Resource Allocation In Mobile Edge Computing System Based On Energy Saving

The mobile edge computing(MEC)technology refers to the MEC server collecting large amounts of idle computing power and storage space distributed at the edge of the network to perform computing-intensive and delay-sensitive tasks of mobile devices.Mobile devices can use MEC technology to offload some computing tasks to the MEC server,and then the MEC server can process these computing tasks remotely.Therefore,the time and energy consumption of the system can be significantly reduced and the life of the mobile device can be extended.In this paper,with energy consumption minimization as the optimization goal,three different optimization scenarios are proposed.They are the multi-user MEC system for secure offloading,the wireless powered MEC system,and the MEC system consisted of unmanned aerial vehicles.In response to these scenarios,this paper separately optimizes the system's computing resources and offloading resources to achieve the optimization goal.Chapter 2 studies the physical layer security and resource allocation of multi-users MEC systems.Chapter 2 considers the application of physical layer security technology in a multi-users MEC system to ensure the security of offload information.The MEC system consists of an access point(AP)with an integrated MEC server,multiple mobile devices(MDs),and a malicious eavesdropper.The optimization problem proposed in this chapter reduces the energy consumption of the system by jointly optimizing the allocation of the size of local computing tasks,the computing frequency of local central processing unit(CPU),offloading power and offloading timeslots.To solve the proposed optimization problem,Chapter 2 obtain the optimal solutions of the problem by jointly applying the difference of convex algorithm(DCA)and the Lagrange duality method.In addition,Chapter 2 also proposes a low complexity algorithm based on Karush Kuhn Tucker(KKT)conditions.The final simulation results show that the energy consumption performance of the DCA scheme proposed in Chapter 2 is better than the benchmark schemes.Chapter 3 studies the resource allocation of wireless powered MEC systems.In Chapter 3,a unified multi-user MEC system for wireless power transmission is proposed.The multi-antennas AP(integrated with the MEC server)in the system provides energy for MDs through wireless power transmission(WPT)technology,and Each MD relies on the energy collected from the AP to complete its computing tasks.With the application of MEC technology and WPT technology,these MDs can complete the computing tasks locally or offload all or part of the tasks to the AP for computation according to Time Division Multiple Access(TDMA)Protocol.Based on the proposed MEC model,Chapter 3 jointly optimizes the allocation of the energy transmission beamforming at the AP,the computing frequency of local CPU at each MD,the size of local computing task at each MD,and the offloading timeslot of each MD.Under the constraints of computing delay and energy transmission,the energy consumption of the system is minimized.In terms of algorithm design,Chapter 3 uses advanced optimization solutions to obtain optimal solutions of the optimization problem in a semi-closed form.The final numerical results prove that the performance of the proposed design scheme is better than other benchmark schemes.Chapter 4 studies the problem of ssecure offloading in the MEC system consisted of unmanned aerial vehicle(UAV).Chapter 4 conceives a secure and energy-saving computation offloading strategy for UAV-MEC systems,the key is the application of physical layer security technology in the presence of active eavesdroppers.Based on the proposed UAV-MEC system model,Chapter 4proposes an optimization problem of minimizing the energy consumption of UAVs.To solve the problem,Chapter 4 first convert the original optimization problem into a optimization problem with single variable.Then Chapter 4 prove that the converted problem is a convex optimization problem.Finally,the solutions of the problem can be obtained through the traditional convex optimization method.In addition,Chapter 4 analyzes the energy consumption of UAVs under three offloading strategies,i.e.,full local computing,partial offloading and full offloading.Finally,the numerical results show the energy consumption performance of the UAV-MEC system under the above three specific offloading strategies.