MAT-IGA: A Deadline and Priority-Aware Task Offloading Method in Resource-Constrained Vehicular Networks

With the developments of the intelligent vehicles, the most significant contradiction is coming to the limited computing resource and the unlimited requirements. Task offloading has been proposed to overcome this. Most of the existing task offloading methods aim to optimize the global delay. However, from the user’s point of view, different in-vehicle applications (such as entertainment and autonomous driving) have different importance and delay requirements. Therefore, simply minimizing the latency of all tasks does not meet the QoS (Quality of Service) of each user. Unfortunately, very few people discuss this practical issue. In this paper, the problem of vehicular task offloading in resource-constrained scenarios is studied, and a two-stage task offloading scheme MAT-IGA based on multi-node collaboration is proposed. In the first stage, the complex tasks are pre-segmented adaptively, and the optimal matching set is solved by combining the improved multi-round deferred-acceptance algorithm, which prioritizes the resource requirements of emergency tasks and improves the reliability of the strategy. In the second stage, a chaotic genetic algorithm based on opposition-based learning is used to optimize resource allocation, and the global delay and cost are optimized on the basis of ensuring the success rate. The simulation results show that the proposed method is superior to the common baseline algorithm in different vehicle numbers and task characteristics.