Tridib Mukherjee and Sandeep K. S. Gupta Impact Lab ( impact.asu )

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Modeling Framework to Evaluate Crises Response Effectiveness 2008 IEEE International Conference on Technologies for Homeland Security

10. 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

11. 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

12. 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

13. 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

14. Application of CRM 2008 IEEE International Conference on Technologies for Homeland Security

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. Questions ?? Impact Lab (http://impact.asu.edu) Creating Humane Technologies for Ever-Changing World 2008 IEEE International Conference on Technologies for Homeland Security

27. Additional Slides 2008 IEEE International Conference on Technologies for Homeland Security

28. 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

29. 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