Abstract: 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.

无线传感网络环境下基于信息新鲜度约束的事件触发卡尔曼一致性滤波算法

[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 36^th 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 8^th 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 46^th 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 46^th IEEE Conf on Decision and Control, p.5492-5498.

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

[16]Olfati-Saber R, Shamma JS, 2005. Consensus filters for sensor networks and distributed sensor fusion. Proc 44^th 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 15^th 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 19^th 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 57^th 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 33^rd 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.