Energy-aware wireless sensor placement in structural health monitoring using hybrid discrete firefly algorithm

2014 ◽  
Vol 22 (4) ◽  
pp. 648-666 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Ting-Hua Yi ◽  
Huan Zhang ◽  
Hong-Nan Li
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Firefly Algorithm ◽  
Sensor Placement ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Structural Health ◽  
Discrete Firefly Algorithm

scholarly journals A Comparative Study of Genetic and Firefly Algorithms for Sensor Placement in Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Ting-Hua Yi ◽  
Huan Zhang ◽  
Hong-Nan Li
Keyword(s):  
Structural Health Monitoring ◽  
Comparative Study ◽  
Health Monitoring ◽  
Firefly Algorithm ◽  
Hamming Distance ◽  
Optimization Problems ◽  
High Reliability ◽  
Sensor Placement ◽  
Structural Health ◽  
Wide Range

Optimal sensor placement (OSP) is an important task during the implementation of sophisticated structural health monitoring (SHM) systems for large-scale structures. In this paper, a comparative study between the genetic algorithm (GA) and the firefly algorithm (FA) in solving the OSP problem is conducted. To overcome the drawback related to the inapplicability of the FA in optimization problems with discrete variables, some improvements are proposed, including the one-dimensional binary coding system, the Hamming distance between any two fireflies, and the semioriented movement scheme; also, a simple discrete firefly algorithm (SDFA) is developed. The capabilities of the SDFA and the GA in finding the optimal sensor locations are evaluated using two disparate objective functions in a numerical example with a long-span benchmark cable-stayed bridge. The results show that the developed SDFA can find the optimal sensor configuration with high reliability. The comparative study indicates that the SDFA outperforms the GA in terms of algorithm complexity, computational efficiency, and result quality. The optimization mechanism of the FA has the potential to be extended to a wide range of optimization problems.

Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 907-918 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Mei-Xi Xie ◽  
Ting-Hua Yi ◽  
Hong-Nan Li
Keyword(s):  
Wireless Sensor Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Firefly Algorithm ◽  
Wireless Sensor ◽  
Network Configuration ◽  
Automatic Learning ◽  
Structural Health ◽  
Health Monitoring Systems

Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.

Magneto-inductive waveguide as a passive wireless sensor net for structural health monitoring

2010 ◽  
Author(s):  
Ye Chen ◽  
Shasi Munukutla ◽  
Praveenkumar Pasupathy ◽  
Dean P. Neikirk ◽  
Sharon L. Wood
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Wireless Sensor ◽  
Structural Health
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