Due to increasing maritime activities, the number of Maritime Internet-of-things (MIoT) devices requiring real-time marine data processing is growing exponentially. To offload maritime tasks and address the limited computational capabilities of heterogeneous MIoT devices, edge and cloud computing networks are employed. However, these networks introduce several challenges, including increased energy consumption and service latency within the complex marine network environment. Current state-of-the-art solutions address these issues by focusing exclusively on real-time offloading data, neglecting the relationship with past offloading tasks. In this work, we develop an optimization framework, named VESBELT, for offloading tasks from essel users to nearby dge ervers or the cloud server, aiming to reduce nergy consumption and service atency rade-off through a multi-objective linear programming problem. However, finding optimal solutions from this formulation is considered an NP-hard problem. To address this, we introduce VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems that leverage ensemble convolutional, artificial neural networks and Long-short-term memory, respectively, to achieve solutions in polynomial time. The developed ensemble models integrate multiple combinations of deep learning models and exploit the pre-trained models to provide real-time solutions with better prediction accuracy. The experimental findings, obtained using Python programming version 3.10.2, indicate that the proposed VESBELT-ECNN, VESBELT-EANN, and VESBELT-ELSTM systems outperform existing approaches in terms of user Quality of Experience (QoE) in the timeliness domain and energy savings.

中文翻译：

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VESBELT：使用集成神经网络在海事物联网网络中实现节能、低延迟感知的任务卸载


由于海事活动的增加，需要实时海洋数据处理的海事物联网（MIoT）设备的数量呈指数级增长。为了卸载海事任务并解决异构 MIoT 设备有限的计算能力，采用了边缘和云计算网络。然而，这些网络带来了一些挑战，包括在复杂的海洋网络环境中增加能源消耗和服务延迟。当前最先进的解决方案通过仅关注实时卸载数据而忽略与过去卸载任务的关系来解决这些问题。在这项工作中，我们开发了一个名为 VESBELT 的优化框架，用于将任务从 essel 用户卸载到附近的分布式服务器或云服务器，旨在通过多目标线性规划问题来减少能源消耗和服务效率折衷。然而，从这个公式中找到最优解被认为是一个 NP 难题。为了解决这个问题，我们引入了 VESBELT-ECNN、VESBELT-EANN 和 VESBELT-ELSTM 系统，它们分别利用集成卷积、人工神经网络和长短期记忆来在多项式时间内实现解决方案。开发的集成模型集成了深度学习模型的多种组合，并利用预先训练的模型提供具有更好预测精度的实时解决方案。使用 Python 编程版本 3.10.2 获得的实验结果表明，所提出的 VESBELT-ECNN、VESBELT-EANN 和 VESBELT-ELSTM 系统在及时性领域的用户体验质量 (QoE) 和节能方面优于现有方法。