A Parallel Simulated Annealing Architecture for Model Updating in Wireless Sensor Networks

• Published in: IEEE sensors journal Vol. 9; no. 11; pp. 1503 - 1510
• Main Authors: Zimmerman, A.T., Lynch, J.P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Communication cables; Computation; Computer networks; Data processing; In-network data processing; Large-scale systems; Mathematical models; Model updating; Monitoring; Monitoring systems; Networks; parallel algorithms; Predictive models; Sensor phenomena and characterization; Sensors; Simulated annealing; structural monitoring; Studies; Wireless communication; Wireless networks; Wireless sensor networks; wireless sensors
• DOI: 10.1109/JSEN.2009.2019323
• ISSN: 1530-437X

In recent years, wireless sensing technologies have provided a much sought-after alternative to expensive cabled monitoring systems. Wireless sensing networks forego the high data transfer rates associated with cabled sensors in exchange for low-cost and low-power communication between a large number of sensing devices, each of which features embedded data processing capabilities. As such, a new paradigm in large-scale data processing has emerged; one where communication bandwidth is somewhat limited but distributed data processing centers are abundant. By taking advantage of this grid of computational resources, data processing tasks once performed independently by a central processing unit can now be parallelized, automated, and carried out within a wireless sensor network. By utilizing the intelligent organization and self-healing properties of many wireless networks, an extremely scalable multiprocessor computational framework can be developed to perform advanced engineering analyses. In this study, a novel parallelization of the simulated annealing stochastic search algorithm is presented and used to update structural models by comparing model predictions to experimental results. The resulting distributed model updating algorithm is validated within a network of wireless sensors by identifying the mass, stiffness, and damping properties of a three-story steel structure subjected to seismic base motion.