Distributed Structural Health MonitoringA Cyber-Physical System Approach

Chenyang LuDepartment of Computer Science and Engineering

Outline

Distributed Structural Health Monitoring

ART: Adaptive Robust Topology Control

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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

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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

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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

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Raw D

ata

PartialResults

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

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Raw D

ata

PartialResults

(1) FFT

(2) Power Spectrum

(3) Curve Fitting

(4) DLAC

D Integers

Healthy Model Damaged Location

D Floats

D/2 Floats

P Floats

D: # of samplesP: # of natural freq.(D » P)

Data Flow Analysis DLAC Algorithm

(3a) Coefficient Extraction

(3b) Equation Solving

5*PFloats

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Data Flow Analysis DLAC Algorithm

4096 bytes

(4) DLAC

(1) FFT

(2) Power Spectrum

(3) Curve Fitting

Healthy Model Damaged Location

8192 bytes

4096 bytes

D: 2048P: 5

Integer: 2 bytesFloat: 4 bytes

(3a) Coefficient Extraction

(3b) Equation Solving

100bytes

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20 bytes

Effective compression ratio of 204:1

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

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Energy Consumption

Decentralized

Centralized

0 0.05 0.1 0.15 0.2 0.25

SamplingComputationCommunication

Energy consumption (mAh)

Evaluation

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Energy Consumption

Raw Data_x000d_Collection

FFT

Power_x000d_Spectrum

Coefficient_x000d_Extraction

Equation_x000d_Solving

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

SamplingComputationCommunication

Energy Consumption (mAh)

Evaluation

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

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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

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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

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Advantages of Topology Control Testbed Topology

0 dBm-15 dBm-25 dBm

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Is Per-Link Topology Control Beneficial? Impact of TX power on PRR

3 of 4 links fail @ -10 dBm ...

... but have modest performance @ -5 dBmInsight 1: Transmission power should be set on a per-

link basis to improve link quality and save energy.

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What is the Impact of Transmission Power on Contention?

Highcontention

Low signal strength

Insight 2: Robust topology control algorithms must avoid increasing contention under heavy network load.

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Is Dynamic Power Adaptation Necessary? Link 110 -> 139

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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.

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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?

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Are Link Indicators Robust Indoors? Links 106 -> 129 &104 -> 105

RSSI threshold = -85 dBm, PRR threshold = 0.9

4% false positive rate62% false negative rate

RSSI threshold = -84 dBm, PRR threshold = 0.9

66% false positive rate6% false negative rateInsight 4: Instantaneous LQI and RSSI are not robust

estimators of link quality in all environments.

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Summary of Insights

1. Set transmission power on a per-link basis2. Avoid increasing contention under heavy network load3. Adapt transmission power online4. LQI and RSSI are not robust estimators of link quality

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ARTAdaptive and Robust Topology control

Designed based on insights from empirical study1. Adjusts each link’s power individually 2. Detects and avoids contention at the sender3. Tracks link qualities in a sliding window, adjusting transmission power

at per-packet granularity4. Does not rely on LQI or RSSI as link quality estimators5. Is simple and lightweight by design

392B of RAM, 1582B of ROM, often zero network overhead

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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

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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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