<![CDATA[Optimal placement of wireless structural health monitoring (SHM) sensors has to consider modal identification accuracy and power efficiency. In this study, two-tier wireless sensor network (WSN)-based SHM systems with clusters of sensors are investigated to overcome this difficulty. Each cluster contains a number of sensor nodes and a cluster head (CH). The lower tier is composed of sensors communicating with their associated CHs, and the upper tier is composed of the network of CHs. The first step is the optimal placement of sensors in the lower tier via the effective independence method by considering the modal identification accuracy. The second step is the optimal placement of CHs in the upper tier by considering power efficiency. The sensors in the lower tier are partitioned into clusters before determining the optimal locations of CHs in the upper tier. Two approaches, a constrained K-means clustering approach and a genetic algorithm (GA)-based clustering approach, are proposed in this study to cluster sensors in the lower tier by considering two constraints: (1) the maximum data transmission distance of each sensor; (2) the maximum number of sensors in each cluster. Given that each CH can only manage a limited number of sensors, these constraints should be considered in practice to avoid overload of CHs. The CHs in the upper tier are located at the centers of the clusters determined after clustering sensors in the lower tier. The two proposed approaches aim to construct a balanced size of clusters by minimizing the number of clusters (or CHs) and the total sum of the squared distance between each sensor and its associated CH under the two constraints. Accordingly, the energy consumption in each cluster is decreased and balanced, and the network lifetime is extended. A numerical example is studied to demonstrate the feasibility of using the two proposed clustering approaches for sensor clustering in WSN-based SHM systems. In this example, the performances of the two proposed clustering approaches and the K-means clustering method are also compared. The two proposed clustering approaches outperform the K-means clustering method in terms of constructing balanced size of clusters for a small number of clusters. Doi: 10.28991/CEJ-2022-08-12-01 Full Text: PDF]]>
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