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

Abstract

Abstract-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.

In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks

“…SA is a nature-based probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. Due to the distributed nature of WSNs, algorithms such as SA can be implemented in parallel fashion [17,21], in order to compute type-threshold functions in the network. Our second proposed scheme is based on computational intelligence, in particular, bioinspired mechanisms.…”

Networked computing in wireless sensor networks for structural health monitoring

“…Zimmerman et al [44] proposed a new parallel SA technique more suited to be implemented over a wireless sensing system for structural health monitoring as it reduces the communication required between processing nodes. This technique breaks up the traditionally serial SA tree (which is continuous across all temperature steps) into a set of smaller search trees, each of which corresponds to a given temperature step and begins with the global minimum values for the preceding temperature step.…”

Smart sensing, monitoring, and damage detection for civil infrastructures

“…Nagayama et al (2007) has realized the autonomous decentralized SHM system using iMote2 wireless smart sensor prototype incorporating with TinyOS (Levis et al, 2005), middleware services, and a flexibility-based damage detection algorithm. Zimmerman et al (2009) has proposed a decentralized modal analysis scheme using chain-like topology of the wireless smart sensor network with frequency-domain output-only modal analysis techniques, and it has been implemented to extract modal properties of a (Lee et al, 2002) historic theatre. Sim et al (2010) has developed decentralized data aggregation, which processes cross-correlation function or random decrement functions in several preset clusters to reduce the bandwidth of transmitted data, to determine the global modal properties with high fidelity.…”

“…They are all validated in a laboratoryscale truss structure. Different with the above approaches, Zimmerman et al (2009) has succeeded to update the FE model of a 3-story shear building structure using wireless smart sensor network with embedded parallel simulated annealing algorithm.…”