PSO based Multiobjective Optimization for parameter adaptation in CR based IoTs.

Cognitive Radio (CR) has emerged as a reliable technology to handle large number of connected devices in the upcoming Internet of Things (IoTs). Recent trends in communication technology are moving towards adapting the cognitive radio networks into IoT. To achieve this, spectrum sensing task should be followed by real time tuning of transmission parameters so that the objectives of minimum transmit power, minimum bit error rate (BER) and maximum throughput could be achieved for different service types.The decision making module for cognitive radio isresponsible to reach at some autonomous decision for a set of transmission parameters according to the transmission scenario. In this paper, Particle swarm optimization (PSO) based decision making module has been designed to support three modes of operation.The simulation results have been compared with Real coded Genetic Algorithm (GA) that has different encodingmechanism as compared to widely prevalent Binary coded Genetic Algorithm (BCGA) scheme used in the past. The results demonstrate that the parameter adaptation for PSO based engine outperforms the GA based implementation for all the transmission modes in CR based IoTs.