Research On MEC-based Task Offloading Optimization In Internet Of Vehicles

Networked vehicles consist of a VANET(Vehicular ad hoc networks)through V2I(Vehicle-to-Infrastructure)links and V2V(Vehicle-to-Vehicle)links,however,with the huge increase of mobile applications supported by Onboard units,this technology is facing new challenges.Offloading computing tasks to the cloud is considered as a promising approach,but remote deployments creat capacity limitations and delay fluctuations of backbone networks and backhaul networks,which have resulted in the decline of vehicular QoS(Quality of Service).Mobile Edge Computing(MEC)provide IT service environment and cloud computing capability at the edge of the mobile network,and MEC not only meets the expansion requirements of vehicle computing ability,but also makes up the delay shortcoming of core cloud computing.However,with the increase of the number of vehicle terminals,the limited computing resources and the energy consumption cost of MEC offloading system will seriously restrict the interests brought by the task offloading,and the scarcity of spectrum resources is also a difficult problem to be considered in task offloading.In summary,this thesis studies the problem of where to offload and how to allocate computing resources and wireless resources,when the task is offloaded in the MEC-based vehicular networks.The research content mainly includes the following two aspects:1.Matching problem between requesting nodes and servicing nodes is studied when a vehicle wants to offload tasks,a MEC-based offloading framework in vehicular networks is proposed,a vehicle can either offload the task to MEC sever as V2 I link or neighboring vehicle as V2 V link.Considering the limitation and heterogeneity of resources,and the diversity of tasks,the offloading framework is established as combination auction model,and a multi-round sequential combination auction mechanism is proposed,which consists of Analytic Hierarchy Process ranking,task bidding and multi-dimension knapsack algorithm decided offloading winners.Simulation results show that the proposed mechanism can maximize the interests in service nodes while increasing the interests in requesting vehicles under the constraints of the delay and the capacity condition.2.In a scene when the LTE and the LTE-U coexist,the task offloading decision and resource allocation issues are studied for vehicle heterogeneous networks.Considering the link differentiation requirements,i.e.,the high capacity of V2 I links and the high reliability of V2 V links,QoS is modeled as the combination of data transfer rate and latency.According to different QoS to determine the communication mode,the improved K-means algorithm is used to cluster the requesting vehicles,then the LTE-U technology with contention free period which is combined with carrier aggregation technology,and the distribution Q-Learning algorithm is adopted to allocate the channels and power.The simulation results show that the proposed mechanism can maximize the ergodic capacity of the whole LTE area including the overlapping area of the LTE and the LTE-U,while ensuring the communication quality of the only LTE-U.