Context aware computing for the Internet of Things: a survey

Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Zaslavsky, Arkady, Christen, Peter and Georgakopoulos, Dimitrios 2013. Context aware computing for the Internet of Things: a survey. Communications Surveys and Tutorials, IEEE Communications Society 16 (1) , pp. 414-454. 10.1109/SURV.2013.042313.00197

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Abstract

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1553-877X
Date of First Compliant Deposit:	10 August 2020
Date of Acceptance:	17 April 2013
Last Modified:	06 Nov 2023 13:43
URI:	https://orca.cardiff.ac.uk/id/eprint/134048

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