Conference Paper

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

Dept. of Comput. Sci. & Eng., Arizona State Univ., Tempe, AZ
DOI: 10.1109/THS.2008.4534512 Conference: Technologies for Homeland Security, 2008 IEEE Conference on
Source: IEEE Xplore

ABSTRACT Crises management for smart-infrastructure - infused with sensors, actuators, and intelligent agent technologies for monitoring, access control, and crisis response - requires objective and quantitative evaluation to learn for future. The concept of criticality - characterizing the effect of crises on the inhabitants of smart-infrastructure - is used in this regard. This paper establishes a criticality response modeling (CRM) framework to perform quantitative evaluation of criticality response. The framework can further be incorporated in any criticality-aware middleware for smart-infrastructure. An established stochastic model for criticality response is used from our previous work. The effectiveness of criticality response is measured in terms of the Manageability metric, characterized by the Q-value or qualifiedness of the response actions. The CRM is applied to fire emergencies in an envisioned smart oil & gas production platforms (OGPP). A simulation based evaluation, using CRM over OGPP, show that high manageability is achieved with - i) fast criticality detection, ii) fast response actuation, and iii) non-obliviousness to any subsequent criticality during response actuation - verifying the applicability of Q-value as the manageability metric.

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Available from: Tridib Mukherjee, Jan 06, 2015
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    • "A statebased stochastic model is established in this regard where critical states are considered when the system is under one or more criticalities. Using the stochastic model a generic modeling framework is developed i) to evaluate the effectiveness of the crises management in smart-spaces; and ii) to select appropriate crises response actions accordingly [1]. "
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    • "A state-based stochastic model is established in this regard where critical states are considered when the system is under one or more criticalities. Using the stochastic model a generic modeling framework is developed i) to evaluate the effectiveness of the crises management in smart-spaces; and ii) to select appropriate crises response actions accordingly [1]. Figure 1 depicts the modeling framework and its applicability to various phases of crises management. "
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    • "The transition probabilities are based on a previous study on the human error probabilities in the OGPP [21]. The windows-of-opportunity are taken as the average time for survival of humans under asphyxiation [12]. The following subsection discusses how the stochastic model can aid in crisis response planning and evaluation. "
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    ABSTRACT: In recent years, crisis response has become cyber- physical in nature because of the increased use of computing technologies by the responders. As such, crisis preparedness requires objective evaluation of crisis response in addition to the traditional drills. This paper develops a generic Crisis Response Evaluation Tool (CRET) for off-line objective crisis response evaluation to improve preparedness. The evaluation is performed through model-based engineering, which allows specification and automated analysis of crisis response behavior. An established state-based stochastic model is used to describe the behavior of crisis response processes. The effectiveness of a planned action is measured in terms of the action's qualifiedness (also called the Q-value)—which depends on the probability of any additional crises and the conformance to a temporal window-of-opportunity before which any action has to be performed. CRET uses Ab- stract Architecture Description Language (AADL) to specify the stochastic crisis response behavior model. Using this specification, CRET objectively analyzes the planned actions' Q-values under different circumstances; thus enabling an objective evaluation for crisis preparedness. Index Terms—Crisis Response, Crisis Preparedness, Criticality, Stochastic Model, Human-centered computing.
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