基于DEABC算法的EH-CRAIoT的资源分配算法研究  

DEABC⁃based cognitive radio agriculture IoT resource allocation algorithm with function of energy harvesting

作　　者：崔馨月 富爽[1] CUI Xinyue;FU Shuang(Heilongjiang Bayi Agricultural University,Daqing 163319,China)

机构地区：[1]黑龙江八一农垦大学,黑龙江大庆163319

出　　处：《现代电子技术》2024年第13期55-60,共6页Modern Electronics Technique

基　　金：黑龙江省自然科学基金联合引导项目(LH2023F042)。

摘　　要：针对具有无线能量收集功能的认知农业物联网中上行链路资源分配的优化问题,提出一种基于差分蜂群算法的资源分配算法。首先建立了认知农业物联网资源分配的系统模型,以系统的平均传输速率最大化为优化目标,通过在传统的人工蜂群算法中引入差分算法的更新策略,提升人工蜂群算法的收敛速度和搜索能力;再利用改进后的差分蜂群算法求解得出最优分配方案。仿真实验表明,差分蜂群算法具有良好的寻优效率和收敛性能,通过调整传感器节点的发射功率和感知时隙,能够有效在传感器节点进行频谱感知、信息传输的能量需求的前提下提升系统的平均传输速率。This article proposes a resource allocation algorithm based on differential evolution artificial bee colony(DEABC)algorithm for the optimization of uplink resource allocation in cognitive radio agriculture IoT(CRAIoT)with the function of energy harvesting(EH).A system model for resource allocation in the CRAIoT is established,the optimization goal of which is to maximize its average transmission rate.By introducing the update strategy of differential evolution(DE)algorithm into the traditional artificial bee colony(ABC)algorithm,the convergence speed and search ability of the ABC algorithm are improved,and the improved DEABC algorithm is used to solve the optimal allocation scheme.Simulation experiments have shown that the DEABC algorithm has good optimizing efficiency and convergence performance.By adjusting the transmission power and perception time slot of sensor nodes,the proposed method can effectively improve the average transmission rate of the system while meeting the energy requirements for spectrum sensing and information transmission of sensor nodes.

关 键 词：认知无线电 农业物联网 资源分配 无线能量收集 系统平均传输速率 蜂群算法 

分 类 号：TN929.5-34[电子电信—通信与信息系统]