Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (4): 664-673    DOI: 10.3785/j.issn.1008-973X.2024.04.002
计算机与控制工程     
面向实时决策的物联网时效与失真性能研究
王艳芳(),王伟,董云泉*()
1. 南京信息工程大学 电子与信息工程学院，江苏 南京 210044
Study of timeliness and distortion performance for real-time decision making in IoT
Yanfang WANG(),Wei WANG,Yunquan DONG*()
1. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
 全文: PDF(1538 KB)   HTML
摘要：

在物联网中，传感器及时、准确地数据传输是决策单元获得有效数据决策（如估计、推断或控制）的保障. 为了减小估计失真，决策单元同时使用多个数据包并利用最佳线性无偏估计量（BLUE）进行联合估计. 以决策信息年龄（AuD）和均方误差（MSE）分别度量系统决策时刻信息的时效性和失真，提出2种决策方案，研究所提方案的信息时效性与失真性能. 在固定数量数据包的决策方案中，监测中心每收到固定数量数据包后进行一次估计；在固定时间间隔的决策方案中，监测中心每隔固定时间间隔进行一次估计. 通过调度系统的决策过程来最小化平均AuD和平均失真的加权和，实现系统的时效性和失真的关系平衡. 仿真结果表明，通过优化系统决策过程，所提方案可以实现在提升系统时效性的同时减小估计失真.
关键词： 物联网(IoT);  更新决策系统;  决策信息年龄(AuD);  估计失真;  决策调度    
Abstract:

The sensor's timely and accurately data transmission is a guarantee for the decision-making unit to obtain effective data for decision-making (e.g., estimation, inference, or control) in IoT. To reduce estimation distortion, the decision unit uses multiple packets concurrently for joint estimation by using the best linear unbiased estimator (BLUE). Age upon decisions (AuD) and mean-squared-error (MSE) were introduced as metrics to measure the timeliness and the distortion of the information at the decision moments of the system, respectively. Two decision-making strategies were proposed, and the information timeliness and the distortion performance of the proposed strategies were investigated. In the strategy of using a fixed number of packets for decision making, the monitoring center performed an estimation after per fixed number of packets were received. In the strategy of using fixed time intervals for decision making, the monitoring center made an estimation at fixed intervals. The relationship between the system timeliness and the distortion was balanced by scheduling the decision process of the system to minimize the weighted sum of average AuD and average distortion. Simulation results show that the proposed strategies can improve the system timeliness and reduce the distortion performance by scheduling the decision-making process of the system.
Key words: internet of things (IoT)    update-and-decide system    age upon decisions (AuD)    estimation distortion    decision scheduling
收稿日期: 2023-08-29 出版日期: 2024-03-27
CLC:  TN 92  
基金资助: 国家自然科学基金资助项目（62071237）.
通讯作者: 董云泉     E-mail: 20211218041@nuist.edu.cn;yunquandong@nuist.edu.cn
作者简介: 王艳芳（1998—），女，硕士生，从事无线网络通信研究. orcid.org/0009-0000-9512-4791. E-mail：20211218041@nuist.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
王艳芳
王伟
董云泉

引用本文:

王艳芳,王伟,董云泉. 面向实时决策的物联网时效与失真性能研究[J]. 浙江大学学报(工学版), 2024, 58(4): 664-673.

Yanfang WANG,Wei WANG,Yunquan DONG. Study of timeliness and distortion performance for real-time decision making in IoT. Journal of ZheJiang University (Engineering Science), 2024, 58(4): 664-673.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.04.002    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I4/664

图 1  物联网更新决策系统
图 2  传感模型
图 3  决策信息年龄和信息年龄的样本路径示例
图 4  数据包的到达时间间隔与系统时间
图 5  系统平均决策信息年龄与到达率的关系
图 6  系统平均决策信息年龄与数据包个数（或决策间隔）的关系
图 7  数据包个数（或决策间隔）对平均失真的影响
图 8  平均决策信息年龄和平均失真加权和与权重的变化关系
图 9  决策过程与权重对平均决策信息年龄和平均失真加权和的影响
1 杨荣悦, 张鹏洲, 宋卿 基于5G技术的智能车联网研究与展望[J]. 电信科学, 2020, 36 (5): 106- 114
YANG Rongyue, ZHANG Pengzhou, SONG Qing Research and prospect of intelligent internet of vehicles based on 5G technology[J]. Telecommunications Science, 2020, 36 (5): 106- 114
2 JU Z, ZHANG H, LI X, et al A survey on attack detection and resilience for connected and automated vehicles: from vehicle dynamics and control perspective[J]. IEEE Transactions on Intelligent Vehicles, 2022, 7 (4): 815- 837
doi: 10.1109/TIV.2022.3186897
3 HE Q, DAN G, FODOR V. Minimizing age of correlated information for wireless camera networks [C]// IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Honolulu: IEEE, 2018: 547–552.
4 LI B, FEI Z, ZHANG Y UAV communications for 5G and beyond: recent advances and future trends[J]. IEEE Internet of Things Journal, 2019, 6 (2): 2241- 2263
doi: 10.1109/JIOT.2018.2887086
5 BURHANUDDIN L A B, LIU X, DENG Y, et al QoE optimization for live video streaming in UAV-to-UAV communications via deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2022, 71 (5): 5358- 5370
doi: 10.1109/TVT.2022.3152146
6 HABIBZADEH H, DINESH K, SHISHVAN O R, et al A survey of healthcare internet-of-things (HIoT): a clinical perspective[J]. IEEE Internet Things Journal, 2020, 7 (1): 53- 71
doi: 10.1109/JIOT.2019.2946359
7 SONG H, GAO S, LI Y, et al Train-centric communication based autonomous train control system[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8 (1): 721- 731
doi: 10.1109/TIV.2022.3192476
8 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: 350–358.
9 KAUL S, YATES R, GRUTESER M. Real-time status: how often should one update? [C]// 2012 Proceedings IEEE INFOCOM . Orlando: IEEE, 2012: 2731–2735.
10 YATES R D, SUN Y, BROWN D R, et al Age of information: an introduction and survey[J]. IEEE Journal on Selected Areas in Communications, 2021, 39 (5): 1183- 1210
doi: 10.1109/JSAC.2021.3065072
11 WANG Q, CHEN H, ZHAO C, et al Optimizing information freshness via multiuser scheduling with adaptive NOMA/OMA[J]. IEEE Transactions on Wireless Communications, 2022, 21 (3): 1766- 1778
doi: 10.1109/TWC.2021.3106778
12 CHIARIOTTI F, HOLM J, KALØR A E, et al Query age of information: freshness in pull-based communication[J]. IEEE Transactions on Communications, 2022, 70 (3): 1606- 1622
doi: 10.1109/TCOMM.2022.3141786
13 DONG Y, CHEN Z, LIU S, et al. Age of information upon decisions [C]// 2018 IEEE 39th Sarnoff Symposium . Newark: IEEE, 2018: 1–5.
14 DONG Y, FAN P. Age upon decisions with general arrivals [C]// 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference . New York: IEEE, 2018: 825–829.
15 DONG Y, CHEN Z, LIU S, et al Age-upon-decisions minimizing scheduling in Internet of Things: to be random or to be deterministic?[J]. IEEE Internet of Things Journal, 2020, 7 (2): 1081- 1097
doi: 10.1109/JIOT.2019.2950054
16 BAO Z, DONG Y, CHEN Z, et al Age-optimal service and decision processes in Internet of Things[J]. IEEE Internet of Things Journal, 2021, 8 (4): 2826- 2841
doi: 10.1109/JIOT.2020.3020875
17 CHEN S, ZHANG T, CHEN Z, et al Minimizing age-upon-decisions in bufferless system: service scheduling and decision interval[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (1): 1017- 1031
doi: 10.1109/TVT.2022.3202790
18 DONG Y, FAN P, LETAIEF K B Energy harvesting powered sensing in IoT: timeliness versus distortion[J]. IEEE Internet of Things Journal, 2020, 7 (11): 10897- 10911
doi: 10.1109/JIOT.2020.2990715
19 DONG Y Distributed sensing with orthogonal multiple access: to code or not to code?[J]. IEEE Transactions on Signal Processing, 2020, 68: 1315- 1330
doi: 10.1109/TSP.2020.2971203
20 ORNEE T Z, SUN Y. Sampling for remote estimation through queues: age of information and beyond [C]// International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks . Avignon: IEEE, 2019: 1–8.
21 BASTOPCU M, ULUKUS S. Age of information for updates with distortion [C]// 2019 IEEE Information Theory Workshop (ITW) . Visby: IEEE, 2019: 1–5.
22 QIAO L, ZHOU Y Timely split inference in wireless networks: an accuracy-freshness tradeoff[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (12): 16817- 16822
doi: 10.1109/TVT.2023.3294494
23 HUANG Y, ZHANG W. Research on the methods of data mining based on the edge computing for the IoT [C]// IEEE International Conference on Integrated Circuits and Communication Systems . Raichur: IEEE, 2023: 1–6.
24 CUI S, XIAO J J, GOLDSMITH A J, et al Estimation diversity and energy efficiency in distributed sensing[J]. IEEE Transactions on Signal Processing, 2007, 55 (9): 4683- 4695
doi: 10.1109/TSP.2007.896019
[1] 胡浪涛,杨瑞,黄崇文,刘全金,吴磊,谭镇坤. RIS辅助双功能雷达与隐蔽通信系统性能优化[J]. 浙江大学学报(工学版), 2023, 57(12): 2533-2543.
[2] 周欣彤,肖琨. 综合上下行链路的无线能量收集协作网络资源分配[J]. 浙江大学学报(工学版), 2023, 57(12): 2544-2552.
[3] 刘翀赫,余官定,刘胜利. 基于无线D2D网络的分层联邦学习[J]. 浙江大学学报(工学版), 2023, 57(5): 892-899.
[4] 陈俊杰,李洪均,朱晓军. 采用Benders分解的5G核心网用户面动态部署算法[J]. 浙江大学学报(工学版), 2023, 57(3): 625-631.
[5] 刘蕾,李保罡,杨志. 基于安全状态更新的信息年龄-能量权衡[J]. 浙江大学学报(工学版), 2023, 57(2): 426-436.
[6] 陈扬钊,袁伟娜. 深度学习辅助上行免调度NOMA多用户检测方法[J]. 浙江大学学报(工学版), 2022, 56(4): 816-822.
[7] 孙晨,吴哲奕,袁建涛. 电力物联网中节能的免许可D2D接入算法设计[J]. 浙江大学学报(工学版), 2020, 54(10): 1867-1873.
[8] 汪周飞,袁伟娜. 基于深度学习的多载波系统信道估计与检测[J]. 浙江大学学报(工学版), 2020, 54(4): 732-738.
[9] 刘春生,单洪,王斌,黄郡. 基于Bregman散度的无线传感器网络定位[J]. 浙江大学学报(工学版), 2019, 53(8): 1525-1535.
[10] 张雪芹,张立,顾春华. 社交网络中社会工程学威胁定量评估[J]. 浙江大学学报(工学版), 2019, 53(5): 837-842.
[11] 郑晓雁,陈惠芳,谢磊. 感知数据错误化攻击下分布式检测的性能分析[J]. 浙江大学学报(工学版), 2019, 53(3): 563-570.
[12] 司恩波, 王晶, 靳其兵, 周靖林. 工业无线网络链路选择与时隙分配的同步优化[J]. 浙江大学学报(工学版), 2016, 50(6): 1203-1213.
[13] 吴端坡, 金心宇, 蒋路茸, 欧阳博. 高速铁路网络环境下掉话率分析[J]. 浙江大学学报(工学版), 2015, 49(4): 705-710.
[14] 董利达,黄聪,管林波. 基于双树结构的无线HART调度策略[J]. J4, 2014, 48(3): 391-397.
[15] 李训文,金文光,邵琦青,高伟. 基于PLC网络的改进AODV备份路由协议[J]. J4, 2013, 47(7): 1218-1224.