A holistic approach to decentralized structural damage localization using wireless sensor networks

Authors:
Gregory Hackmann;Fei Sun;Nestor Castaneda;Chenyang Lu;Shirley Dyke
Affiliations:
Washington University in St. Louis, Department of Computer Science and Engineering, Campus Box 1045, One Brookings Drive, St. Louis, MO 63130, United States;Washington University in St. Louis, Department of Computer Science and Engineering, Campus Box 1045, One Brookings Drive, St. Louis, MO 63130, United States;Purdue University, Department of Civil Engineering, 550 Stadium Mall Drive, West Lafayette, IN 47907, United States;Washington University in St. Louis, Department of Computer Science and Engineering, Campus Box 1045, One Brookings Drive, St. Louis, MO 63130, United States;Purdue University, Department of Mechanical Engineering, 585 Purdue Mall, West Lafayette, IN 47907, United States
Venue:
Computer Communications
Year:
2012

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Abstract

Wireless sensor networks (WSNs) have become an increasingly compelling platform for Structural Health Monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the Damage Localization Assurance Criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by two orders of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system's ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 65.5% and energy consumption by 64.0% compared to a typical centralized solution.