The growing usage of the industrial cognitive radio sensor network (ICRSN) brings profound changes to the Internet of Things. The ICRSN is an emerging technique to transfer industrial data, which has strict and accurate communication requirements in a large number of areas such as environmental surveillance, building monitoring, control, and many other areas. The problem of maximizing the sum bandwidth by using a spectrum allocation algorithm has been extensively studied in this paper. Inspired by chaos theory and quantum computing, this work presents a new chaotic hybrid immune genetic algorithm (CHIGA). We then introduce a spectrum allocation model that considers both network reward, throughput, and convergence time. The improvement of CHIGA performance through experimental simulations is evaluated in terms of the sum network reward compared to methods based on simulated annealing (SA), ant colony optimization (ACO), and particle swarm optimization (PSO). Simulation results show that the CHIGA has a higher network reward and throughput existing optimized algorithms while maintaining total system throughput.

中文翻译：

---

ICRSN中用于频谱分配优化的混沌混合免疫遗传算法

工业认知无线电传感器网络（ICRSN）的使用不断增长，给物联网带来了深刻的变化。ICRSN是一种新兴的传输工业数据的技术，它在许多领域（例如环境监控，建筑物监控，控制和许多其他领域）都具有严格而准确的通信要求。本文已经广泛研究了使用频谱分配算法最大化总带宽的问题。受混沌理论和量子计算的启发，这项工作提出了一种新的混沌混合免疫遗传算法（CHIGA）。然后，我们引入一种频谱分配模型，该模型同时考虑网络奖励，吞吐量和收敛时间。与基于模拟退火（SA），蚁群优化（ACO）和粒子群优化（PSO）的方法相比，通过实验仿真对CHIGA性能的改进是根据总网络奖励来评估的。仿真结果表明，CHIGA具有较高的网络奖励和吞吐量，而现有的优化算法却保持了系统总吞吐量。