A Novel Clustering Routing Algorithm for Bridge Wireless Sensor Networks Based on Spatial Model and Multicriteria Decision Making

The Bridge Health Monitoring System (BHMS) has been widely implemented and advocated globally to replace traditional bridge management and maintenance practices. Employing wireless sensors for collecting and transmitting bridge monitoring data via a self-organizing bridge wireless sensor network (BWSN) is crucial for ensuring the efficient operation and cost-effectiveness of BHMS. To validate and enhance the performance of BWSN, this study develops a bridge space model (SM-SSB) based on the Simple-supported Beam Bridge, and introduces a Critical-TOPSIS Clustering (CTC) routing algorithm on SM-SSB, employing multi-criteria decision-making (MCDM) for optimal cluster head (Optimal-CH) and relay node (Optimal-RN) selection efficiently. The CTC algorithm's main benefit is its holistic approach to considering factors such as the distance between nodes and the base station (BS), node densityC node residual energy, and the frequency of CH selection. Utilizing the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm enhanced by the CRITIC (CRiteria Importance Though Intercrieria Correlation) method (referred to as CRITIC-TOPSIS) sorting all nodes and then proceeds to quickly opt for the Optimal-CH and the Optimal-RN within the subspace of each bridge space model simultaneously. In the Matlab environment, the CTC algorithm's performance is benchmarked against LEACH, Fuzzy-TOPSIS, CTC, EEM-CRP, and AROA algorithms, examining metrics like dead node distribution, network life cycle, packet reception rate, and variance of energy consumption. Simulation outcomes indicate that the CTC algorithm achieves a balanced energy consumption across nodes, significantly boosting the energy efficiency of bridge sensor networks and fulfilling the BWSN routing algorithm's conditions.