Maritime Mobile Edge Computing for Sporadic Tasks: A PPO-Based Dynamic Offloading Strategy

Yanglong Sun, Wenqian Luo, Zhiping Xu, Bo Lin, Weijian Xu () and Weipeng Liu
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Yanglong Sun: Navigation College, Jimei University, Xiamen 361000, China
Wenqian Luo: School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
Zhiping Xu: School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
Bo Lin: School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
Weijian Xu: School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
Weipeng Liu: Navigation College, Jimei University, Xiamen 361000, China

Mathematics, 2025, vol. 13, issue 16, 1-20

Abstract: Maritime mobile edge computing (MMEC) technology enables the deployment of high-precision, computationally intensive object detection tasks on resource-constrained edge devices. However, dynamic network conditions and limited communication resources significantly degrade the performance of static offloading strategies, leading to increased task blocking probability and delays. This paper proposes a scheduling and offloading strategy tailored for MMEC scenarios driven by object detection tasks, which explicitly considers (1) the hierarchical structure of object detection models, and (2) the sporadic nature of maritime observation tasks. To minimize average task completion time under varying task arrival patterns, we formulate the average blocking delay minimization problem as a Markov Decision Process (MDP). Then, we propose an Orthogonalization-Normalization Proximal Policy Optimization (ON-PPO) algorithm, in which task category states are orthogonally encoded and system states are normalized. Experiments demonstrate that ON-PPO effectively learns policy parameters, mitigates interference between tasks of different categories during training, and adapts efficiently to sporadic task arrivals. Simulation results show that, compared to baseline algorithms, ON-PPO maintains stable task queues and achieves a 22.9 % reduction in average task latency.

Keywords: Maritime Internet of Things; maritime mobile edge computing; reinforcement learning; PPO; object detection task (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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