title: Deep Reinforcement Learning based Energy-efficient Task Scheduling for Vehicular Edge Computing
Creators: name: Li, Peisong
Publisher: name: University of Liverpool Repository
Year: 2025

description: Nowadays, the increasing complexity of vehicles has led to a growing demand for in-vehicle services that rely on a variety of sensors. These service requests, such as autonomous driving, are safety-oriented and typically require the fusion of processing results from numerous independent computation tasks generated by diverse onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. 
In this context, Vehicular Edge Computing (VEC) emerges as a promising paradigm for delivering a wide array of intelligent services. Within scenarios involving VEC, computation tasks generated from vehicles can be offloaded to edge servers, effectively reducing the time taken to complete service requests and lowering the energy consumption of vehicles. Therefore, there is a requirement for a task scheduling strategy to make decisions regarding task scheduling for vehicles.
With the aim of optimizing the completion time and energy consumption of in-vehicle service requests, this thesis focuses on designing an efficient method for task scheduling and resource allocation in VEC scenarios.
Firstly, a task scheduling strategy is designed to allocate multiple AI model inference tasks to different in-vehicle applications within the vehicle, utilizing the proposed Simplest Allocation Method. With this strategy, time-consuming tasks can be allocated to the applications with light workload, thereby reducing the completion time of the multi-model inference services. Experimental results validate the effectiveness of task scheduling strategy in reducing the completion time of multi-model inference services.
Secondly, Deep Reinforcement Learning (DRL) is employed to enhance the aforementioned task scheduling strategy. After self-directed learning, the improved DRL-based task scheduling method can dynamically make decisions regarding task scheduling, aiming to minimize request completion time. Experimental results indicate that the DRL-based method outperforms conventional heuristic methods in terms of reducing request completion time and decision-making time. 
Thirdly, in order to jointly optimize request completion time and energy consumption for multiple vehicles in VEC scenarios, a multi-agent reinforcement learning-based task scheduling method is proposed. This method allows computation tasks to be executed either locally or offloaded to edge servers and idle vehicles. Extensive experiments are performed and the results demonstrate that the proposed method outperforms the compared methods in terms of request completion time, overall energy consumption, and method scheduling time.
Finally, in order to satisfy the requirements of dynamic VEC scenarios with a changing number of vehicles, a task scheduling and resource allocation method is devised based on the proposed Environment-adaptive Proximal Policy Optimization algorithm. This method surpasses existing studies by incorporating task prioritization and dynamic transmit power allocation to further reduce completion time and energy consumption. Moreover, the proposed method exhibits adaptability to dynamic VEC scenarios, enabling decision-making for varying numbers of vehicles.