An Effective Multi-Objective Optimization Algorithm for Spectrum Allocations in the Cognitive-Radio-Based Internet of Things

The continuous growth of interconnected objects in the Internet of Things (IoT) raises a challenge to the wireless communication technology. Cognitive radio could make full use of the dynamic spectrum access and spectrum diversity over wide spectrum to alleviate the spectrum scarcity problem and satisfy the enormous connectivity demands in IoT, which has garnered significant attention over the last few years. This paper addresses the spectrum allocation problem with respect to both spectrum utilization and network throughput in the cognitive-radio-based IoT. On the one side, each link in a transmission path intends to improve the transmission performance on the assigned spectrum channel to maximize the end-to-end throughput. On the other side, these links share the same spectrum channel to concurrently transmit as much as possible to achieve the maximum spectrum utilization. In order to solve the problem, we propose a concurrent transmission model in the network which reveals the constraints of mutual interference and resource competition in links concurrent transmissions. Based on this model, we formulate the spectrum allocation plan for links as the chromosome (solution) in genetic algorithms. Then, we apply the nondominated sorting genetic algorithm-II to solve the multiobjective spectrum allocation problem. Simulation results validate that the proposed strategy can search the optimal solutions efficiently and satisfy the requirements of spectrum allocation in various cases.