信息年龄和能耗联合优化的无线体域网卸载和调度策略

张政; 谢鑫; 柏桐; 林金朝; 李章勇

doi:10.11959/j.issn.1000-436x.2024148

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信息年龄和能耗联合优化的无线体域网卸载和调度策略

学术论文 | 更新时间：2024-10-10

- 信息年龄和能耗联合优化的无线体域网卸载和调度策略
- Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN
- 通信学报 2024年45卷第9期页码：92-100
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院,重庆 400065
  2.重庆邮电大学光电信息感测与微系统重庆市重点实验室;重庆 400065
  3.重庆邮电大学自动化学院,重庆 400065
  4.重庆邮电大学光电工程学院,重庆 400065
- 作者简介：
  
  [ "张政（1989- ），男，湖南岳阳人，重庆邮电大学博士生，主要研究方向为无线体域网、智慧医疗、实时调度等。" ]
  [ "谢鑫（1990- ），男，河南南阳人，博士，重庆邮电大学讲师，主要研究方向为工业物联网、实时调度、信息年龄优化等。" ]
  [ "柏桐（1987- ），男，重庆人，博士，重庆邮电大学讲师，主要研究方向为微弱信号检测、无线体域网等。" ]
  [ "林金朝（1966- ），男，四川蓬溪人，博士，重庆邮电大学教授、博士生导师，主要研究方向为无线通信传输、无线体域网与信息处理等。" ]
  [ "李章勇（1976- ），男，重庆人，博士，重庆邮电大学教授，主要研究方向为无线体域网、人机耦合、图像处理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U21A20447;62171073;62311530103);重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX1523);重庆邮电大学博士研究生创新人才基金资助项目(BYJS202206)
- DOI：10.11959/j.issn.1000-436x.2024148
  中图分类号： TN915.04
- 收稿：2024-02-29，
  
  修回：2024-07-26，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
张政,谢鑫,柏桐等.信息年龄和能耗联合优化的无线体域网卸载和调度策略[J].通信学报,2024,45(09):92-100.

ZHANG Zheng,XIE Xin,BAI Tong,et al.Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN[J].Journal on Communications,2024,45(09):92-100.
张政,谢鑫,柏桐等.信息年龄和能耗联合优化的无线体域网卸载和调度策略[J].通信学报,2024,45(09):92-100. DOI： 10.11959/j.issn.1000-436x.2024148.

ZHANG Zheng,XIE Xin,BAI Tong,et al.Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN[J].Journal on Communications,2024,45(09):92-100. DOI： 10.11959/j.issn.1000-436x.2024148.

摘要

在无线体域网（WBAN）中，为保障生理数据的实时传输并降低节点能耗，提出了一种联合优化的卸载与调度策略。该策略通过选择在传感节点或Sink处理数据，最小化信息年龄（AoI）与能耗的加权和。为解决卸载和调度决策的强耦合问题，采用双层马尔可夫决策过程（MDP），并通过深度强化学习（DRL）应对维数灾难。仿真结果表明，DRL策略在性能上接近MDP，且在节点增加时，相较于RRG与EG策略，平均加权和分别降低约3.58%和24.9%，收敛速度约为MDP的2倍。

Abstract

Both real-time physiological data transmission and reduced energy consumption are critical to wireless body area network (WBAN). A joint optimization strategy for offloading and scheduling was proposed to minimize the weighted sum of age of information (AoI) and energy consumption by determining whether data should be processed at sensor nodes or the Sink. To handle the strong coupling between offloading and scheduling decisions

a two-layer Markov decision process (MDP) was used to approximate the optimal solution. A deep reinforcement learning (DRL) approach was introduced to address the dimensionality issue. Simulations show that the DRL strategy performs comparably to the MDP under various weight factors and frame lengths. Furthermore

as the number of sensor nodes increases

the DRL strategy reduces the weighted sum by 3.58% and 24.9% compared to RRG and EG strategies

respectively

and converges twice as fast as the MDP.

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Keywords

references

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