Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN

ZHANG Zheng; XIE Xin; BAI Tong; LIN Jinzhao; LI Zhangyong

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

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Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN

Papers | 更新时间：2024-10-10

- Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN
- Journal on Communications Vol. 45, Issue 9, Pages: 92-100(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(U21A20447;62171073;62311530103);The Nature Science Foundation of Chongqing(CSTB2022NSCQ-MSX1523);The Innovative Talent Program for Doctoral Students of Chongqing University of Posts and Telecommunications(BYJS202206)
- DOI：10.11959/j.issn.1000-436x.2024148
  CLC： TN915.04
- Received：29 February 2024，
  
  Revised：2024-07-26，
  
  Published：25 September 2024
- 稿件说明：
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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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references

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