The Multi-access Edge Task Offloading In C-V2X

With fulfillment and commercial deployment of the 5G network,the internet of connected vehicle based on cellular mobile network is surging developed.Cellular vehicle-to-everything(C-V2X)is a new-born technology combining electronic,information,traffic,artificial intelligence and so on.Vehicles can communicate with other devices on the road to get traffic information via radio network,which also invokes new inspirations of self-driving technology.However,on the one hand,most missions involved in self-driving are computation-intensive and latency-sensitive; on the other hand,the calculation and memory resources in vehicles are limited.Due to the gap between high task requirements and vehicle resource limitations,it is hard for vehicles to handle all the self-driving tasks by itself.Fortunately,the appearance of multi-access edge computing(MEC)architecture brings a promising measure to bridge this gap.MEC servers offer mobile devices with computation and memory resources at the network edge.Thus,vehicles can offload complicated tasks to MEC server for tackling.Through investigating,we found that it casts stringent requirements for task offloading in C-V2X system on three dimensions,namley information security,offloading latency and energy consumption.To be specific,MEC makes up the limitation of vehicle resources,it enables the wireless communication between vehicles and MEC server.Compared with computing tasks locally,privacy data is easier for attacker to eavesdrop during task offloading period,which brings the insecurity wireless communication problem.Additionally,on the one hand,the autonomous vehicle is tight related to human life safety.If offloaded task cannot be computed before task deadline,there might be a car accident.On the other hand,the tendency of autonomous vehicles developing is electricity driven.Hence,task offloading should consume as little energy as possible to save power for normal vehicle miles.However,few works study task offloading problem combining information security,offloading latency and energy consumption these three factors.Based on this blank,we established system models of task offloading under MEC based C-V2X circumstance and proposed an effective task offloading scheme.The main research works are as follows:(1)The architecture and application of C-V2X and MEC were illustrated firstly.The importance of task offloading technology in MEC based C-V2X for autonomous vehicle was explained.Then,we found that autonomous vehicles cast stringent requirement of task offloading on information security,offloading latency and energy consumption three points due to its life-related and electricity-driven features.According to our investigating,the research of task offloading combining these three important dimensions is nearly situated in blank state.Therefore,we built system models under MEC based C-V2X task offloading.Specifically,system models are divided into four parts,namely task offloading model,wireless channel model,task computation model and vehicle energy consumption model.Artificial noise technology is adopted in system model to confuse the data recognition of eavesdropper and protect information security at physical layer.(2)According to autonomous vehicle actual requirement,we put up with a latency and energy consumption jointly optimizing problem based on information security.When analyzing the problem,we found that the mobility is an inevitable factor in C-V2X system.It may cause the variation of wireless channel and further influence the data transmission in task offloading.Therefore,we proposed a time approximation algorithm to tackle this problem.Then,we solved the optimization problem and found the optimal offloading proportion to minimize the weighted sum of latency and energy consumption.A physical layer security assisted task offloading scheme was proposed to cater the requirements of autonomous vehicle task offloading in information security,offloading latency and energy consumption and better abide by the real condition of MEC based C-V2X system.(3)We testified the proposed physical layer assisted task offloading scheme.First,the parameter setting of simulations were presented.Then,the experiments were demonstrated based on control variate method to explore the influence of self-interference on task offloading proportion,the relationship between artificial noise power and the task offloading,and the impact of weight parameters on the weighted sum of latency and energy consumption.Last,we compared the scheme with previous methods to study the variation of weighted sum of latency and energy consumption under different self-interference and the variation of artificial noise power and offloading proportion under different speed velocity.The experimental results show the effectiveness and feasibility of our proposed scheme.