Towards Trustworthy Data for Structural Health...

transcript

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: mbhuiyan3@fordham.edu, zakirulalam@gmail.com

http://storm.cis.fordham.edu/~bhuiyan/, https://sites.google.com/site/zakirulalam/

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

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

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

• A CPS, an IoT system, an application of Smartcity• Aircraft, building, bridge, nuclear plants, dams, wind turbine,

ship, vehicle, etc

Nov. 18,

2015May 17,

• Smart sensor nodes• Sensors

• CPU

• Wireless transceivers

What is SHM System?

Outline

• Some Solutions

• Conclusions

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

Data Collection in SHM Systems

Outline

• Some Solutions

• Conclusions

• 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

• 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

• Data can be compromised: • At the acquisition

• After the acquisition

• At the transmission

• During transmission

• After transmission, and

• Before aggregation

• 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

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

Outline

• Some Solutions

• Conclusions

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

• 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)

++ Damage ++ Fault + - Damage ++ Fault

+ - Damage + - Fault

+ - Damage - - Fault

Outline

• Some Solutions

• Conclusions

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

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

• 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

• 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

• 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

Some Solutions

• Solution Objectives

The acquireddata is

trustworthy

Transmit thetrustworthy

Receive trustworthy

Trustworthy decision-

making in 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

• 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

• 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

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

• Structural damage is correctly located

Fault Tolerant WSN-based

Structural Health

Monitoring, IEEE DSN

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

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

• 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

• 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

• 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

• 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.

• Two kind of approaches could be o TrustData evaluation

• Truth discovery

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

IDEA (4)

• Guaranteeing trustworthy decision-making from data reduced at the acquisition • Energy consumption reduction

• Wireless bandwidth reduction

• Real-time decision making

• Cost reduction

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

• 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

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

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

• The idea of plagiarism algorithm may help• Data Forensic Tools are employed by journals in a manner

similar to tools used to detect plagiarism.

IDEA (7)

• Acquired Data Trustworthiness Models• The data we collect is of high-quality or trustworthy

• But there is NO model to guarantee it

• 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

• 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

• 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

• 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

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.

Outline

• Some Solutions

• Conclusions

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)

Contact@zakirulalam@gmail.com, mbhuiyan3@fordham.eduhttps://sites.google.com/site/zakirulalam/home

Towards Trustworthy Data for Structural Health...

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