Smart Structures and Systems

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Wireless sensor network design for large-scale infrastructures health monitoring with optimal information-lifespan tradeoff

  • Xiao-Han, Hao (State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau) ;
  • 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 : 2022.03.31
  • Accepted : 2022.09.15
  • Published : 2022.12.25
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Abstract

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

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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