Research On Content Scheduling Optimization Strategy Of Self-Powered Roadside Unit

The Internet of Vehicles is the key direction for the development of intelligent transportation in the future.With the communication between vehicles,vehicles and roads,and vehicles and other traffic participants,it can effectively alleviate the information asymmetry of intelligent transportation.As an important part of the Internet of Vehicles,the roadside unit can collect vehicle data content and efficiently use computing and analytics capabilities to empower intelligent transportation applications and deliver information content to other vehicles to enhance information sharing capabilities.At present,the self-powered roadside unit powered by renewable energy is widely used in the sections that cannot be covered by the traditional power grid.In order to make the self-powered roadside unit maintain a long-term and stable working state in the face of complex traffic variables to meet the requirements of the Internet of Vehicles for the timeliness of information content,it is necessary to reasonably plan the content scheduling strategy of the self-powered roadside unit and optimize its working energy consumption under the premise of ensuring work efficiency.This paper proposes a self-powered roadside unit content scheduling strategy from the perspectives of content calculation and content push to achieve energy consumption optimization.The main work of the paper is as follows :(1)This chapter proposes a self-powered roadside unit data content calculation delay-energy consumption balancing scheduling strategy.The self-powered roadside unit collects the data content of the arriving vehicle and adjusts the calculation frequency to calculate and process it to reduce the calculation energy consumption.Considering the randomness of the speed state of the arriving vehicle and the quantity state of the calculated content,a content computing scheduling model is constructed based on the Markov decision framework.The Markov chain is used to characterize the content cache queue state transition process,and the average processing delay and average energy consumption of the calculated content are analyzed.Under the condition of energy consumption constraint,the optimal scheduling strategy for minimizing the content computing delay is solved.Finally,the simulation results show that the proposed self-powered roadside unit data content calculation delay-energy consumption balance scheduling strategy can adaptively adjust the number of calculation content to reduce the content calculation delay and energy consumption,and the strategy is compared with the greedy strategy and Q-learning strategy.The analysis shows that the proposed strategy has more advantages in optimizing delay and energy consumption.(2)This paper proposes an optimal pushing scheduling strategy for self-powered roadside units joint content cache.The self-supplied circuit-side unit pushes content to the vehicle by prefetching cached content and renewable energy to reduce transmission delay,control traffic load,and reduce energy consumption.Considering the randomness of prefetching content and renewable energy collection,a content push scheduling model is constructed based on the Markov decision framework,and the system state transition process is represented by a two-dimensional Markov chain.Energy consumption,dynamically adjust the selection of content pushed by the self-powered roadside units for vehicles in different locations.Finally,the strategy and the greedy push strategy are compared and analyzed,and it is verified that the content push scheduling strategy of the joint cache can make the self-powered roadside units adaptively adjust the content to be pushed by vehicles in different locations,so as to meet the constraints of the energy consumption of the self-powered roadside units to push the content.Next,minimize the delay in pushing content.