Energy‐aware versatile wireless sensor network configuration for structural health monitoring

Abstract

In this paper, a sensor network configuration optimization approach is proposed to design informative and energy-efficient wireless sensor networks. In particular, the design of cluster-based versatile wireless sensor networks for structural health monitoring is considered. In contrast to conventional cluster-based wireless sensor placement methods, a clustering optimization algorithm is proposed to determine the optimal locations of the cluster heads and the base station to enhance the energy efficiency of the network. The proposed approach determines the optimal wireless sensor network configuration that achieves the required estimation accuracy with minimal energy cost. Moreover, the proposed approach utilizes a holistic measure to assess the overall performance of multitype sensing devices. Furthermore, by implementing a genetic algorithm (GA) strategy, the proposed approach is computationally efficient and widely applicable for large-scale civil engineering infrastructures. To demonstrate the performance of the proposed approach, the wireless sensor network configuration design of a bridge model and a space truss model is presented.

The Current Development of Structural Health Monitoring for Bridges: A Review

“…The current consumption before and after activating the system on a commercial wireless platform verified the ultra-low power consumption of the system. Considering WSNs with multi-type sensor clusters, Hao et al [46] developed a clusterbased network optimization algorithm to improve the energy utilization efficiency of the network and extend the network lifetime. On this basis, they obtained the coefficient of variation of the estimated parameters based on Bayesian inference, which can be used as a global measure to evaluate the accuracy of the sensor network.…”

“…On this basis, they obtained the coefficient of variation of the estimated parameters based on Bayesian inference, which can be used as a global measure to evaluate the accuracy of the sensor network. In another study, Hao et al [47] added the genetic algorithm (GA) strategy based on the previous research to improve the computational efficiency of this method. The performance of the proposed method was verified in two WSNs.…”

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

“…• Electrochemical sensors: the three main types of these sensors are (1) potentiometric sensors [219,220] (2) amperometric sensors [221,222] and (3) conductometric sensors [223,224]. • Wireless monitoring via wireless sensors: In recent decades, the development of wireless sensor technologies has been based on advancements in microelectromechanical systems (MEMS) technology, digital electronics, and wireless communications [225][226][227]. The two main subsystems of this system are (1) Portable Inspection and Maintenance Strategy (PIMS) [228,229] and (2) Portable Data Acquisition Strategy (PDAs) [230].…”

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

“…Additionally, strategies for optimizing relay node placement, considering energy conservation and monitoring efficiency, have been explored in separate studies [91,92]. The utilization of split clusters within sensor networks has shown promise for SHM [93]. Moreover, comprehensive network optimization, with an emphasis on minimizing energy consumption, incorporates power control, packet transmission with error-correcting codes, and topology control [94,95].…”

“…promise for SHM [93]. Moreover, comprehensive network optimization, wit on minimizing energy consumption, incorporates power control, packe with error-correcting codes, and topology control [94,95].…”