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.
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Project supported by the Civil Aviation Science and Technology Project (No. MHRD20150220), the Fundamental Research Funds for the Central Universities, China (No. 3122017003), and the Natural Sciences and Engineering Research Council of Canada
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Rui WANG and Yahui LI designed the research. Hui SUN and Youmin ZHANG guided the research. Rui WANG and Yahui LI drafted the manuscript. All authors contributed to the interpretation of the results and the revision of the paper.
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Rui WANG, Yahui LI, Hui SUN, and Youmin ZHANG declare that they have no conflict of interest.
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Wang, R., Li, Y., Sun, H. et al. Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network. Front Inform Technol Electron Eng 22, 51–67 (2021). https://doi.org/10.1631/FITEE.2000206
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DOI: https://doi.org/10.1631/FITEE.2000206
Key words
- Distributed Kalman consensus filter (KCF)
- Event-triggered mechanism
- Age of information (AoI)
- Stability analysis
- Energy optimization