Research On Computing Offloading And Energy Efficiency Optimization Based On Mobile Edge Computing

The intelligent interconnection and cloud computing technologies become increasingly mature when the rapid development of mobile Internet of Things and wireless communication technologies.Explosive terminal device access and the data traffic generated have grown exponentially,especially the computation-intensive and delay-sensitive mobile application data bring severe challenges to traditional communication network structures.At the same time,terminal devices with limited computing and energy resources unable to meet the requirements of high complexity and energy consumption business scenarios.It has aroused widespread attention of academia and industry to seek a new generation of network structure and information processing mechanism,which can response the needs of an increasingly data-driven era.Accordingly,Mobile Edge Computing(MEC)came into being with the advantages of cloud computing and mobile Internet,it can provide the computing and storage functions needed by applications to a greater extent closer to users,so as to relieve the pressure of the core network and provide users with low latency,low energy consumption and high reliable network transmission at the same time.However,for the increasing computing requirements of edge users and hardware constraints of energy limited,let the computing offloading strategy and energy allocation methods of MEC have great challenges.Therefore,studying efficient computation offloading and energy optimization mechanisms is an important issue that needs to be solved in MEC system.As a key technology of MEC network,computation offloading can offload real-time application data to the edge of network that near the terminal device,and provide computing and energy support for resource-constrained mobile device application processing,the additional overhead of task offloading to the core cloud and the resource and delay consumption of the backhaul link are reduced at the same time.Energy harvesting technology(EH)is attracted much attention,it is used to provide special energy supply for energy-constrained terminal device,and ensures efficient and orderly processing of tasks.Thus,this thesis further studies computation offloading,energy efficiency optimization scheme and system resource optimization of MEC environments,so as to reduce task execution delay and improve MEC system energy efficiency.The main contents are as follows:1.Aiming at the computationally intensive task offloading makes the energy consumption of mobile terminal devices become higher,a computing offloading strategy based on Population Diversity-Binary Particle Swarm Optimization(PD-BPSO)algorithm is proposed.Firstly,the energy consumption model of the terminal devices under the constraint of multi-object is established.Secondly,the particle swarm optimization algorithm is used to convert the offloading of task into the particle optimization process,and obtain the optimal offloading selection with satisfying the energy consumption minimization.Finally,numerical results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.2.Aiming at the low energy efficiency and inflexible service of resource-constrained terminal devices caused by intensive computing tasks offloading in mobile edge computing,a system energy efficiency optimization scheme based on energy harvesting is proposed.Firstly,the energy harvesting status and power allocation of users are analyzed under the constraints of offloading transmission power and so on,and a joint optimization model is established to maximize system energy efficiency;Secondly,the offloading energy efficiency is transformed into standard convex optimization by the generalized fractional programming theory,and the objective function is iteratively optimized by setting the Lagrange function to obtain the optimal energy indicator variable and power allocation.Finally,the simulation results show that the proposed scheme can improve user energy efficiency by 15.6% and realize green communication.