Full Text:   <4601>

Summary:  <1636>

CLC number: TP391; TP393

On-line Access: 2021-01-11

Received: 2020-04-30

Revision Accepted: 2020-08-30

Crosschecked: 2020-12-11

Cited: 0

Clicked: 5262

Citations:  Bibtex RefMan EndNote GB/T7714


Rui Wang


Hui Sun


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.1 P.51-67


Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Author(s):  Rui Wang, Yahui Li, Hui Sun, Youmin Zhang

Affiliation(s):  College of Information Engineering and Automation, Civil Aviation University of China, Tianjin 300300, China; more

Corresponding email(s):   h-sun@cauc.edu.cn

Key Words:  Distributed Kalman consensus filter (KCF), Event-triggered mechanism, Age of information (AoI), Stability analysis, Energy optimization

Rui Wang, Yahui Li, Hui Sun, Youmin Zhang. Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 51-67.

@article{title="Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network",
author="Rui Wang, Yahui Li, Hui Sun, Youmin Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network
%A Rui Wang
%A Yahui Li
%A Hui Sun
%A Youmin Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 1
%P 51-67
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000206

T1 - Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network
A1 - Rui Wang
A1 - Yahui Li
A1 - Hui Sun
A1 - Youmin Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 1
SP - 51
EP - 67
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000206

This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft’s loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Amini A, Asif A, Mohammadi A, 2018. An event-triggered average consensus algorithm with performance guarantees for distributed sensor networks. Proc IEEE Int Conf on Acoustics, Speech and Signal Processing, p.3409-3413.

[2]Fan S, Yan HC, Zhang H, et al., 2017. Prediction consensus-based distributed Kalman filtering with packet loss. Proc 36th Chinese Control Conf, p.7950-7955.

[3]Farag AM, 2018. Thermal comfort investigation for commercial aircraft cabin by using CFD. Proc IEEE Aerospace Conf, p.1-10.

[4]Farazi S, Klein AG, Brown DR, 2018. Age of information in energy harvesting status update systems: when to preempt in service? Proc IEEE Int Symp on Information Theory, p.2436-2440.

[5]Godsil CD, Royle G, 2001. Algebraic Graph Theory. Springer, New York, USA.

[6]Heinzelman WB, Chandrakasan AP, Balakrishnan H, 2002. An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun, 1(4):660-670.

[7]Hudhajanto RP, Fahmi N, Prayitno E, et al., 2018. Real-time monitoring for environmental through wireless sensor network technology. Proc Int Conf on Applied Engineering, p.1-5.

[8]Kaul S, Gruteser M, Rai V, et al., 2011. Minimizing age of information in vehicular networks. Proc 8th Annual IEEE Communications Society Conf on Sensor, Mesh and ad hoc Communications and Networks, p.350-358.

[9]Kaul SK, Yates RD, Gruteser M, 2012. Status updates through queues. Proc 46th Annual Conf on Information Sciences and Systems, p.1-6.

[10]Li F, Liu JJ, Ren JL, et al., 2016. Numerical investigation of airborne contaminant transport under different vortex structures in the aircraft cabin. Int J Heat Mass Transf, 96:287-295.

[11]Li WL, Jia YM, Du JP, 2016. Event-triggered Kalman consensus filter over sensor networks. IET Contr Theory Appl, 10(1):103-110.

[12]Liu XD, Li LY, Li Z, et al., 2017. Stochastic stability condition for the extended Kalman filter with intermittent observations. IEEE Trans Circ Syst II Expr Briefs, 64(3):334-338.

[13]Liu YG, Xu BG, Shi BH, 2013. Kalman filtering for stochastic systems with consecutive packet losses and measurement time delays. Contr Theory Appl, 30(7):898-908.

[14]Olfati-Saber R, 2007. Distributed Kalman filtering for sensor networks. Proc 46th IEEE Conf on Decision and Control, p.5492-5498.

[15]Olfati-Saber R, 2009. Kalman-consensus filter: optimality, stability, and performance. Proc 48th IEEE Conf on Decision and Control held jointly with 28th Chinese Control Conf, p.7036-7042.

[16]Olfati-Saber R, Shamma JS, 2005. Consensus filters for sensor networks and distributed sensor fusion. Proc 44th IEEE Conf on Decision and Control, p.6698-6703.

[17]Paul A, Kamwa I, Jóos G, 2018. Centralized dynamic state estimation using a federation of extended Kalman filters with intermittent PMU data from generator terminals. IEEE Trans Power Syst, 33(6):6109-6119.

[18]Saha S, Majumdar A, 2017. Data centre temperature monitoring with ESP8266 based wireless sensor network and cloud based dashboard with real time alert system. Proc Devices for Integrated Circuit, p.307-310.

[19]Shi J, Qi GQ, Li YY, et al., 2018. Stochastic convergence analysis of cubature Kalman filter with intermittent observations. J Syst Eng Electron, 29(4):823-833.

[20]Song WH, Wang JN, Wang CY, et al., 2018. Event-triggered cooperative unscented Kalman filtering. Proc 15th Int Conf on Control, Automation, Robotics and Vision, p.1004-1009.

[21]Steele JM, 2004. The Cauchy-Schwarz Master Class: an Introduction to the Art of Mathematical Inequalities. Cambridge University Press, New York, USA.

[22]Sun SL, Tian T, Lin HL, 2016. Optimal linear estimators for systems with finite-step correlated noises and packet dropout compensations. IEEE Trans Signal Process, 64(21):5672-5681.

[23]Sun Y, Cyr B, 2019. Sampling for data freshness optimization: non-linear age functions. J Commun Netw, 21(3):204-219.

[24]Talak R, Karaman S, Modiano E, 2018. Distributed scheduling algorithms for optimizing information freshness in wireless networks. Proc IEEE 19th Int Workshop on Signal Processing Advances in Wireless Communications, p.1-5.

[25]Tang HY, Wang JT, Song LQ, et al., 2019. Scheduling to minimize age of information in multi-state time-varying networks with power constraints. Proc 57th Annual Allerton Conf on Communication, Control, and Computing, p.1198-1205.

[26]Wang R, Li YX, Sun H, et al., 2017. Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter. ISA Trans, 71:112-120.

[27]Wang R, Wang XY, Sun H, et al., 2019. Analysis of estimator and energy consumption with multiple faults over the distributed integrated WSN. Int J Model Ident Contr, 32(2):154-168.

[28]Yu ZQ, Zhang YM, Liu ZX, et al., 2019a. Distributed adaptive fractional-order fault-tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks. IET Contr Theory Appl, 13(17):2917-2929.

[29]Yu ZQ, Qu YH, Zhang YM, 2019b. Distributed fault-tolerant cooperative control for multi-UAVs under actuator fault and input saturation. IEEE Trans Contr Syst Technol, 27(6):2417-2429.

[30]Yu ZQ, Liu ZX, Zhang YM, et al., 2020. Distributed finite-time fault-tolerant containment control for multiple unmanned aerial vehicles. IEEE Trans Neur Netw Learn Syst, 31(6):2077-2091.

[31]Zhang LL, Zhang Y, 2018. A distributed energy-efficient target tracking algorithm based on event-triggered strategy for sensor networks. Proc 33rd Youth Academic Annual Conf of Chinese Association of Automation, p.12-17.

[32]Zhou ZM, Fu CC, Xue CJ, et al., 2020. Energy-constrained data freshness optimization in self-powered networked embedded systems. IEEE Trans Comput-Aided Des Integr Circ Syst, 30(10):2293-2306.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE