Squirrel Search Algorithm Based Support Vector Machine for Congestion Control in WSN-IoT

As the Wireless sensor network (WSN) has huge part in Internet of Things (IoT), it is used in different applications, for example, detecting climate and sending information by means of the internet. In any case, because of the issue of weighty congestion, the performance of WSN-IoT may affect. Despite the fact that machine learning calculations have been introduced by analysts for distinguishing the congested data, accuracy of detection is further to be enhanced. To control the congestion, squirrel search algorithm (SSA) based support vector machine (SVM) is proposed in this paper. To enhance on the execution of SVM, tuning parameters of SVM is improved utilizing SSA algorithm. Simulation results depict that the SVM-SSA outflanks the model, for example, SVM with Genetic Algorithm (SVM-GA), SVM and TCP dependent on throughput, energy utilization, delivery ratio, and overhead. Also, detection accuracy of SVM-SSA increase to 92%.