부산대학교 도서관

As 5-th Generation (5G) mobile communication and edge computing technologies mature, Intelligent Transportation System (ITS) are gradually becoming a reality. In the 5G heterogeneous network, resources such as computing, storage, and communication are allocated to each Road Side Unit (RSU) to provide intelligent services for vehicles. However, the existing average allocation method based on historical experience can easily lead to over-concentration or insufficient resources, which causes waste and reduces the Quality of Service (QoS). To solve this problem, this paper proposes a Multi-Objective Neural Time-series Prediction (M-ONTP) scheme for resource pre-allocation scenario in ITS. The scheme takes into account the complexity and diversity of service resource, innovatively treats the number of vehicles and communication power as joint optimization metrics, and proposes a multi-objective learning model. Benefiting from the vehicle data collected by RSUs in real time, we utilize historical traffic information to predict future road load and rely on Software Defined Network (SDN) to design a flexible resource pre-allocated architecture for ITS. To enhance the effectiveness of feature capture, M-ONTP also organically integrates various neural networks, which can appropriately handle large-scale time-series traffic flow. And we choose two layers of road data for fitting, which ensures that the model has a wide horizon to receive sufficient information. SUMO-based simulation experiments show that our scheme accurately realizes the prediction of joint objective and has significant performance advantage over other models. Meanwhile, our pre-allocation strategy reduces the total resource consumption by about 7%, increases the sufficiency rate by about 7%, and decreases the redundancy by about 12% while ensuring enough service resource to maintain normal QoS, which validates the effectiveness of M-ONTP.