Evolution

Abstract

Transmission rate is one of the contributing factors in the performance of Wireless Sensor Networks (WSNs). Congested network causes reduced network response time, queuing delay and more packet loss. To address this issue, we have proposed a transmission rate control method. The current node in a WSN adjusts its transmission rate based on the traffic loading information gained from the downstream node. Multi classification is used to control the congestion using Support Vector Machine (SVM). In order to get less miss classification error, Differential Evolution (DE) and Grey Wolf Optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE–SVM and GWO-SVM are more proficient than the other classification techniques in terms of classification error.