A status updating policy based on age of information in wireless sensor network

ZHAO Yue-chao; YANG Tao; HU Bo

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A status updating policy based on age of information in wireless sensor network

更新时间：2026-09-04

- A status updating policy based on age of information in wireless sensor network
- Vol. 37, Issue 11, Pages: 29-34(2020)
- 作者机构：
  
  1. 复旦大学信息科学与工程学院,上海,200433
  2. 复旦大学信息科学与工程学院智慧网络与系统研究中心,上海,200433
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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赵悦超, 杨涛, 胡波. 无线传感器网络中基于信息年龄的状态更新策略[J]. 微电子学与计算机, 2020, 37(11): 29-34.

ZHAO Yue-chao, YANG Tao, HU Bo. A status updating policy based on age of information in wireless sensor network[J]. 2020, 37(11): 29-34.
赵悦超, 杨涛, 胡波. 无线传感器网络中基于信息年龄的状态更新策略[J]. 微电子学与计算机, 2020, 37(11): 29-34. DOI：

ZHAO Yue-chao, YANG Tao, HU Bo. A status updating policy based on age of information in wireless sensor network[J]. 2020, 37(11): 29-34. DOI：

摘要

针对具有能量收集的无线传感器网络(WSN)场景，传感器定期向基站发送状态更新信息，但受频谱资源限制，在给定时隙仅允许有限数量传感器进行发送。同时，发射功率受限于剩余能量的约束。围绕上述问题，提出了基于信息年龄(AoI)的马尔可夫决策过程(MDP)来构建状态的更新策略方案。模型刻画了WSN中频谱、能量及发送时隙三者之间的内在关系，同时，针对大规模传感网络下高计算复杂度问题，提出了基于Whittle Index的调度算法。该算法通过迭代得到了渐近最优性能，计算复杂度较之大规模MDP求解显著降低，兼顾了调度的实时性和准确性。

Abstract

For wireless sensor networks (WSN) scenarios with energy harvesting

sensors regularly send status update information to the base station

but limited by spectrum resources

only a small number of sensors are allowed to send in a given time slot. At the same time

the transmission power is limited by the remaining energy. In view of the above problems

we propose a state update strategy scheme based on the Markov Decision Process (MDP) of the Age of Information(AoI). The model depicts the intrinsic relationship between the spectrum

energy

and transmission time slots in the WSN. In order to solve the problem of high computational complexity in large-scale sensor networks

a scheduling algorithm based on Whittle Index is proposed. This algorithm obtains asymptotically optimal performance through iteration

and the computational complexity is significantly reduced compared to large-scale MDP solutions

taking into account the real-time and accuracy of scheduling.

关键词

Keywords

references

HSU Y P, MODIANO E, DUAN L J. Scheduling algorithms for minimizing age of information in wireless broadcast networks with random arrivals[J].IEEE Transactions on Mobile Computing, 2019.DOI: 10.1109/TMC.2019.2936199.

LENGS Y, YENER A. Age of information minimization for an energy harvesting cognitive radio[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(2): 427-439. DOI: 10.1109/TCCN.2019.2916097.

WHITTLEP. Restless bandits: activity allocation in a changing world[J]. Journal of Applied Probability, 1988, 25(A): 287-298. DOI: 10.2307/3214163.

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LIUH Y, LIU K Q, ZHAO Q. Learning and sharing in a changing world: non-Bayesian restless bandit with multiple players[C]//2011 Information Theory and Applications Workshop. La Jolla, IEEE, 2011. DOI: 10.1109/ITA.2011.5743588.

KAUL S, YATES R, GRUTESER M. Real-time status: how often should one update?[C]//2012 Proceedings IEEE INFOCOM. Orlando: IEEE, 2012. DOI: 10.1109/INFCOM.2012.6195689.

HSUY P, MODIANO E, DUAN L J. Age of information: design and analysis of optimal scheduling algorithms[C]//2017 IEEE International Symposium on Information Theory. Aachen, IEEE, 2017. DOI: 10.1109/ISIT.2017.8006590.

TALAK R, KARAMAN S, MODIANO E. Minimizing age-of-information in multi-hop wireless networks[C]//2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton). Monticello: IEEE, 2017. DOI: 10.1109/ALLERTON.2017.8262777.

KAUL S, GRUTESER M, RAI V, et al. Minimizing age of information in vehicular networks[C]//2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. Salt Lake City, IEEE. 2011. DOI: 10.1109/SAHCN.2011.5984917.

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