# Context Aware Computing for The Internet of Things: A Survey

arXiv: https://arxiv.org/abs/1305.0982 · HTML: https://mlsanity.com/paper/1305.0982/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2013.

## Built on by (most cited first, 18 of 18)

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- [Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation System](https://mlsanity.com/paper/2107.14374/) (2021)
- [From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey](https://mlsanity.com/paper/2211.11259/) (2022)
- [Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems](https://mlsanity.com/paper/2212.11709/) (2022)
- [Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition](https://mlsanity.com/paper/2404.11742/) (2024)
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## Abstract (arXiv)

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.