ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1629563
Cumulative Confidence-Driven Task Offloading for Object Detection in Maritime Internet of Things
Provisionally accepted
- 1Navigation Institute, Jimei University, Xiamen, China
- 2School of Ocean Information Engineering, Jimei University, Xianmen, China
- 3School of Information and Communications Engineering, Xi’an Jiaotong University, Xian, China
- 4Fujian Xinzhi Information Technology Co., LTD, Longyan, Longyan, China
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Maritime mobile edge computing (MMEC) technology facilitates the deployment of computationally intensive object detection tasks on Maritime Internet of Things (MIoT) devices with limited computing resources. However, the dynamic marine network and environmental interference in feature extraction adversely affect detection accuracy and delay. In this paper, we propose a cumulative confidence-driven joint scheduling strategy for image detection tasks in MMEC scenarios. The strategy employs lightweight and full models as the detection framework.Through an adaptive decision-making scheme for marine device image recognition, the proposed strategy accumulates results from different models within the framework to ensure quality of service (QoS). To obtain a dynamic offloading strategy that minimizes total system cost of latency and energy consumption, the problem is divided into two sub-problems, and a chemical reaction optimization algorithm is used to reduce computational complexity. Then, a state normalization action project deep deterministic policy gradient (SNAP-DDPG) algorithm is proposed to handle environmental dynamics, achieving minimized system cost with satisfied QoS. The simulation results indicate that, compared to existing algorithms, the proposed SNAP-DDPG algorithm keeps object detection confidence with latency reducing by 34.78%.
Keywords: Maritime internet of things, Edge computing, You look only once, Task offloading, reinforcement learning
Received: 19 May 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Sun, Luo, Xu, Mei, Peng and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Weijian Xu, School of Ocean Information Engineering, Jimei University, Xianmen, China
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