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 2

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 3

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 4

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 5 Raw Data Partial Results

Damage Localization Algorithm Damage 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 6 Raw Data Partial Results

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

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

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

Energy Consumption Evaluation 10

Energy Consumption Evaluation 11

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 12 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.

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

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 14

Advantages of Topology ControlTestbed Topology 0 dBm -15 dBm -25 dBm 15

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

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

Is Dynamic Power Adaptation Necessary?Link 110 ->

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

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? 20

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

Summary of Insights 1.Set transmission power on a per-link basis 2.Avoid increasing contention under heavy network load 3.Adapt transmission power online 4.LQI and RSSI are not robust estimators of link quality 22

ART Adaptive and Robust Topology control Designed based on insights from empirical study 1.Adjusts each link’s power individually 2.Detects and avoids contention at the sender 3.Tracks link qualities in a sliding window, adjusting transmission power at per-packet granularity 4.Does not rely on LQI or RSSI as link quality estimators 5.Is simple and lightweight by design  392B of RAM, 1582B of ROM, often zero network overhead 23 G. Hackmann, O. Chipara, and C. Lu, Robust Topology Control for Indoor Wireless Sensor Networks, SenSys 2008.

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

For More Information   Structural Monitoring:  ART: 25