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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

Distributed Structural Health MonitoringA Cyber-Physical System Approach

Chenyang Lu

Department of Computer Science and Engineering

outline
Outline
  • Distributed Structural Health Monitoring
  • ART: Adaptive Robust Topology Control
structural health monitoring shm
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
existing approaches
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
our approach
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

damage localization algorithm damage localization assurance criterion dlac
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

data flow analysis

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

data flow analysis1

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

evaluation truss
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

summary
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.

outline1
Outline
  • Distributed Structural Health Monitoring
  • ART: Adaptive Robust Topology Control
topology control
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
advantages of topology control

-15 dBm

-25 dBm

0 dBm

Advantages of Topology Control

Testbed Topology

is per link topology control beneficial

... 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

what is the impact of transmission power on contention

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?
can link stability be predicted

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

are link indicators robust indoors
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?
are link indicators robust indoors1

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

summary of insights
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
art adaptive and robust topology control
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.

acknowledgement
Acknowledgement
  • Computer Science: Greg Hackmann,Fei Sun, Octav Chipara
  • Structural Engineering: Nestor Castaneda, Shirley Dyke
for more information
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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