Latency Minimization Oriented Hybrid Offshore and Aerial-based Multi-access Computation Offloading for Marine Communication Networks

• Published in: IEEE transactions on communications 2023-11, Vol.71 (11), p.1-1
• Main Authors: Dai, Minghui, Huang, Ning, Wu, Yuan, Qian, Liping, Lin, Bin, Su, Zhou, Lu, Rongxing
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous aerial vehicles; Autonomous underwater vehicles; Communication; Communication networks; Communications networks; Computation offloading; Data processing; Edge computing; Energy consumption; hybrid offshore and aerial-based computing offloading; Marine resources; Mobile computing; Multi-access edge computing; Multi-access mobile edge computing; NOMA; NOMA and FDMA; Optimization; Quality of service; Quality of service architectures; resource allocation; Resource management; Surface vehicles; Task analysis; Unmanned aerial vehicles; Unmanned vehicles; Workload; Workloads
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2023.3306581

The explosively increasing development of marine communication networks will improve the quality of service (QoS) of marine applications (e.g., ocean farm and marine tourism), which has attracted much attention from both academia and industrial in recent years. However, real-time data processing for diverse marine tasks (especially those computing-intensive and latency-sensitive tasks) is still challenging due to the limited marine communication and computing resources. Mobile edge computing (MEC) driven by powerful computing capability is envisioned as a promising solution to address the issue for resource-constrained marine services. In this paper, we propose a hybrid offshore and aerial-based multi-access edge computing scheme in marine communication networks to improve the QoS of marine applications. Specifically, we consider a scenario that both offshore base-station and unmanned aerial vehicles (UAVs) are equipped with edge-servers, and the computation workloads of unmanned surface vehicle (USV) can be simultaneously offloaded to offshore base-station and UAVs via multi-access manner. To minimize the latency of completing USV's workloads and reduce USV's energy consumption, we formulate a joint optimization problem to optimize the offloading decision, transmission time, and computing-rate allocation, with the objective of Minimizing the Maximum Workloads Latency (MMWL). Exploiting the features of the formulated problem, we present a layered structure approach and decompose it into three subproblems. We propose efficient algorithms to obtain the optimal solutions and validate the optimality of the proposed algorithms. Finally, we provide simulation results and analysis to demonstrate the effectiveness and efficiency of the proposed scheme and algorithms in comparison with benchmark algorithms.