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WTC 2018, July 16-17, Guangzhou University
1Towards Trustworthy Data for SHM
Towards Trustworthy Data for Structural Health Monitoring
Presenter:
Md Zakirul Alam Bhuiyan, PhD
Assistant Professor
Department of Computer and Information Sciences, Fordham University, NYC
Visiting Professor, Guangzhou UniversityEmail: [email protected], [email protected]
http://storm.cis.fordham.edu/~bhuiyan/, https://sites.google.com/site/zakirulalam/
WTC 2018, July 16-17, Guangzhou University
WTC 2018, July 16-17, Guangzhou University
2Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
3Towards Trustworthy Data for SHM
What is SHM System?
Disease/Damage: An event that is due to a significant change in the structure
http://www.owi-lab.be/content/state-art-and-new-developments-field-structural-health-monitoring
• Patient Health Monitoring <> Structural Health Monitoring
WTC 2018, July 16-17, Guangzhou University
4Towards Trustworthy Data for SHM
http://www.owi-lab.be/content/state-art-and-new-developments-field-structural-health-monitoring
What is SHM System?
• Patient Health Monitoring <> Structural Health Monitoring
WTC 2018, July 16-17, Guangzhou University
5Towards Trustworthy Data for SHM
• A CPS, an IoT system, an application of Smartcity• Aircraft, building, bridge, nuclear plants, dams, wind turbine,
ship, vehicle, etc
Nov. 18,
2015May 17,
2012
• Smart sensor nodes• Sensors
• CPU
• Wireless transceivers
5
What is SHM System?
WTC 2018, July 16-17, Guangzhou University
6Towards Trustworthy Data for SHM
What is SHM System?
WTC 2018, July 16-17, Guangzhou University
7Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
8Towards Trustworthy Data for SHM
Data Collection in SHM Systems
SHM requirements Wired System Wireless System
Low cost Equipment Expensive Low-cost
Cabling Long cables No cables
Deployment time Months ~ years Hours ~ days
High spatial density X0~X00 X00~X000
Sampling Fast on command/event triggered
Delay <μs Seconds ~ minutes(due to the wireless link)
High frequency and synchronized
Frequency >10KHz Sync error <1μs
Frequency < 10KHzLarge sync error
Fast and reliable data delivery 100% data delivery, instant delivery
Data can get lost, single hop bandwidth < 100kbps
Reliable and accurate damage detection
Benefit from centralized algorithms, but constraint by low density & inflexibity
Constraint by limited computation power, but benefits from high density and flexible
WTC 2018, July 16-17, Guangzhou University
9Towards Trustworthy Data for SHM
Data Collection in SHM Systems
WTC 2018, July 16-17, Guangzhou University
10Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
11Towards Trustworthy Data for SHM
• Integrity problem• Security attacks
• Data compromised• Collusion attack, malicious attack, false data injection
• Intentionally device configuration >> irregular data• Some sensors constantly provide truthful data while
• Others may generate biased, compromised, or even fake data
• Service made unavailable >> irregular data
Challenges with Trustworthy Data Collection in SHM
WTC 2018, July 16-17, Guangzhou University
12Towards Trustworthy Data for SHM
• Integrity problem• Data source integrity
• Is the source of the data really what is supposed to be?
• Malicious proprietors• Data manipulation attack
(one data point is replaced with another data point)
• Sensor faults• Hardware/software fault
• Other• Misuse
• Human mistakes and naivety
Challenges with Trustworthy Data Collection in SHM
WTC 2018, July 16-17, Guangzhou University
13Towards Trustworthy Data for SHM
• Data can be compromised: • At the acquisition
• After the acquisition
• At the transmission
• During transmission
• After transmission, and
• Before aggregation
Challenges with Trustworthy Data Collection in SHM
WTC 2018, July 16-17, Guangzhou University
14Towards Trustworthy Data for SHM
• Data can be compromised by:
Illegal values
Violated attribute dependencies
Uniqueness violation
Referential integrity violation
Missing values
Misspellings
Cryptic values
Embedded values
Misfielded values
Word transpositions
Duplicate records
Contradicting records
Wrong references
Overlapping data/matching records
Name conflicts
Structural conflicts
Challenges with Trustworthy Data Collection in SHM
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15Towards Trustworthy Data for SHM
Traditional Data Quality Checking Not Helpful
0/1 pattern, sum, avg., max,
metric
Decision-making
Meaningful decision for an
event?
Damage, crack, corrosion, displacement in a structure need real measured information
WTC 2018, July 16-17, Guangzhou University
16Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
17Towards Trustworthy Data for SHM
Consequences of Security Attacks in SHM
• Once Attacks Happened on the Data• Poor data collection
• Trustworthy issues
• Lack of confidence in data
• Poor relationship between the data and system
• Inconsistent reporting• Bad or delayed decision-making
• Low confidence in the decision
• Missed opportunities
• Loss of performance • Increased workloads
• Decreased meaningful output
• Decreased monitoring quality
WTC 2018, July 16-17, Guangzhou University
18Towards Trustworthy Data for SHM
Consequences of Security Attacks in SHM
• An undiscovered yet interesting fact in SHM system, i.e., • The real measured signals introduced by one or more sensors
(possibly faulty or compromised sensors) may cause • An undamaged event to be identified as damaged (false positive)
• A damaged event to be identified as undamaged (false negative)
WTC 2018, July 16-17, Guangzhou University
19Towards Trustworthy Data for SHM
Consequences of Security Attacks in SHM
++ Damage ++ Fault + - Damage ++ Fault
+ - Damage + - Fault
+ - Damage - - Fault
WTC 2018, July 16-17, Guangzhou University
20Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
21Towards Trustworthy Data for SHM
Solutions for Trustworthy Data
• Data trustworthiness is a multi-faceted concepts• It means different things to different people, devices, or
applications
• The prevention of unauthorized and improper data modification
• The quality of data
• The consistency and correctness of data
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22Towards Trustworthy Data for SHM
Solutions for Trustworthy Data
DataTrustworthiness
Data Collection and Usage
Management (Authorized
activity) Identity
Management (Devices, People,
Organizations)
Fault Management
Data Provenance (of data, software,
and request)
Potential Attack
Management (Authorized
activity) )
Assessing data trustworthiness
The Trust Data Fabric
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23Towards Trustworthy Data for SHM
Solutions for Trustworthy Data
• The trustworthiness of data is versatile• It is hard to quantify
• It may change, independent from direct modifications• Time, real-world facts
• Its implication may vary, depending on applications• High trustworthiness is always preferred
• However, high trustworthiness often has high costs
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24Towards Trustworthy Data for SHM
Solutions for Trustworthy Data
• Data trustworthiness is critical for making “good” decisions• Few efforts have been devoted to investigate approaches for
assessing how trusted data is
• No techniques exist able to protect against data deception
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25Towards Trustworthy Data for SHM
Solutions for Trustworthy Data
• Different definitions require different approaches.• Access control after device deployment
• Access control at the time of data acquisition
• Constraints, etc.
• We need flexible solutions in which application-dependent policies can be specified and enforced
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26Towards Trustworthy Data for SHM
Some Solutions
• Solution Objectives
The acquireddata is
trustworthy
Transmit thetrustworthy
data
Receive trustworthy
Data
Trustworthy decision-
making in SHM
WTC 2018, July 16-17, Guangzhou University
27Towards Trustworthy Data for SHM
IDEA (1)
• At the time of data acquisition• Signal comparison
• Signal to signal
• Signal to noise
• Random signal sampling
• Periodical signal comparing
• Signal correlation analysis
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28Towards Trustworthy Data for SHM
• Proposed a SHM framework• Find out a victim node
(faulty/compromised)• Signal to signal comparison• Analyses signal correlation
• A number of signals of interest (e.g., vibration signals, strain) is measured at each sensor. By analyzing the measured signals, each sensor identifies faulty sensors by using MII
• A joint Gaussian distribution based correlation model is used in this work, where we perform signal statistical analysis.
• The statistical dependency between the two sensors’ signals quantified by MII
Signal to Signal Comparison and Signal Correlation
WTC 2018, July 16-17, Guangzhou University
29Towards Trustworthy Data for SHM
• After faulty sensor detection, • Each sensor locally analyze the signals, compute structural
characteristics (e.g., mode shape ), and then transmits to the sink directly
• The sink assembles all of the received modes and identifies the structural damage
Signal to Signal Comparison and Signal Correlation
Dependable Structural Health Monitoring Using Wireless Sensor Networks, IEEE Transactions on Dependable and Secure Computing (TDSC), 2017
WTC 2018, July 16-17, Guangzhou University
30Towards Trustworthy Data for SHM
Another Way: Frequency Matching Algorithm
Natural frequency matrix
Comparability list for each frequency
Sensor nodes with low comparability is deleted iteratively
The comparability of each sensor node
WTC 2018, July 16-17, Guangzhou University
31Towards Trustworthy Data for SHM
• Structural damage is correctly located
Fault Tolerant WSN-based
Structural Health
Monitoring, IEEE DSN
2012
WTC 2018, July 16-17, Guangzhou University
32Towards Trustworthy Data for SHM
IDEA (2)
o Before the transmissiono A data validation tool may be required whether collected data
should be sent with priority (enhanced security)o Designing event-sensitive data compression technique for SHM
WTC 2018, July 16-17, Guangzhou University
33Towards Trustworthy Data for SHM
o Before the transmissiono A data validation tool may be required whether collected data
should be sent with priority (enhanced security)o Designing event-sensitive data compression technique for SHM
C
WTC 2018, July 16-17, Guangzhou University
34Towards Trustworthy Data for SHM
o Before the transmissiono A data validation tool may be required whether collected data
should be sent with priority (enhanced security)o Designing event-sensitive data compression technique for SHM
C
WTC 2018, July 16-17, Guangzhou University
35Towards Trustworthy Data for SHM
IDEA (3)
• At the time of reception/before aggregation/decision-making, need evaluation of any changes due to sensor faulty/ compromised readings, or damage • In a case when there exist some nodes with
faulty/compromised readings• The presence of structural damage can still be detected
• When there is no structural damage, there is no false alarms issued due to the faulty readings
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36Towards Trustworthy Data for SHM
• Decision fusion: local decision >> global decision
[1] Clouqueur, T., K. Saluja, and P. Ramanathan, Fault tolerance in collaborative sensor networks for target detection. IEEE transactions on computers, 2004. 53(3): p. 320-333.
[2] Krishnamachari, B. and S. Iyengar, Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE transactions on computers, 2004: p. 241-250
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37Towards Trustworthy Data for SHM
• However …
• Decision fusion (local decision→ global decision)• Assumes that each healthy sensor node can make correct local
decision about the event
• However, in SHM, damage detection always relay on the raw data of multiple nodes → individual sensor node is not able to detect damage, even the sensor itself is not faulty
WTC 2018, July 16-17, Guangzhou University
38Towards Trustworthy Data for SHM
• Value Fusion• Sensors in value fusion
exchange their measured values first and then make their decisions
• Value fusion would fail in SHM• Assume the data volume of
each node is low• However, in SHM,
exchanging the raw data directly among the sensor nodes is not applicable
WTC 2018, July 16-17, Guangzhou University
39Towards Trustworthy Data for SHM
• Data collection approaches • should able to detect and locate structural damage in various
environmental and noise conditions.
• should be able to discriminate sensor faults from structural damage.
WTC 2018, July 16-17, Guangzhou University
40Towards Trustworthy Data for SHM
• Two kind of approaches could be o TrustData evaluation
• Truth discovery
WTC 2018, July 16-17, Guangzhou University
41Towards Trustworthy Data for SHM
o Truth discoveryo It is used in many domains in order to resolve conf licts with
multiple noisy data or sources (sensors)
The medias provide billions of pieces of information,unfortunately, not all are reliable, relevant accurate, unbiased,or up-to-date
Before being used, the information are evaluated for truth.
o Key idea
Evaluating ‘true information’ and its ‘source reliability’
o Principle
Infer both truth and source reliability from the data
WTC 2018, July 16-17, Guangzhou University
42Towards Trustworthy Data for SHM
IDEA (4)
• Guaranteeing trustworthy decision-making from data reduced at the acquisition • Energy consumption reduction
• Wireless bandwidth reduction
• Real-time decision making
• Cost reduction
WTC 2018, July 16-17, Guangzhou University
43Towards Trustworthy Data for SHM
IDEA (4)
• Guaranteeing trustworthy decision-making from data reduced at the acquisition • Investigation on the reduced data leading to
untrustworthy decision-making• At a low rate or high rate
• 20Hz, 560Hz, 1024Hz
• With narrow frequency• Single
• Even-sensitive• Threshold (drop if low threshold)
• Frequency content • High or low frequency content data
WTC 2018, July 16-17, Guangzhou University
44Towards Trustworthy Data for SHM
• Guaranteeing trustworthy decision-making from data reduced at the acquisition
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45Towards Trustworthy Data for SHM
• Guaranteeing trustworthy decision-making from data reduced at the acquisition
Quality-Guaranteed Event-Sensitive Data Collectionand Monitoring in Vibration Sensor Networks, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017
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46Towards Trustworthy Data for SHM
IDEA (5)
• Protection to Direct Integrity Attacks• Data integrity
• Data at the acquisition is changed only in a specified and authorized manner
• NO (modification, insertion, deletion, replay) at the acquisition
• System integrity• System configuration is changed only in a specified and authorized
manner• Protection from attack, integration w/ or w/o recovery
• Attacker does not undetectably corrupt system’s function for Alice
WTC 2018, July 16-17, Guangzhou University
47Towards Trustworthy Data for SHM
IDEA (6)
• Designing Acquired Data Forensics Tool • Find out any discrepancy
• Authentication requires access to the original data.
• The identification of a discrepancy is an allegation• It does not mean there was an intentional falsification of data.
• The interpretation of whether any data manipulation is serious
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48Towards Trustworthy Data for SHM
• The idea of plagiarism algorithm may help• Data Forensic Tools are employed by journals in a manner
similar to tools used to detect plagiarism.
WTC 2018, July 16-17, Guangzhou University
49Towards Trustworthy Data for SHM
IDEA (7)
• Acquired Data Trustworthiness Models• The data we collect is of high-quality or trustworthy
• But there is NO model to guarantee it
WTC 2018, July 16-17, Guangzhou University
50Towards Trustworthy Data for SHM
• Example:• Computing trust scores: quantitative measures of
trustworthiness• Data trust scores
• Indicate about how much we can trust the data items
• SHM requirement (reference data having damage event information, noise information, initial data)
• Node trust scores• Indicate about how much we can trust the sensor nodes collect correct data
• Reliability, noise environment, location quality
WTC 2018, July 16-17, Guangzhou University
51Towards Trustworthy Data for SHM
• Assessing data trustworthiness• Computing data trust scores: quantitative measures of
trustworthiness
Node Trust Scores Data Trust Scores
trust score of the data affects the trust score of the sensor nodes that created the data
trust score of the node affects the trust score of the data created by the node
data arrives incrementallyin data stream environments
WTC 2018, July 16-17, Guangzhou University
52Towards Trustworthy Data for SHM
• Assessing data trustworthiness• Computing data trust scores: quantitative measures of
trustworthiness
Current trust scores
of nodes ( )
Next trust scores
of nodes ( )
Intermediate trust
scores of nodes ( )
+
Current trust scores
of data items ( )
Intermediate trust
scores of data items ( )
Next trust scores
of data items ( )
A set of data items of the
same event
in a current window
+
1
2
3
5
4
6
ns
ns
ns
ds
ds
ds
WTC 2018, July 16-17, Guangzhou University
53Towards Trustworthy Data for SHM
• Assessing data trustworthiness• Adaptive data trust score
• Data trust scores can be adjusted according to • The data value similarities and
• The data provenance similarities of
• a set of recent data items (i.e., history),
• reference damage event data set,
• noise information, or
• initial data
WTC 2018, July 16-17, Guangzhou University
54Towards Trustworthy Data for SHM
IDEA (8)
• Digital Citizenship for Acquired Data• Data residency ID, can be given at the time of data acquisition
(such as fingerprint)• Can be used for automatic data providence
• Blockchain (smart contracts) may be employed for data security.• However, at any level if data trustworthy is dubious, data
residency ID holder can be called and re-checked.
WTC 2018, July 16-17, Guangzhou University
55Towards Trustworthy Data for SHM
Outline
• What is SHM System?
• Data Collection in SHM Systems
• Challenges with Trustworthy Data Collection in SHM
• Consequences of Untrustworthy Data in SHM
• Some Solutions
• Conclusions
WTC 2018, July 16-17, Guangzhou University
56Towards Trustworthy Data for SHM
Conclusions
• It is still tough to say which is trustworthy data at the time of • acquisition, sending, processing, storing, and decision making
• Need research attention
• It is still tough to say a change in a structure is due to either• Noise, security attack, sensor fault, or damage
• Really need isolation of each of them • We worked on sensor fault vs. damage (but there are limitations)
WTC 2018, July 16-17, Guangzhou University
57Towards Trustworthy Data for SHM
Contact@[email protected], [email protected]://sites.google.com/site/zakirulalam/home