Retraction Notice: A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-23 DOI:10.1109/TNSE.2025.3582063

Xiaoming Yuan;Zedan Zhang;Chujun Feng;Yejia Cui;Sahil Garg;Georges Kaddoum;Keping Yu

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引用次数: 0

Abstract

The rapid expansion of wearable medical devices and health data of Internet of Medical Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health care in the foreseeable 6 G era. Healthcare applications and services require ultra reliable, ultra low delay and energy consumption data communication and computing. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies empowered IoMT to deal with huge data sensing, processing and transmission in high QoS. However, traditional frame aggregation schemes in WBAN generate too much control frames during data transmission, which leads to high delay and energy consumption and is not flexible enough. In this paper, a Deep Q-learning Network (DQN) based Frame Aggregation and Task Offloading Approach (DQN-FATOA) is proposed. Firstly, different service data were divided into queues with similar QoS requirements. Then, the length of the frame aggregation was selected dynamically by the aggregation node according to the delay, energy consumption, and throughput by DQN. Finally, the number of tasks offloaded was selected due to the current state. Compared with the traditional scheme, the simulation results show that the proposed DQN-FATOA has effectively reduced delay and energy consumption, and improved the throughput and overall utilization of WBAN.

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一种基于dqn的边缘IoMT帧聚合和任务卸载方法

可穿戴医疗设备和医疗物联网（IoMT）健康数据的快速扩张，在可预见的5g时代对智能医疗的高服务质量（QoS）提出了新的挑战。医疗保健应用和服务需要超可靠、超低延迟和能耗的数据通信和计算。无线体域网络（WBAN）和移动边缘计算（MEC）技术使IoMT能够以高QoS处理大量数据感知、处理和传输。然而，传统的WBAN帧聚合方案在数据传输过程中产生了过多的控制帧，导致时延高、能耗大、灵活性差。提出了一种基于深度q学习网络（DQN）的帧聚合和任务卸载方法（DQN- fatoa）。首先，将不同的业务数据划分到具有相似QoS要求的队列中。然后，聚合节点根据DQN的时延、能耗和吞吐量动态选择帧聚合长度。最后，根据当前状态选择卸载的任务数。仿真结果表明，与传统方案相比，提出的DQN-FATOA方案有效地降低了时延和能耗，提高了WBAN的吞吐量和综合利用率。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.