# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
###### Abstract  
With the rapidly escalating demand for high real-time performance and data throughput capabilities, the limitations of on-board computing resources have rendered traditional computing services inadequate to meet these burgeoning requirements. Vehicular edge computing offers a viable solution to this challenge, yet the roadside units (RSUs) are prone to overloading in congested traffic conditions. In this paper, we introduce an optimal task offloading strategy under congested conditions, which is facilitated by a mixed coverage scenario with both 5G base stations and RSUs with the aim of enhancing the efficiency of computing resource utilization and reducing the task processing delay. This study employs long short-term memory networks to predict the loading status of base stations. Then, based on the prediction results, we propose an optimized task offloading strategy using the proximal policy optimization algorithm. The main constraint is that the data transmission rates of users should satisfy the quality of service. It effectively alleviates the overload issue of RSUs during congested conditions and improves service quality. The simulation results substantiate the effectiveness of the proposed strategy in reducing the task processing delay and enhancing the quality of service. Internet of Vehicles; edge computing; long short-term memory networks; proximal policy optimization +
Footnote †: journal: applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-0000  
(BSs) can assist vehicle networks in providing computing services [9]. In urban road segments, moving vehicles and passengers are covered by mixed networks with both the RSUs and BSs. Existing solutions have thoroughly discussed how to alleviate the overload of RSUs, mainly considering task allocation, the utilization of RSUs in other areas, and the optimization of offloading tasks assisted by drones [10; 11; 12]. Tasks are allocated and offloaded to different targets in a certain proportion, offloaded from the local RSU to the adjacent area RSU, and assisted by drones to alleviate the overload of RSUs. In addition, an auxiliary offloading scheme based on BSs has been introduced. However, due to the different communication systems of intelligent connected vehicles, RSUs, and BSs, various protocols are conflicting and complex, making it difficult to know the load status of BSs in real time, which affects the decision making of task offloading [13]. Moreover, due to the dynamic nature of VECNs, it is necessary to consider whether vehicles will exceed the coverage range of RSUs or BSs during the offloading process. The relative positions of vehicles relative to the offloading target and their moving speeds should be primarily considered. In this paper, we propose an optimal task offloading scheme under the congested traffic conditions in a mixed-coverage scenario. In a congested area covered by both RSUs and BSs, a task can be divided into three parts: offloading to the local area, RSU, and BS. However, the real-time uncertainty of BS loading, the moving of vehicles, and the dynamic transmission channel make it a challenge to design an efficient task offloading scheme. Thus, we propose a real-time prediction method based on long short-term memory (LSTM) for the load status of BSs. After this, for the task offloading allocation optimization problem, we use a proximal policy optimization (PPO) algorithm to find the optimal task offloading allocation strategy, provide vehicle users with a better quality of service (QoS), and alleviate the overload of RSUs. The main contributions are summarized as follows:  
* We construct a task offloading system model in a mixed coverage scenario under congested traffic, using both BSs in the 5G network and RSUs in an ITS to assist vehicles in offloading computation tasks. At the same time, it is stipulated that BSs assist RSUs in task offloading, and the computing tasks can be allocated to the local area, RSUs, and BSs.