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Decentralized system identification using stochastic subspace identification for wireless sensor networks
Soojin Cho1, Jong-Woong Park2, Sung-Han Sim3
1School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 689-798, Korea. soojin@unist.ac.kr.
Sensors (Basel, Switzerland)
|April 10, 2015
Summary
Wireless sensor networks (WSNs) offer an economical solution for structural monitoring. This study proposes a decentralized system identification method for WSNs, validated on a 5-story building model.
Area of Science:
- Civil Engineering
- Structural Health Monitoring
- Wireless Sensor Networks
Background:
- Conventional wired structural monitoring systems are expensive to install and maintain.
- Wireless sensor networks (WSNs) provide a feasible and economical alternative for dense sensor deployment on large structures.
- WSNs necessitate decentralized computing due to wireless communication limitations.
Purpose of the Study:
- To propose a decentralized system identification method for WSNs.
- To implement Stochastic Subspace Identification (SSI)-based Decentralized System Identification (SDSI) within a WSN framework.
- To experimentally validate the performance of SDSI in a laboratory setting.
Main Methods:
- Utilizing Stochastic Subspace Identification (SSI) for system identification.
- Implementing SSI-based Decentralized System Identification (SDSI) on Imote2 wireless sensors.
- Deploying sensors in a hierarchical WSN architecture with tight scheduling.
- Conducting laboratory tests on a 5-story shear building model.
Main Results:
- Successful experimental verification of the proposed SDSI method.
- Demonstrated feasibility of decentralized system identification in WSNs for structural monitoring.
- Validated the performance of SDSI on a multi-story building model.
Conclusions:
- SDSI is a viable approach for decentralized system identification in WSNs.
- The proposed method overcomes limitations of centralized processing in WSNs.
- This technology enables cost-effective and efficient structural health monitoring.
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