Context awareness is an approach that has been receiving increasing focus in the past years. A context aware device can understand surrounding conditions and adapt its behavior accordingly to meet user demands. Mobile handheld devices offer a motivating platform for context aware applications as a result of their rapidly growing set of features and sensing abilities. This research aims at building a situational awareness model that utilizes multimodal sensor data provided through the various sensing capabilities available on a wide range of current handheld smart phones. The model will make use of seven different virtual and physical sensors commonly available on mobile devices, to gather a large set of parameters that identify the occurrence of a situation for one of five predefined context scenarios: In meeting, Driving, in party, In Theatre and Sleeping. As means of gathering the wisdom of the crowd and in an effort to reach a habitat sensitive awareness model, a survey was conducted to understand the user perception of each context situation. The data collected was used to build the inference engine of a prototype context awareness system utilizing context weights introduced in  and the confidence metric in  with some variation as a means for reasoning. The developed prototype's results were benchmarked against two existing context awareness platforms Darwin Phones  and Smart Profile , where the prototype was able to acquire 5% and 7.6% higher accuracy levels than the two systems respectively while performing tasks of higher complexity. The detailed results and evaluation are highlighted further in section 6.4.
Computer Science & Engineering Department
MS in Computer Science
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Abdel Samie, M.
(2015).Human centric situational awareness [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Abdel Samie, Mohamed. Human centric situational awareness. 2015. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.