Context Aware Computing for The Internet of Things: A Survey

By Charith Perera, Arkady B. Zaslavsky, Peter Christen, D. Georgakopoulos. 2013

2,775 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Therefore, IoT data needs to be fused with other sources of data, namely context information that complement the understanding of the environment [12].”
Fog Computing for Sustainable Smart Cities (2017, methodology): “According to the literature comparison presented in Table III, context discovery and awareness [Perera et al. 2014a] and semantic annotation is fairly ignored.”
Event-triggered communication and control of networked systems for multi-agent consensus (2017, background): “Given these new-age design considerations, it is not surprising that event-triggering has recently been gaining a lot of traction as a promising paradigm for addressing the issues above (Ko-lios et al., 2016; Perera, Zaslavsky, Christen and Georgakopoulos, 2014).”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, background): “The most popular classiﬁcation of context modeling is deﬁned as key-value, markup scheme, graphical, object oriented, logic-based, ontology-based, spatial, uncertainty, and hybrid context modeling [21], [23], [26], [98], [99].”
Next Generation 5G Wireless Networks: A Comprehensive Survey (2016, background): “Six unique challenges [140] of IoT include (i) Automated sensor configuration, (ii) context discovery, (iii) acquisition, modeling and reasoning (iv) selection of sensors in ‘sensing-as-a-service’ model [140] (v) securityprivacy-trust and (vi) context sharing.”
Mobile Big Data: Concepts and Features Mobile Big Data Analytics Using Deep Learning and Apache Spark (background): “For example, an activity recognition application [3] [5] uses embedded accelerometers of mobile devices to collect proper acceleration data about daily human activities.”
Internet of Things in Industries: A Survey (2014, background): “The data may not have much meaningful value unless people find an effective way to analyze and understand it [62].”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “When they are integrated with conventional networks services, they cause regression in the security of conventional networks [72,73].”
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey (2015, neutral): “The term was ﬁrst coined in 1998 and later deﬁned as “The Internet of Things allows people and things to be connected Anytime, Anyplace, with Anything and Anyone, ideally using Any path/ network and Any service” [1].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, methodology): “Therefore, various context-aware computing methods have been proposed for IoT [54].”
A roadmap for security challenges in the Internet of Things (2017, background): “In “ Context Aware Computing for The Internet of Things: A Survey ” [10], the authors proposed a context awareness for the IoT framework and gave a deep provided an in-depth analysis of the context life cycle (techniques, methods, models, functionalities, systems, applications, and middleware…”
A Survey on Internet of Things From Industrial Market Perspective (2015, background): “Therefore, it is ideal to have a tool that is capable of evaluating a given software component (as a self-contained algorithm that may take primary context information as inputs and outputs secondary context information by using any kind of data reasoning technique [2]) against a given computational”
Future Trends and Current State of Smart City Concepts: A Survey (2020, background): “IoT covers Internet-based technologies together with user needs or user applications, which means with IoT devices, even larger concepts of automation systems could be controlled based on the priorities of the users [79], [80].”
How-to conduct a systematic literature review: A quick guide for computer science research (2022, background): “• [13] presented a context-awareness survey using the stages of context-aware lifecycle to review different methods.”