A Human-Centric Approach to Group-Based Context-Awareness
Nasser Ghadiri, Ahmad Baraani-Dastjerdi, Nasser Ghasem-Aghaee,, Mohammad A. Nematbakhsh

TL;DR
This paper introduces f-Context, a human-centric, service-based framework for group-based context-awareness that models complex uncertainties using language-action theory and computing with words, demonstrated through a mobile user scenario.
Contribution
It presents a novel, theory-based, modular framework for group context-awareness that manages high-order uncertainties with a layered architecture and agent-based implementation.
Findings
Feasibility demonstrated with a mobile user scenario.
Framework effectively models high-order uncertainties.
Provides a theoretical foundation for future research.
Abstract
The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer…
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