Joint Long-Term Processed Task and Communication Delay Optimization in UAV-Assisted MEC Systems Using DQN

• Published in: MILCOM IEEE Military Communications Conference pp. 294 - 299
• Main Authors: Dehkordi, Maryam Farajzadeh, Jabbari, Bijan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.10.2024
• Subjects: Autonomous aerial vehicles; Deep Q Network (DQN); Delays; Internet of Things; Internet of Things (IoT); Mobile Edge Computing (MEC); Multi-access edge computing; Optimization; Real-time systems; resource allocation; Resource management; Stability analysis; System performance; Trajectory; Unmanned Aerial Vehicle (UAV)
• ISSN: 2155-7586
• DOI: 10.1109/MILCOM61039.2024.10773951

Mobile Edge Computing (MEC) assisted by Un-manned Aerial Vehicle (UAV) has been widely investigated as a promising system for future Internet-of-Things (IoT) networks. In this context, delay-sensitive tasks of IoT devices may either be processed locally or offloaded for further processing to a UAV or to the cloud. This paper, by attributing task queues to each IoT device, the UAV, and the cloud, proposes a real-time resource allocation framework in a UAV-aided MEC system. Specifically, aimed at characterizing a long-term trade-off between the time-averaged aggregate processed data (PD) and the time-averaged aggregate communication delay (CD), a resource allocation optimization problem is formulated. This problem optimizes communication and computation resources as well as the UAV motion trajectory, while guaranteeing queue stability. To address this long-term time-averaged problem, a Lyapunov optimization framework is initially leveraged to obtain an equivalent short-term optimization problem. Subsequently, we reformulate the short-term problem in a Markov Decision Process (MDP) form, where a Deep Q Network (DQN) model is trained to optimize its variables. Extensive simulations demonstrate that the proposed resource allocation scheme improves the system performance by up to 36% compared to baseline models.