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Distributed Structural Health Monitoring A Cyber-Physical System Approach

Distributed Structural Health Monitoring A Cyber-Physical System Approach. Chenyang Lu Department of Computer Science and Engineering. Outline. Distributed Structural Health Monitoring ART: Adaptive Robust Topology Control. Structural Health Monitoring (SHM).

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Distributed Structural Health Monitoring A Cyber-Physical System Approach

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  1. Distributed Structural Health MonitoringA Cyber-Physical System Approach Chenyang Lu Department of Computer Science and Engineering

  2. Outline • Distributed Structural Health Monitoring • ART: Adaptive Robust Topology Control

  3. Structural Health Monitoring (SHM) • “More than 26%, or one in four, of the nation's bridges are either structurally deficient or functionally obsolete.” [ASCE 2009] • Detect and localize damages to structures • Wireless sensor networks can monitor at high temporal and spatial granularities • Key Challenges • Computationally intensive • Resource and energy constraints • Long-term monitoring

  4. Existing Approaches • Centralized approach: stream raw sensor data to base station for processing. • Example: Golden Gate Bridge monitoring project [UCB] • Nearly 1 day to collect enough data for one computation • Lifetime of 10 weeks w/4 x 6V lantern battery • Observations • Too much sensor data to stream to the base station • Damage detection is too complex to run entirely on sensors • Separate designs of SHM algorithm and sensor networks

  5. Our Approach • Distributed Architecture • Performs part of computation on sensor nodes • Send partial (smaller) results to base station • Base station completes computation • Cyber-Physical Co-design • Select an SHM algorithm that can be partitioned into components • Optimal partition of the SHM algorithm between sensor nodes and base station Raw Data Partial Results

  6. Damage Localization AlgorithmDamage Localization Assurance Criterion (DLAC) • Use vibration data to identify structure’s natural frequencies. • Match natural frequencies with models of healthy and damaged structures to localize damage. • Important: partition between sensors and the base station. • Minimize energy consumption • Subject to resource constraints Raw Data Partial Results

  7. D Integers (1) FFT D: # of samples P: # of natural freq. (D » P) D Floats (3a) Coefficient Extraction (2) Power Spectrum 5*P Floats D/2 Floats (3) Curve Fitting (3b) Equation Solving P Floats Healthy Model Damaged Location (4) DLAC Data Flow Analysis DLAC Algorithm

  8. 4096 bytes (1) FFT D: 2048 P: 5 Integer: 2 bytes Float: 4 bytes 8192 bytes (3a) Coefficient Extraction (2) Power Spectrum Effective compression ratio of 204:1 100 bytes 4096 bytes (3) Curve Fitting (3b) Equation Solving 20 bytes Healthy Model Damaged Location (4) DLAC Data Flow Analysis DLAC Algorithm

  9. Evaluation: Truss • 5.6 m steel truss structure at UIUC • 14 0.4m-long bays, sitting on four rigid supports • 11 Imote2s attached to frontal pane Damage correctly localized to third bay

  10. Energy Consumption Evaluation

  11. Energy Consumption Evaluation

  12. Summary • Cyber-physical co-design of a distributed SHM system • Reduces energy consumption by 71% • Implemented on iMote2 platform using <1% of memory • Effectively localized damage on two physical structures G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke, A Holistic Approach to Decentralized Structural Damage Localization Using Wireless Sensor Networks, RTSS 2008.

  13. Outline • Distributed Structural Health Monitoring • ART: Adaptive Robust Topology Control

  14. Topology Control • Goal: reduce transmission power while maintaining satisfactory link quality • But it’s challenging: • Links have irregular and probabilistic properties • Link quality can vary significantly over time • Human activity and multi-path effects in indoor environments • Most existing solutions are based on ideal assumptions • Contributions: • Insights from empirical study in an office building • ART: robust topology control designed based on insights

  15. -15 dBm -25 dBm 0 dBm Advantages of Topology Control Testbed Topology

  16. ... but have modest performance @ -5 dBm Insight 1: Transmission power should be set on a per-link basis to improve link quality and save energy. 3 of 4 links fail @ -10 dBm ... Is Per-Link Topology Control Beneficial? Impact of TX power on PRR

  17. Low signal strength High contention Insight 2:Robust topology control algorithms must avoid increasing contention under heavy network load. What is the Impact of Transmission Power on Contention?

  18. Is Dynamic Power Adaptation Necessary? Link 110 -> 139

  19. Insight 3: Robust topology control algorithms must adapt their transmission power in order to maintain good link quality and save energy. Can Link Stability Be Predicted? Long-Term Link Stability

  20. Are Link Indicators Robust Indoors? • Two instantaneous metrics are often proposed as indicators of link reliability: • Received Signal Strength Indicator (RSSI) • Link Quality Indicator (LQI) • Can you pick an RSSI or LQI threshold that predicts whether a link has high PRR or not?

  21. RSSI threshold = -85 dBm, PRR threshold = 0.9 4% false positive rate 62% false negative rate RSSI threshold = -84 dBm, PRR threshold = 0.9 66% false positive rate 6% false negative rate Insight 4: Instantaneous LQI and RSSI are not robust estimators of link quality in all environments. Are Link Indicators Robust Indoors? Links 106 -> 129 &104 -> 105

  22. Summary of Insights • Set transmission power on a per-link basis • Avoid increasing contention under heavy network load • Adapt transmission power online • LQI and RSSI are not robust estimators of link quality

  23. ARTAdaptive and Robust Topology control Designed based on insights from empirical study • Adjusts each link’s power individually • Detects and avoids contention at the sender • Tracks link qualities in a sliding window, adjusting transmission power at per-packet granularity • Does not rely on LQI or RSSI as link quality estimators • Is simple and lightweight by design • 392B of RAM, 1582B of ROM, often zero network overhead G. Hackmann, O. Chipara, and C. Lu, Robust Topology Control for Indoor Wireless Sensor Networks, SenSys 2008.

  24. Acknowledgement • Computer Science: Greg Hackmann,Fei Sun, Octav Chipara • Structural Engineering: Nestor Castaneda, Shirley Dyke

  25. For More Information • http://www.cse.wustl.edu/~lu/ • Structural Monitoring: http://www.cse.wustl.edu/~lu/shm/ • ART: http://www.cse.wustl.edu/~lu/upma.html

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