Modeling decision uncertainties in total situation awareness using cloud computation theory

Saleh Zein-Sabatto; Abdulqadir Khoshnaw; Sachin Shetty; Mohan Malkani; Atindra Mitra

doi:10.1117/12.886968

19 May 2011 Modeling decision uncertainties in total situation awareness using cloud computation theory

Saleh Zein-Sabatto, Abdulqadir Khoshnaw, Sachin Shetty, Mohan Malkani, Atindra Mitra

Proceedings Volume 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V; 80590E (2011) https://doi.org/10.1117/12.886968
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness, decision-making is faced with two types of uncertainties; information uncertainty and data uncertainty. Data uncertainty exists due to noise in sensor measurements and is classified as randomness. Information uncertainty is due to ambiguity of using (words) to describe events. This uncertainty is known as fuzziness. Typically, these two types of uncertainties are handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring of sensitive area. Testing results are reported at the end of the paper.

Citation Download Citation

Saleh Zein-Sabatto, Abdulqadir Khoshnaw, Sachin Shetty, Mohan Malkani, and Atindra Mitra "Modeling decision uncertainties in total situation awareness using cloud computation theory", Proc. SPIE 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V, 80590E (19 May 2011); https://doi.org/10.1117/12.886968

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