Research On Offloading And Caching Strategy Of Internet Of Vehicles Based On Mobile Edge Computing

As one of the main application scenarios of 5G,the Internet of Vehicles needs to deal with massive data.Although traditional cloud computing can provide abundant resources,it is difficult to respond to users’ requests in real time due to its long distance from vehicles,and Mobile Edge Computing can provide timely and reliable services nearby.By studying the unloading and caching technology based on MEC,we can make up for the shortage of vehicle resources and the disadvantage of cloud computing distance.However,the existing unloading and caching strategies are mostly aimed at the fixed network architecture,ignoring the cooperation between devices.This thesis starts from MEC and further studies the unloading and caching strategies of the Internet of Vehicles.The main work is as follows:1)Firstly,a "vehicle-edge-cloud" collaborative architecture based on MEC was established.By combining emerging 5G technologies,the architecture completed the centralized allocation of network resources,expanded network functions and improved network flexibility.By utilizing the cooperation among vehicles,MEC and cloud servers,the single-layer service was extended to multiple layers,thus avoiding resource waste.2)Secondly,aiming at the problems of time delay and energy waste in task unloading of Internet of Vehicles,an improved fireworks algorithm unloading strategy based on MEC was proposed.By expanding the neighborhood of explosion sparks from square to sphere,the displacement operation object was improved from fireworks to all sparks,and the roulette was replaced by the tournament selection strategy with low time cost,which made the improved fireworks algorithm have faster iteration speed and wider search range when solving the unloading optimization problem.Simulation results show that when the task data volume reaches 5 Mbit,the offloading delay of the proposed strategy is about 15.2%,39.1% and 46.2% lower than FWA,DOAG and SDR-AO-ST respectively,and the transmission energy consumption is about 12.8%,22.1% and 33.9% lower than that of FWA,DOAG and SDR-AO-ST respectively.3)Finally,aiming at the problems of low cache hit rate and long download time when caching content in Internet of Vehicles,an ant colony simulated annealing algorithm caching strategy based on MEC was proposed.The local optimal solution of cache optimization problem was able to obtain by introducing delay parameters into ant colony algorithm.On the basis of this solution,the global optimal solution was able to obtain by using the probability jump of simulated annealing algorithm.Applying the combined algorithm to solve cache problem could not only improve the calculation speed,but also jump out of the local optimal solution.Simulation results show that when the number of service content reaches 1000,the cache hit rate of the proposed caching strategy is about15.9%,22.7% and 28.8% higher than that of DLC,MAP and RC respectively,and the average download delay is about 7.9%,17.3% and 33.1% lower than that of DLC,MAP and RC respectively.