Control-Theoretic Approaches for Dynamic Information Assurance

Control-Theoretic Approaches for Dynamic Information Assurance

George VachtsevanosGeorgia Tech

Working MeetingU. C. Berkeley

February 5, 2003

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The Information Assurance – Software Architecture

ConnectionDynamic information assurance will

require models of computation thatCan direct the behavior of intelligent

controller components, route/re-route and blend signals

Specify and validate strategies that involve real-time Q◦S parameters and fault-tolerant constraints timed multitasking domains

Can support reconfiguration strategies involving transient compensation (control) and dynamic transitions

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Can monitor status of configuration changes and globally coordinate them

Can handle unexpected conditions (large-grain disturbances, pop-up targets, etc.) that may arise during a transition; interrupt and safely back-out of a transition

*”Smart” models of computation are required to support concepts and models of information assurance.

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Q◦S Controller

• Communicates with sensor client, i.e. system controller, diagnostic routines, system status, etc.

• Measures on-line available bandwidth and other performance measures and executes Q◦S algorithm

Q SController

NETWORKOF

SENSORS

SYSTEMCONTROLLER/

DIAGNOSTICIAN

feedback

client

Q◦S Controller :

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Dynamic Q◦S Control

• Ni(q) - bandwidth required by application (constraints)• Nimax(t) - available bandwidth at time t• q(t) - vector of sampling rates, bits-per-pixel, etc. • - sensor control • F(q) - user satisfaction function

ADAPTIVENEURAL NET F (q)

S (Q) S(Nimax-Ni(q))Ni(q)

Fmin + -

Nimax(t)

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CONTENTION FORSHARED RESOURCES

REAL-TIME RESPONSIVENESS DEPENDABILITY PRECISION QUALITY OF RESULTS

)STATES"("

VECTOR

RESOURCE

MODIFY /RECONFIGURE /RESCHEDULERESOURCES

RESOURCESBANDWIDTHDYNAMIC SCHEDULINGFAULT TOLERANCE RECONFIGURATION OTHERS

CRITICALAPPLICATIONS

Q◦SMECHANISM

FAILURES

DYNAMICWORKLOADS

PERFORMANCE

ASSESSMENT

IDENTIFY /PREDICT

DISTURBANCES

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Sensors 101

Raw data Information Knowledge

What kind of data?What type of sensors?How many?Where do we place them?

NSF/Other supported activities

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On the Concept of “Fusion”

Sensor Fusion– Data Fusion– Feature Fusion– Sensor Fusion– Report Fusion

Knowledge Fusion

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Sensor Fusion (or Integration)

• Objective: Optimize performance of information gathering process

• Intelligent sensor and knowledge fusion algorithms based on focus of attention via active perception and Dempster-Shafer theory

• Sensor integration at various levels of abstraction - the data, feature, sensor and report levels

• Distinguishability and effectiveness measures defined to guide the sensor integration task

• Off-line and on-line learning techniques for effective data combination

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Optimum Sensor Placement Strategies

• Traditional vs. proposed procedure

Model

Figure-of-Merit Selection

Optimization

Fig. 2a: Traditional sensor placement procedure.

Model

Figure-of-Merit Selection

Optimization

Fig. 2b: Proposed sensor placement procedure.

Performance Assessment

FMECA

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The Value of Information

Question: How do we assess the value of information?

How do we maximize it? MetricsOptimization techniquesControl-theoretic conceptsExamples from diagnosis/prognosis, control, alarming, etc.

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Active Diagnosis• Extends the offline ideas of “Probing” or “Testing”• It is biased to monitor normal conditions• Active Diagnosis Monitors consistency among data• Active Diagnosis of DES - A Design Time Approach

– the system itself is not diagnosable– design a controller called “Diagnostic Controller” that

will make the system diagnosable

• Active Diagnosis Possibilities:– Inline with Intelligent Agent paradigm – Collaboration in Multiagent Systems can be directed to

achieve Active Diagnosis

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Active vs. Passive Diagnosis

• Passive Diagnosis: Diagnoser FSM that monitors

events and sensors to generate diagnosis.

A Diagnosable Plant generates a language from which unobservable failure conditions can be uniquely inferred by the Diagnoser FSM.

• Design-Time Active Diagnosis: Design a controller that will

make an otherwise “non-diagnosable” plant generate a language that is diagnosable.

Plant(Chiller/Pump & Valve)

Controller

Diagnoser FSM

Sensors

ObservableEvents

ObservableState

Diagnosis

Unobservable Failures

Plant(Chiller/Pump & Valve)

Controller

Diagnoser FSM

Sensors

ObservableEvents

ObservableState

Diagnosis

Unobservable Failures

Diagnoser

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Active Diagnosis - Agent Perspective

• Given an anomalous situation, Diagnostic Agent Plans, Learns, and Coordinates.– Learning takes place between

distributed agents that share their experiences

– Coordination helps search, retrieval, adapting activities

– Planning is required to determine if learning and coordination is possible in the given expected time-to-failure condition

• “Run-time” Active Diagnosis– non-intrusive– autonomous and rational

Plant(Chiller/Pump & Valve)

Controller

DiagnosticAgent

SensorAgents

ObservableEvents

ObservableState

Diagnosis

Unobservable Failures

SensorAgents

Network ofDiagnosticAgents

Alarms/DB

Dia

gnos

tic

Age

nt Planning

CoordinationLearning

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Information Assurance

Enabling Technologies:Sensor FusionData ValidationQ◦S methodsPerformance metrics