基于无线能量传输的物联网联合资源分配算法OA
Internet of Things joint resource allocation algorithm based on wireless energy transmission
在物联网场景中,为满足用户发射功率与前传容量的约束条件,需对用户上行传输功率与射频拉远头的前传容量进行联合优化.然而,该优化过程依赖电池供电以维持相关设备的持续运行,但电池电量有限,难以持续、稳定地提供充足的能量支持,使得联合资源分配困难.为解决该问题,提出一种基于无线能量传输的物联网联合资源分配算法.基于无线能量传输技术构建物联网通信模型,利用能量捕获模块从能量广播站捕获能量,为物联网终端提供电量.针对所构建的物联网通信模型,综合考虑物联网的总资源量、总能效及总时延,以物联网的平均能效最大化为目标函数,同时设置任务处理消耗电量约束、物联网资源约束及基站发射功率约束,构建物联网联合资源分配优化模型.利用灰狼优化算法求解所构建的联合资源分配优化模型,输出最优的资源分配方案.实验结果表明,采用所提方法分配物联网联合资源,能量效率高于15 Mb/J,能够有效提升网络通信效率.
In the Internet of Things(IoT)scenario,in order to meet the constraints of user transmission power and forward capacity,it is necessary to jointly optimize the user's uplink transmission power and the forward capacity of the remote radio unit.However,this process requires battery power to maintain the continuous operation of related equipment.Due to limited battery capacity,it is difficult to provide sufficient and stable energy support,which leads to difficulties in joint resource allocation.Therefore,an IoT joint resource allocation algorithm based on wireless energy transmission is proposed.An IoT communication model is constructed based on wireless energy transmission technology,the energy capture modules are used to capture energy from energy broadcasting stations to provide power for the IoT terminals.In allusion to the constructed IoT communication model,the maximization of the average energy efficiency of the Internet of Things is used as the objective function by taking into account the total resources,energy efficiency,and latency of the IoT.The constraints on task processing power consumption,IoT resources,and base station transmission power are set to construct an IoT joint resource allocation optimization model.The grey wolf optimization algorithm is used to solve the constructed joint resource allocation optimization model and output the optimal resource allocation scheme.The experimental results show that the energy efficiency of allocating IoT joint resources using the proposed method is higher than 15 Mb/J,effectively improving network communication efficiency.
张晓宇
辽宁工业大学 电子与信息学院,辽宁 锦州 121001
信息技术与安全科学
无线能量传输物联网联合资源分配能量捕获模块灰狼优化算法通信效率
wireless energy transferInternet of Thingsjoint resource allocationenergy harvesting modulegrey wolf optimization algorithmcommunication efficiency
《现代电子技术》 2026 (2)
23-28,6
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