融合无线携能通信的可充电雾计算迁移研究

A SWIPT⁃based rechargeable fog computation offloading mechanism

雾计算网络场景下移动设备因其电池寿命有限而大大制约了其工作能力,简单的计算迁移方案已无法满足用户的服务需求。因此,文中提出了一种融合无线携能通信(Simultaneous Wireless Information and Power Transfer, SWIPT)的可充电雾计算迁移机制。具体地,通过充分考虑任务迁移比、传输时间和功率分割比的联合优化,构建了一个多用户场景下最小化所有任务完成总能耗的优化问题。基于上述非凸优化问题,提出了一个基于凸差规划与加速梯度的交替优化算法,该算法基于凸差规划和交替优化理论,将非凸优化问题转化成两个凸优化子问题进行交替求解;同时,结合加速梯度下降方法,实现任务迁移比、传输时间和功率分割比等最优解的快速求解。特别地,通过在传统FC模型中融合SWIPT技术,使得智能设备从射频信号中解码结果信息的同时可以从中采集能量,以延长电池的使用寿命。最后,仿真结果表明文中所提出的可充电雾计算迁移机制可以有效降低任务处理能耗,较DGECO方案能耗平均降低了约12.8%。

In fog computing networks,the limited life of batteries of mobile devices greatly restricts network performance,and the simple computation offloading scheme is incapable to meet the service requirements.Therefore,this paper proposes a rechargeable fog computation offloading mechanism based on simultaneous wireless information and power transfer(SWIPT).First,an optimization problem that minimizes the total energy consumption for completing all tasks in a multi⁃user scenario is formulated,and the joint optimization of task offloading ratio,transmission time and power splitting ratio is fully considered.Second,in regard of the formulated non⁃convex optimization problem,an alternating optimization algorithm is.This algorithm converts the solution of the non⁃convex optimization problem into two convex optimization sub⁃problems by employing proposed based on the difference⁃of⁃convex programming(DCP)and the accelerated gradient.Meanwhile,combined with the accelerated gradient descent method,the optimal solutions of task offloading ratio,transmission time and power splitting ratio can be achieved with a fast convergence speed.In particular,by integrating SWIPT into a traditional FC model, smart devices can capture energy from the radio⁃frequency signal while decoding the result. Thus,the battery life can be extended. Finally, the simulation results demonstrate that the proposedrechargeable fog computation offloading mechanism reduces the energy consumption of smart devices andextends their service time, and compared with that of the DGECO scheme, its energy consumption isreduced by an average of 12.8%.