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

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.

Hierarchical Context Monitoring and Composition Framework

As the first step in designing the proposed framework, we carefully investigate promising contexts and classify them. Since contexts could be an abstraction of one or more types of sensor data, classifying them also means finding out abstraction scopes where contexts are very likely to be found. In this work we mainly look into the types of services in terms of contexts, network connectivity and coverage, relevance between activities, and the scope involved in context composition. Based on such features, contexts are categorized into three different levels: personal, regional, and global.