Context-aware Computing in the Internet of Things: A Survey on Internet of Things From Industrial Market Perspective (1502.00164v1)

Abstract: The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communication and computing has played a critical role throughout the last few years of ubiquitous computing and is expected to play a significant role in the IoT paradigm as well. In this article, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for contextaware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.

• The paper presents a comprehensive evaluation of industrial IoT solutions using a novel taxonomy based on context-aware computing frameworks.
• It reveals significant market trends, including projections of 50-100 billion connected devices by 2020 across various sectors.
• The study emphasizes challenges like interoperability and energy management, advocating for standard frameworks and improved user-context recognition.

Overview of "Context-aware Computing in the Internet of Things: A Survey on Internet of Things From Industrial Market Perspective"

This paper provides a comprehensive survey of context-aware computing within the Internet of Things (IoT) from an industrial perspective. The authors examine various IoT solutions, highlighting their application in context-aware communication and computing, a critical driver of the IoT paradigm. The survey addresses the lack of extensive reviews on industrial IoT solutions, contrasting with the academic focus of past surveys.

Key Contributions

The paper systematically evaluates IoT technologies using a conceptual framework based on established context-aware computing theories. It introduces an evaluation taxonomy encompassing features of context-aware tagging, selection, presentation, and execution. This approach allows the authors to dissect how IoT products across various categories—such as smart wearables, homes, cities, environments, and enterprises—integrate context-aware functionalities. The taxonomy aids in understanding data flow, sensor implementation, user interaction, notification mechanisms, and machine learning capabilities in these products.

Notable Results and Claims

1. Market Trends: IoT market growth is underscored by substantial statistics and predictions. The paper estimates that by 2020, 50 to 100 billion devices will connect to the Internet, surpassing human population ratios. This growth spans sectors like smart homes and cities, which significantly contribute to economic projections.
2. Context-aware Features: Many IoT products focus on data visualization and user interface design, often relying on traditional screen-centric interactions. There's a noted trend towards utilizing machine learning to enhance IoT devices' intelligence and predictive capabilities.
3. Interoperability Challenges: The survey emphasizes the importance of interoperability for IoT solutions, facilitated by industry standards and alliances like the AllSeen Alliance. The authors point to existing obstacles created by proprietary protocols and propose interoperability as a critical enabler for market scaling and innovation.

Implications for Future Research

1. Evolving Frameworks: The paper suggests developing advanced analytical frameworks that are domain-independent to accommodate diverse IoT applications. Such frameworks would support cloud-based and localized data processing, enhancing the development of intelligent solutions.
2. User and Context Recognition: Further work is needed to improve IoT products' ability to recognize individual users within a shared environment, adapting functionalities to personal preferences and behaviors.
3. Energy Management: Innovative solutions for comprehensive energy usage planning in smart environments remain an untapped opportunity. Future research should consider creating simulation tools for optimizing deployment and operation of IoT devices.

Conclusion

By evaluating over 100 IoT solutions, this survey provides valuable insights into the integration of context-aware capabilities in the marketplace. It highlights significant gaps in current research, particularly regarding industrial adoption, and proposes avenues for enhanced device interoperability and energy efficiency. These findings serve as a guideline for further advancements and standardization efforts within the IoT ecosystem, fostering a more interconnected, contextually aware network of devices and systems.