Aiming at the problem that the location of the secondary base station affects the interference between the primary and secondary systems directly and the reasonable allocation of channel resources, an Internet of Things (IoT) sensor network resource allocation scheme using an improved chaotic firefly algorithm is proposed. This solution builds a multi-objective optimization model based on interference analysis of the working scenario of the cognitive radio. The goal is to protect the primary user’s normal activity to maximize the throughput of the secondary system and maximize the number of users that can be covered by the secondary base station. Because the multi-objective model is a non-linear convex optimization problem, the paper uses an improved chaotic firefly algorithm to solve it. Chaos algorithm is introduced into the firefly algorithm. By perturbing individuals, the convergence speed is accelerated and the probability of local optimization is reduced. The algorithm can efficiently obtain the optimal solution while reducing the complexity of the problem. The simulation results show that the method proposed in this paper can optimize the performance of the secondary system while guaranteeing the priority of the primary user. And it is superior to several advanced algorithms.

针对次基站的位置直接影响主次系统之间的干扰以及信道资源合理分配的问题，提出了一种使用改进的混沌萤火虫算法的物联网传感器网络资源分配方案。 。该解决方案基于对认知无线电工作场景的干扰分析，构建了一个多目标优化模型。目的是保护主要用户的正常活动，以使辅助系统的吞吐量最大化，并使辅助基站可以覆盖的用户数量最大化。由于多目标模型是非线性凸优化问题，因此本文采用改进的混沌萤火虫算法对其进行求解。萤火虫算法中引入了混沌算法。通过扰动个人，可以加快收敛速度​​，并降低局部优化的可能性。该算法可以有效地获得最优解，同时降低问题的复杂性。仿真结果表明，本文提出的方法可以在保证主用户优先级的同时，优化辅助系统的性能。它优于几种高级算法。