A Biologically Inspired Channel Allocation Method for Image Acquisition in Cognitive Radio Sensor Networks

In recent years, cognitive radio sensor networks (CRSNs) have been commonly applied in environmental monitoring and image acquisition. However, recent advances in channel allocation have led to lower network reward, lifetime, and energy utilization rate. As a basic and fundamental problem to obtain image data in CRSNs, it governs the performance of CRSNs. To further improve the reward and throughput of obtaining image, this paper proposes an improved immune hybrid bat algorithm (IIHBA) based on bat algorithm. Furthermore, we develop a simulation environment and compared the performance of IIHBA with particle swarm optimization (PSO) and genetic algorithm (GA). Last but not the least, computational experiments showed that the reward is improved 11.36%, 27.20% respectively compared with GA and PSO when the number of users is 20 and the number of channels is 5. Based on the above findings, the proposed scheme can improve the reward of system, especially in terms of higher-throughput.