Fractional red panda optimisation-based cluster head selection and routing in IoT

In the present world, the IoT has been developed as a widespread network for various smart devices in numerous applications. IoT is considered a significant technology for achieving the requirements for a variety of applications. However, load balancing, energy inadequate battery power and security may affect the performance of IoT. Therefore, the Fractional Red Panda Optimisation (FrRPO)-based Cluster Head (CH) selection and routing is proposed in this paper. The IoT network simulation is the primary process. The Deep Q Net (DQN) is utilised for predicting the energy. The FrRPO with fitness factors like predicted energy, delay and distance is used to select the CH. Moreover, the FrRPO with fitness factors like throughput, energy, distance and reliability is used for routing. The metrics like energy consumption, delay and throughput are considered to validate the model, which attains the optimal results of 0.555 J, 0.666 sec and 89.02 Mbps.