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

WANG Rui; LI Yahui; SUN Hui; ZHANG Youmin

doi:10.1631/FITEE.2000206

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

Special Issue on Distributed Filtering and Control of Complex Networks and Systems | Updated：2022-05-19

- Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 1, Pages: 51-67(2021)
- Affiliations：
  
  College of Information Engineering and Automation, Civil Aviation University of China, Tianjin 300300, China
  Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada
- Author bio：
  
  Hui SUN, E-mail: h-sun@cauc.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000206
  CLC： TP391;TP393
- Received：30 April 2020，
  
  Revised：2020-12-11，
  
  Published：2021-01
- Accepted：
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WANG Rui, LI Yahui, SUN Hui, et al. 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.
DOI：

WANG Rui, LI Yahui, SUN Hui, et al. 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. DOI： 10.1631/FITEE.2000206.

摘要

提出一种新的基于无线传感网络的事件触发卡尔曼一致性滤波（ET-KCF）算法。该算法基于信息新鲜度，通过计算采样信息的信息年龄（ageofinformation，AoI）度量信息的新鲜度。该算法集成传统的事件触发机制、信息新鲜度计算方法和卡尔曼一致性滤波（KCF）算法，可以更有效地估计飞机舱内的污染物浓度。该方法还考虑了数据包丢失和通信路径丢失对信息传输的影响，提出一种基于AoI约束的ET-KCF阈值选择方法，将每个数据包的AoI与系统最小平均AoI比较。该方法减少了对过期信息的传输，大大降低了网络能耗。最后，利用李雅普诺夫稳定性理论和矩阵理论证明了算法的收敛性。仿真结果表明，与现有KCF算法相比，该算法具有更好的容错性，与其他ET-KCF算法相比，其功耗更低。

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.

关键词

Keywords

references

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