Context Aware Computing for The Internet of Things: A Survey (1305.0982v1)

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.

• The paper presents a comprehensive survey of 50 IoT projects to evaluate context-aware computing methods and highlight future research prospects.
• It details a four-phase context life cycle—acquisition, modelling, reasoning, and distribution—that enhances processing of raw sensor data.
• The study underscores the importance of robust middleware and hybrid approaches to support dynamic sensor integration and advanced context reasoning.

Context Aware Computing for the Internet of Things: A Survey

This paper provides a comprehensive survey of context-aware computing in the field of the Internet of Things (IoT). The authors systematically review various projects, systems, toolkits, and middleware to synthesize lessons learned and identify future research opportunities.

Context-awareness, integral to pervasive and ubiquitous computing systems since the 1990s, has transitioned into the IoT domain, leveraging context to add significant value to raw sensor data. This survey particularly covers the decade from 2001 to 2011, analyzing 50 projects using a taxonomy developed by the authors.

Context-awareness in IoT

As sensors proliferate, generating enormous data volumes, processing all this data becomes infeasible. Context-aware computing addresses this challenge by automating the detection, reasoning, and distribution of relevant data, thereby enhancing the capability of IoT systems to interact meaningfully with their environments. Here are some core aspects addressed in the paper:

Context Life Cycle

The context life cycle elucidated in the paper consists of four key phases:

1. Context Acquisition: Involves extracting data from physical, virtual, or logical sensors using techniques such as push/pull data retrieval or periodic event-based monitoring.
2. Context Modelling: Techniques for structuring context data include key-value pairs, markup schemes like XML, graphical models such as UML, object-oriented models, logic-based models, and ontologies. Each method has its strengths and weaknesses regarding complexity, scalability, and expressive richness.
3. Context Reasoning: Methods for deriving high-level context from low-level data include supervised learning, unsupervised learning, rules, fuzzy logic, ontological reasoning, and probabilistic logic. A hybrid approach integrating multiple methods often provides a more robust solution.
4. Context Distribution: Consists of mechanisms like queries and publish/subscribe models to disseminate context data to interested consumers.

Analyzed Projects

The survey examines 50 significant projects, evaluating them based on several criteria including context modelling, reasoning, distribution, architecture, history and storage capabilities, knowledge management, event detection, and data processing, among others.

Key Insights and Lessons Learned

1. Toolkit vs. Middleware: The distinction between toolkits and middleware highlights the need for the latter to support broader, scalable IoT applications.
2. Hybrid Approaches: Successful context-aware systems often integrate multiple reasoning and modelling techniques to leverage each method's strengths.
3. Mobility Support: Context-aware systems need to support continuity across different environments and devices.
4. Automated Configuration: Future IoT systems must incorporate automated or semi-automated processes for sensor configuration and context discovery, crucial for the scalability of IoT systems.
5. Dynamic Composition: Systems must support the dynamic integration of new sensors and context sources to adapt to the IoT's evolving landscape.
6. Inter-middleware Communication: Context sharing across different middleware instances is essential, given the decentralized nature of the IoT.

Future Research Directions

The paper identifies critical areas for future exploration:

1. Automated Sensor Configuration: Leveraging standards like TEDS and SensorML for automatic sensor integration.
2. Advanced Context Discovery: Utilizing semantic technologies and linked data for more efficient context annotation and discovery.
3. Broader Applicability of Context-aware Techniques: Developing frameworks that facilitate the integration of diverse modelling and reasoning techniques.
4. Selection and Quality of Sensors: Establishing frameworks for sensor selection based on parameters like accuracy, cost, and relevancy.
5. Enhanced Security and Privacy: Addressing multifaceted security and privacy concerns across all layers of the IoT stack.
6. Efficient Context Sharing: Enabling seamless context information exchange between different middleware solutions.

Conclusion

This survey methodically consolidates the state of context-aware computing in IoT, underscoring the necessity of robust middleware solutions capable of dynamic configuration, real-time processing, and multi-faceted reasoning. By drawing on a decade of research, it offers crucial insights and directives for advancing the integration of context-awareness in the IoT, aiming to create intelligent environments responsive to users' needs and conducive to a connected world.