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Context Aware Computing for The Internet of Things: A Survey

arXiv 1305.0982 ↗First posted 5 May 2013cs.SE cs.HCcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2013.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 5; 2025: 4; 2026: 2.

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  1. 2024A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directionscited by 27
  2. 2025Xiaomi MiMo-VL-Miloco Technical Reportcited by 15
  3. 2024When IoT Meet LLMs: Applications and Challengescited by 11
  4. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
  5. 2024The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Settingcited by 7
  6. 2025A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysiscited by 5
  7. 2026SpeechLess: Micro-utterance with Personalized Spatial Memory-aware Assistant in Everyday Augmented Realitycited by 3
  8. 2021SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoTcited by 1
  9. 2021AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systemscited by 1
  10. 2023Lightweight Modeling of User Context Combining Physical and Virtual Sensor Datacited by 1
  11. 2021Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Thingscited by 0
  12. 2021Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation Systemcited by 0
6 more
  1. 2022From Traditional Adaptive Data Caching to Adaptive Context Caching: A Surveycited by 0
  2. 2022Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systemscited by 0
  3. 2024Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognitioncited by 0
  4. 2024Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Studycited by 0
  5. 2025Design of Cavity Backed Slotted Antenna using Machine Learning Regression Modelcited by 0
  6. 2026PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Managementcited by 0