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A Modeling Framework for Evaluating Effectiveness of Smart-Infrastructure Crises Management Systems. Tridib Mukherjee and Sandeep K. S. Gupta Impact Lab ( http://impact.asu.edu ) School of Computing & Informatics Arizona State University sandeep.gupta@asu.edu. Outline. Motivation.

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a modeling framework for evaluating effectiveness of smart infrastructure crises management systems

A Modeling Framework for Evaluating Effectiveness of Smart-Infrastructure Crises Management Systems

Tridib Mukherjee and Sandeep K. S. Gupta

Impact Lab (http://impact.asu.edu)

School of Computing & Informatics

Arizona State University

sandeep.gupta@asu.edu

2008 IEEE International Conference on Technologies for Homeland Security

outline
Outline
  • Motivation.
  • Smart-Infrastructure Crises Management.
  • Criticality Response Modeling (CRM) framework to evaluate crises response for smart-infrastructure.
  • Application of CRM to fire emergencies in offshore Oil & Gas Production Platforms (OGPP).
  • Simulation based verification of the framework.
  • Conclusions & Future Work.

2008 IEEE International Conference on Technologies for Homeland Security

goals of homeland security
Goals of Homeland Security
  • Department of Homeland Security (DHS) missions include
    • Prevention of terrorist attacks within the US.
    • Reduction of vulnerability to terrorism.
    • Minimizing the damage from potential attacks and natural disasters.
    • In summary:be prepared for potential national crises and planning proper responses.
  • DHS combines 22 federal agencies into four policy directorates
    • Border and Transportation Security.
    • Emergency Preparedness and Response.
    • Information Analysis and Infrastructure Protection.
    • Science and Technology.

2008 IEEE International Conference on Technologies for Homeland Security

importance of crises response and preparedness to dhs
Importance of crises response and preparedness to DHS
  • In 2004, over $4 billion of Homeland Security Grants allocated for assistance to the first responders.
  • In 2005, $7.4 billion fund budgeted for Emergency Preparedness and Response (around 20% of the total budget).
    • over $3.5 billion (50%) budgeted for assistance to first responders.
  • Since March 1, 2003, approximately $8 billion awarded to state, tribal and local governments to prevent, prepare for, respond to and recover from acts of terrorism and all hazards.

2008 IEEE International Conference on Technologies for Homeland Security

what are crises
What are Crises?

Massive (cascading) catastrophic events leading to loss of lives/property

  • natural disasters – hurricanes (e.g. Katrina), earthquakes.
  • man-made disasters – terrorist attacks (9/11).
  • other disasters – fire in building, leakage in nuclear plant.

2008 IEEE International Conference on Technologies for Homeland Security

management of crises
Management of Crises
  • Systematic attempt to prepare, avoid and/or respond to crises
  • Four operational phases
    • Response – immediate actions to protect lives/property.
    • Recovery – efforts in the aftermath of crises.
    • Mitigation – lessen the impact of the crises.
    • Preparedness – effort to reduce impact in future.

Courtesy: City of Crookston

Motivation: evaluation of response processesessential for preparedness

2008 IEEE International Conference on Technologies for Homeland Security

smart infrastructure crises response
Smart-Infrastructure & Crises Response

Courtesy: Vanderbilt University & Drexel University

  • Integrated computing systems for physical processes (including crises response).
  • Operations in computing entities affect the physical world & vice versa.
  • Requirements
  • Autonomy – self healing, self configuring, self optimizing
  • Validation – performance evaluation

Problem:quantitative measuresrequired to evaluate

crises response processes to incorporate autonomy

2008 IEEE International Conference on Technologies for Homeland Security

crises management fire in smart building
Crises Management – Fire in Smart-Building

Causing

Event

Additional

Events

Detection

Detection

Crisis

Response

Preparedness

Recovery

Mitigation

Trapped People & Rescuers

Detect fire using information from sensors

  • Notify 911
  • provide information to the first responders

Detect trapped people

Learning

Evaluate Effectiveness

of Response Process

  • Analyze the Spatial Properties
    • how to reach the source of fire;
    • which exits are closest;
    • is the closest exist free to get out;
  • Determine the required actions
    • instruct the inhabitants to go to nearest safe place;
    • co-ordinate with the rescuers to evacuate.

Research Focus

2008 IEEE International Conference on Technologies for Homeland Security

modeling framework to evaluate crises response effectiveness

Modeling Framework to Evaluate Crises Response Effectiveness

2008 IEEE International Conference on Technologies for Homeland Security

definitions concepts
Definitions & Concepts
  • Critical events
    • Causes emergencies/crisis.
    • Leads to loss of lives/property.
  • Criticality
    • Effects of critical events on the smart-infrastructure.
    • Critical State – state of the system under criticality.
    • Window-of-opportunity (W) – temporal constraint for criticality.
  • Manageability – effectiveness of the criticality response actions in minimizing the disasters.

Critical Event

CRITICAL

STATE

NORMAL

STATE

Timely Criticality

Response within

window-of-opportunity

Mismanagement

of any

criticality

DISASTER

(loss of lives/property)

2008 IEEE International Conference on Technologies for Homeland Security

slide11

State Based Stochastic Model for Criticality Response

NORMAL STATE

  • Zoom into Critical State.
    • System in different sub-state for different criticalities.
    • Hierarchical organization of sub-states.
  • Criticality Link (CL) –takes the system down the hierarchy
    • associates with probability of criticality occurrence.
  • Mitigative Link (ML) – takes the system up the hierarchy
    • associates with
      • response action.
      • probability of success.
      • time to take action.

CRITICAL STATE

Mitigative

Link (ML)

Criticality

Link (CL)

2008 IEEE International Conference on Technologies for Homeland Security

slide12

State Based Stochastic Model for Criticality Response

NORMAL STATE

Manageability in terms ofQ-valueorQualifiednessof actions

  • probability of reaching normal state based on
    • Probabilities of MLs.
    • Probabilities of CLs at intermidiate states.
    • Conformity to timing requirements.

Q-valueis a quantitative measure

to evaluate crises response.

CRITICAL STATE

Mitigative

Link (ML)

Criticality

Link (CL)

Goal:developenablingframework to apply Q-value metric.

2008 IEEE International Conference on Technologies for Homeland Security

criticality response modeling crm framework
Criticality Response Modeling (CRM) Framework

Crisis

Response

Preparedness

Recovery

Mitigation

Mitigation

Evaluate Effectiveness

of Response Process

Identify the critical events

Evaluate the Q-valueof

Criticality Response Process

Determine the

Window-of-opportunity

Learning

Determine the

possible occurrences of

multiple criticalities

Apply the Stochastic Model

Determine the states &

transition probabilities

CRM

Framework

2008 IEEE International Conference on Technologies for Homeland Security

application of crm

Application of CRM

2008 IEEE International Conference on Technologies for Homeland Security

fire emergencies in offshore oil gas production platforms ogpp example process flow
Fire Emergencies in offshore Oil & Gas Production Platforms (OGPP) – example process flow*

* D. G. DiMattia, F. I. Khan, and P. R. Amyotte, “Determination of human error probabilities for offshore platform musters,” Journal of Loss Prevention in the Process Industries, vol. 18, pp. 488–501, 2005.

2008 IEEE International Conference on Technologies for Homeland Security

crm for fire emergencies in ogpp identify criticalities
CRM for fire emergencies in OGPP – Identify Criticalities

Identify the decision boxes of the process flow as criticalities.

criticality 1 (c1)

criticality 3 (c3)

criticality 2 (c2)

criticality 4 (c4)

2008 IEEE International Conference on Technologies for Homeland Security

crm for fire emergencies in ogpp identify response actions
CRM for fire emergencies in OGPP – Identify Response Actions

Identify the appropriate decision branches of the process flow as response actions.

Response to c1

c1

Response to c2

c2

c3

c4

Response to c3, c4

2008 IEEE International Conference on Technologies for Homeland Security

crm for fire emergencies in ogpp identify states and determine window of opportunity
CRM for fire emergencies in OGPP – Identify States and Determine Window-of-opportunity

Criticalities

  • c1 – Fire Alarm.
  • c2 – Imminent danger e.g. health hazards.
  • c3 – Assistance required to others e.g. trapped personnel.
  • c4 – Evacuation path not tenable.

Fire Alarm

Fire Alarm &

Assistance Required

Fire Alarm &

Imminent Danger

Fire Alarm &

Non-tenable Path

Window-of-opportunity

  • survival time under asphyxiation.

Fire Alarm &

Non-tenable Path &

Assistance Required

Fire Alarm &

Imminent Danger &

Assistance Required

Fire Alarm &

Assistance Required &

Non-tenable Path

2008 IEEE International Conference on Technologies for Homeland Security

crm for fire emergencies in ogpp determine state transition probabilities
CRM for fire emergencies in OGPP – Determine State Transition Probabilities

State transition probabilities derived from established probability distribution in [1].

0.40365

0.1634

0.1892

0.1755

0.1977

0.284877

Fire Alarm

0. 2094

0.5862

0.2965

0.4897

0.1634

Fire Alarm &

Assistance Required

Fire Alarm &

Imminent Danger

Fire Alarm &

Non-tenable Path

0.2649

0.5717

0.4138

0.3348

0.2649

0.41861

0.5717

0.481

[1] D. G. DiMattia, F. I. Khan, and P. R. Amyotte, “Determination of human error probabilities for offshore platform musters,” Journal of Loss Prevention in the Process Industries, vol. 18, pp. 488–501, 2005.

Fire Alarm &

Non-tenable Path &

Assistance Required

Fire Alarm &

Imminent Danger &

Assistance Required

Fire Alarm &

Assistance Required &

Non-tenable Path

2008 IEEE International Conference on Technologies for Homeland Security

slide20

Simulation Study

  • Response Action Selection Policies
    • Greedy – response actions corresponding to ML with maximum probability
      • Oblivious of subsequent criticalities.
    • Mitigative Action based Criticality Management (MACM) – response actions corresponding to MLs with maximum Q-values
      • Not oblivious of subsequent criticalities.
  • Simulation Goal
    • Compare different response action selection policies.
    • Evaluate impact of timing factors to manageability of criticality response
      • Criticality detection delay.
      • Response action actuation delay.
    • Verifies applicability of Q-value as manageability metric.

2008 IEEE International Conference on Technologies for Homeland Security

greedy and macm action selection comparison
Greedy and MACM action selection Comparison

(MACM)

(MACM)

Low manageability for Greedy response action selection

Zero manageability for high detection delay

(Q-value)

Low manageability for increase innumber of simultaneous criticalities

(sec)

2008 IEEE International Conference on Technologies for Homeland Security

effect of actuation and detection delay for two simultaneous criticalities
Effect of Actuation and Detection Delay for two simultaneous criticalities

Low manageability for high action time

Low manageability for high action time

(Q-value)

(sec)

(sec)

2008 IEEE International Conference on Technologies for Homeland Security

effect of actuation and detection delay for three simultaneous criticalities
Effect of Actuation and Detection Delay for three simultaneous criticalities

Low manageability for increase innumber of simultaneous criticalities

(Q-value)

(sec)

(sec)

2008 IEEE International Conference on Technologies for Homeland Security

conclusions
Conclusions
  • CRM framework developed for evaluating effectiveness of crises response processes.
  • CRM applied to real crisis situation – fire emergencies in Oil & Gas Production Platforms.
  • CRM enables
    • Q-value based quantitative evaluation of crises response.
    • automated learning from the outcome.
    • steeper learning curve – improved preparedness for crises response.

2008 IEEE International Conference on Technologies for Homeland Security

future work
Future Work
  • Q-value calculation computationally expensive
    • good metric for evaluation.
    • bad for on-line planning.
  • Probabilistic planning to select response actions based on the stochastic model.
    • determine optimal response selection policy.
    • computation complexity within temporal requirements.
  • Develop simulation tools and visualization of the planned actions and their effects
    • for use by the disaster manager.

2008 IEEE International Conference on Technologies for Homeland Security

questions
Questions ??

Impact Lab (http://impact.asu.edu)

Creating Humane Technologies

for Ever-Changing World

2008 IEEE International Conference on Technologies for Homeland Security

additional slides
Additional Slides

2008 IEEE International Conference on Technologies for Homeland Security

effectiveness evaluation for the response actions
Effectiveness Evaluation for the Response Actions
  • Generally in terms of cumbersome documents
    • Reports / recommendations
    • Qualitative & subjective
    • Inadequate for smart-infrastructure
      • Requires quantitative evaluation
      • Objective comparison between different response actions for steeper learning curve
      • Evaluate impact of different parameters to the effectiveness of criticality response
  • Quantitative Evaluation
    • What are the evaluation criteria & metrics?
      • Theoretical Foundation Established in our previous work – crises characterized as criticalities.
    • How to perform evaluation for any crises response process?
      • Research Goal: Develop generic evaluation framework for crisis response.
  • Contributions
    • Criticality Response Modeling (CRM) Framework
    • Application of CRM for fire emergencies in offshore Oil & Gas Production Platforms (OGPP)
    • Simulation based evaluation of CRM over OGPP

2008 IEEE International Conference on Technologies for Homeland Security

manageability as q value
Manageability as Q-value

NORMAL

STATE

Probability of

reaching the

normal state

from state i

  • Manageability from any arbitrary critical state x
    • i an immediate upstream state.

n

i

px,i

Qx,i,n = px,iPi,n ifW met

= 0 if W NOT met

x

Pi,n= 1if i = n

= (1 -  pi,j )  pi,kPk,n +  pi,jPj,nifi n &W met

= 0ifW NOT met

(i,j)  CL(i)

(i,j)  CL(i)

(i,k)  ML(i)

Probability of a

criticality at state i

Probability of reaching

normal state if NO additional

criticality occurs at state i

Probability of reaching

normal state if ANY additional

criticality occurs at state i

2008 IEEE International Conference on Technologies for Homeland Security