2008 ieee international conference on technologies for homeland security a modeling framework for...

2008 IEEE International Conference on Technologies for Homeland Security

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

[email protected]

http://impact.asu.edu/



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.


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.


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.


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.


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 processes essential for preparedness


Smart-Infrastructure & Crises Response

• Integrated computing systems for physical processes (including crises response).

• Operations in computing entities affect the physical world & vice versa.

Courtesy: Vanderbilt University & Drexel University

Requirements• Autonomy – self healing, self

configuring, self optimizing

• Validation – performance evaluation

Problem: quantitative measures required to evaluate

crises response processes to incorporate autonomy


Crises Management – Fire in Smart-Building

Crisis Response Recovery Preparedness

Detect fire using information from sensors

CausingEvent Detection

• Notify 911

• provide information to the first responders

• 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.

Trapped People & Rescuers

Additional Events

Detect trapped people

Detection

Evaluate Effectiveness of Response Process

Learning

Research Focus

Mitigation


Modeling Framework to Evaluate

Crises Response Effectiveness


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

Timely Criticality Response within

window-of-opportunity

Mismanagement of any

criticality

NORMAL STATE

CRITICALSTATE

DISASTER(loss of lives/property)


• 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 with1. response action.2. probability of success.3. time to take action.

State Based Stochastic Model for Criticality Response

NORMAL STATE

CRITICAL STATE

Mitigative Link (ML)

Criticality Link (CL)


Manageability in terms of Q-value or Qualifiedness of actions

– probability of reaching normal state based on

1. Probabilities of MLs.

2. Probabilities of CLs at intermidiate states.

3. Conformity to timing requirements.

State Based Stochastic Model for Criticality Response

NORMAL STATE

CRITICAL STATE

Mitigative Link (ML)

Criticality Link (CL)

Q-value is a quantitative measure to evaluate crises response.

Goal: develop enabling framework to apply Q-value metric.


Crisis Response Recovery PreparednessMitigationMitigation

Identify the critical events

Determine the Window-of-opportunity

Determine the possible occurrences of

multiple criticalities

Determine the states & transition probabilities

Apply the Stochastic Model

Evaluate the Q-value of Criticality Response Process

CRM Framework

Evaluate Effectiveness of Response Process

Criticality Response Modeling (CRM) Framework

Lea

rnin

g


Application of CRM


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.

S tar t

F ir e & Ex p lo s io nAlar m ?

N o

I m m in en tD an g er ?

Yes

R etu r n p r o c es s eq u ip m en tto s af e s ta te

M ak e w o r k p lac e s af e

N o

Ev a lu a te p o te n t ia l e v a c u a t io np a th s a n d c h o o s e ro u te (o r

a lt e rn a te ro u te )

M o v e a lo n g ev ac u atio nr o u te

L is ten & f o llo w P Aan n o u n c em en ts

As s is tan c er eq u ir ed f o r

o th er s ?

Yes

Yes

E v ac u atio np ath n o tten ab le?

N o

Yes

R eg is te r a t tem p o r ar yr efu g e

N o

P r o v id e f eed b ac k


S tar t


N o


Yes



N o






o th er s ?

Yes

Yes


N o

Yes


N o


CRM for fire emergencies in OGPP – Identify Criticalities

criticality 1 (c1)

criticality 2 (c2)

criticality 3 (c3)

criticality 4 (c4)

Identify the decision boxes of the process flow as criticalities.


CRM for fire emergencies in OGPP – Identify Response Actions

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

S tar t


N o


Yes



N o






o th er s ?

Yes

Yes


N o

Yes


N o


Response to c1

c1

c2c3

c4

Response to c3, c4

Response to c2


CRM for fire emergencies in OGPP – Identify States and Determine Window-of-opportunity

Fire Alarm

Fire Alarm &Imminent Danger

Fire Alarm &Non-tenable Path

Fire Alarm &Assistance Required

Fire Alarm &Non-tenable Path &

Assistance Required

Fire Alarm &Imminent Danger &

Assistance Required

Fire Alarm &Assistance Required &

Non-tenable Path

Criticalities

1. c1 – Fire Alarm.

2. c2 – Imminent danger e.g. health hazards.

3. c3 – Assistance required to others e.g. trapped personnel.

4. c4 – Evacuation path not tenable.

Window-of-opportunity survival time under

asphyxiation.


CRM for fire emergencies in OGPP – Determine State Transition Probabilities

Fire Alarm

Fire Alarm &Imminent Danger

Fire Alarm &Non-tenable Path

Fire Alarm &Assistance Required

Fire Alarm &Non-tenable Path &

Assistance Required

Fire Alarm &Imminent Danger &

Assistance Required

Fire Alarm &Assistance Required &

Non-tenable Path

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

[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.

0.1634

0.284877

0.40365

0.1755

0.1634

0.2965 0.48970.5862

0.5717

0.5717 0.2649

0.2649

0.481

0.1977 0.1892

0. 2094

0.41861 0.3348

0.4138


• 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.

Simulation Study


Greedy and MACM action selection Comparison

Low manageability for Greedy response action selection

(sec)

(Q-v

alu

e)

(MACM)

(MACM)

Zero manageability for high detection delay

Low manageability for increase in number of simultaneous criticalities


Effect of Actuation and Detection Delay for two simultaneous criticalities

Low manageability for high action time

(sec)

(sec)

(Q-v

alu

e)

Low manageability for high action time


Effect of Actuation and Detection Delay for three simultaneous criticalities

Low manageability for increase in number of simultaneous criticalities

(sec)(sec)

(Q-v

alu

e)


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.


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.


Questions ??

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

Creating Humane Technologies for Ever-Changing World



Additional Slides


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


Manageability as Q-value

n

NORMAL STATE

i

x

px,i

• Manageability from any arbitrary critical state x

– i an immediate upstream state.

Qx,i,n = px,iPi,n if W met = 0 if W NOT met

Pi,n = 1 if i = n = (1 - pi,j ) pi,kPk,n + pi,jPj,n if i n & W met = 0 if W NOT met

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

Probability of reaching the normal state from state i

(i,j) CL(i) (i,k) ML(i) (i,j) CL(i)

2008 ieee international conference on technologies for homeland security a modeling framework for...

Documents

crises response processes

importance of crises

aftermath of crises

infrastru crises management

potential national crises

disasters terrorist

emergency preparedness

physical processes