Research On Edge Computing And Offloading Mechanism Of Parken Vehicles Based On Blockchain

With the rapid development of mobile Internet,smart cities,autonomous driving,and mobile devices,many computation-intensive but resource-limited nodes are emerging.Therefore,it is imperative to explore a safe and effective offloading scheme for computational tasks.The storage and computing capacity of vehicles has also been greatly improved by advances in hardware and communication technology,but these resources are often idled and wasted while the vehicles are parked.The use of parking resources can relieve the pressure on the central node in traditional computing systems,bringing vested interest to vehicle owners and a lasting impact on society.The main contents of this paper are as follows:(1)The Blockchain-based Parked Vehicular Edge Computing(BPVEC)platform is proposed to guarantee the security and privacy of vehicle resource sharing by using blockchain technology and to provide distributed accounting capability for transactions.An integrated optimization framework is proposed to leverage the green energy utilization and service latency limits among the block generation,task computing,and communication process.The problem is solved based on mixed-timescale optimization algorithm.A shaped deep deterministic policy gradient(DDPG)algorithm is proposed to accelerate the learning rate of computational frequency control in the short-term stage;while in the long-term stage,for the mixed-integer programming of task offloading and blockchain parameters adjustment,a series of transformation is employed to preserve convexity.Simulation results demonstrating that the proposed scheme balances between the distributed energy/computation resources,service latency demand,and blockchain bearable capacity,while the battery depreciation cost is heavily reduced.(2)To increase the availability of the system,the directed acyclic graph(DAG)is adopted to describe the characteristics of the task of a complex application and employed collaborative computing between vehicles to provide efficient computing services.The vehicle adopts a hybrid energy supply mode,and the calculation costs of different modes are integrated into the system.Considering the uncertainty of PV’s sojourn time and the randomness of green energy arrival,the reliability of the system is modeled.Alternating optimization(AO)is used to solve the optimal computing resource allocation strategy and offloading strategy respectively.For the complex constraint forms in the optimization problem,the multi-level Kuhn-Munkres(KM)algorithm is used to implement hierarchical scheduling according to task priority to decouple the complex dependencies among tasks.Simulation results show that the system can provide effective computing services for complex applications,maintain reliability in various environments,and maximize the utility of PVs.