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Control-Theoretic Approaches for Dynamic Information Assurance. George Vachtsevanos Georgia Tech Working Meeting U. C. Berkeley February 5, 2003. The Information Assurance – Software Architecture Connection. Dynamic information assurance will require models of computation that - PowerPoint PPT Presentation
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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
