【摘要】 移动边缘计算(Mobile Edge Computing,MEC)技术是当前无线传感器网络的重要研究方向之一。MEC技术能将无线传感器设备的本地计算任务卸载到边缘云服务器进行计算,从而大大提高了无线传感器网络的计算能力。但是无线网络中大量设备同时进行计算卸载会导致信号干扰和边缘云服务器的计算负载过大。为了提高无线网络的计算质量,首先提出了一种最小化多个无线传感器设备的MEC系统计算时间周期的合理时间分配和计算卸载的策略,并采用了5G非正交多址接入和串行干扰删除技术使多个无线设备可以利用相同的子载波同时进行计算卸载,从而提高计算卸载的效率;然后建立了无线设备能量捕获和任务计算的相关模型,将上述模型和策略建模为一个优化问题进行求解;最后通过数值分析实验验证了所提策略的有效性。更多还原

【Abstract】 Mobile edge computing(MEC) technology is one of the important research directions of current wireless sensor networks.MEC technology can offload local computing tasks of wireless sensor devices to the edge cloud server for computing,thereby greatly improve the computing capacity of wireless sensor networks.However,a large number of devices in the wireless network perform computation offload at the same time,which will cause signal interference and excessive computational load on the edge cloud server.First,in order to improve the computation quality of wireless networks,a reasonable time allocation and computation offloading strategy for minimizing the computing time period of a MEC system with multiple wireless sensor devices is proposed,and 5 G non-orthogonal multiple access and successive interference cancellation technology enables multiple wireless devices to perform computation offloading at the same time using the same subcarrier,there by improving the efficiency of computation offloading.Then the related models of wireless device energy harvesting and task computing are established,which are modeled as an optimization problem according to the above models and strategies,and the problem is solved.Finally,the effectiveness of the proposed strategy is verified by numerical analysis experiments.更多还原