HiCon: a hierarchical context monitoring and composition framework for next-generation context-aware services

Korea Adv. Inst. of Sci. & Technol., Seoul
IEEE Network (Impact Factor: 2.54). 08/2008; 22(4):34 - 42. DOI: 10.1109/MNET.2008.4579769
Source: IEEE Xplore


This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.

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Available from: Seungwoo Kang, Mar 22, 2013
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    • "HiCon [16] "
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    • "A monitoring framework should deal with a broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. For example, in [65], context is classified in three main layers regarding personal, regional and global scale while in [66], it is clearly stated that a context monitoring framework should have the capability of monitoring physical to application-layer context information via appropriate cross-layer mechanisms. "
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    • "Several research efforts have been conducted for developing a new conceptual framework in understanding the context-aware computing service market. Cho et al. (2008) proposes an effective framework for extracting context and provides an example service of location information. Rosemann et al. (2006) proposes a stratified layer framework for context by extending the scope of process modeling beyond immediately available context. "
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