Smart Structures and Systems

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Robust wireless sensor network configuration design for structural health monitoring with optimal information-energy tradeoff

  • Xiao-Han Hao (School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University) ;
  • Sin-Chi Kuok (State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau) ;
  • Ka-Veng Yuen (State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau)
  • Received : 2023.04.09
  • Accepted : 2024.08.23
  • Published : 2024.06.25
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Abstract

In this paper, a robust wireless sensor network configuration design method is proposed to develop the optimal configuration under the consideration of sensor failure and energy consumption. A malfunctioned sensor in a wireless sensor network may lead to data transmission failure of the entire sensing cluster, inducing severe deterioration in system identification performance. The proposed method determines a wireless sensor network configuration that is robust against sensor failure. By utilizing Bayesian inference, we introduce a robust indicator to evaluate the impact on estimation accuracy of sensor configurations with various malfunctioned sensors. Moreover, a network formation strategy is proposed to optimize the energy efficiency of the wireless sensor network configuration. Therefore, the resultant robust wireless sensor network configuration can operate with the minimum energy consumption while the measurement information of the sensor network with malfunctioned sensors can be guaranteed. The proposed method is illustrated by designing the robust wireless sensor network configurations of a truss model and a bridge model.

Keywords

Acknowledgement

This work is funded by the Science and Technology Development Fund, Macau SAR under Research Grant SKL-IOTSC(UM)-2021-2023 and 0094/2021/A2, the Research Committee of University of Macau under Research Grant MYRG2018-00048-AAO and SRG2021-00006-FST, and the Guangdong-Hong Kong-Macau Joint Laboratory Program under Grant 2020B1212030009. These generous supports are gratefully acknowledged.

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