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Abstract
The advent of the Internet of Things (IoT) and the concomitant development of smart systems has rendered context-aware computing an emerging field of research. The IoT facilitates the large-scale integration of Machine-to-Machine (M2M) communication systems, largely independent of human intervention. The context of a situation, encompassing factors, such as mood, location, and activity, is typically taken into account by humans in an implicit manner, influencing their subsequent actions. Similarly, IoT based smart systems require context data acquired through the use of sensors. The primary challenge lies in the adaptation of context information through the proper modeling and analysis of the vast and heterogeneous sensor data. The phases of context acquisition, modeling, reasoning, and dissemination are collectively referred to as the context management life cycle. The principal aim of this paper is to provide a comprehensive overview of the current state of the art in each phase of the context management life cycle. This study presents a comprehensive review of the tools, techniques, algorithms, and architectures documented in the relevant literature, with a focus on research papers and articles published between 2010 and 2024. The discussion and open issues section at the end of the paper offer insights for future researchers engaged in the study, development, implementation, and evaluation of techniques and approaches for context management in IoT.
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