The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller-Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.
The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several e↵orts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among di↵erent devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios. IntroductionIn the last few years, the coupling of networking communication capabilities and devices with disparate characteristics and capabilities (e.g., sensors, actuators) has prompted di↵erent actors (ranging from academia, to service providers, manufacturers and operators) into the development of solutions towards an Internet of Things (IoT). These solutions are able to remotely exploit the sensing and actuating capabilities of such devices and convey them into communicating and processing platforms, empowering di↵erent kinds of "smart" scenarios [1,2]. The added value generated by bridging the physical and digital worlds has contributed to a continuously increasing massification of connected devices and generated information exchanges ([3] indicates 7.3 billion Machine-to-Machine (M2M) networked devices by 2018, globally), raising connectivity provisioning and operation concerns at all levels. The stringent new requirements placed over the underlying networking fabric to support this connectivity explosion have prompted the need for ground-breaking ideas and solutions, able not only to support these challenges, but also to confer the capability and flexibility to better face future challenges and requirements.Information Centric Networking (ICN) [4,5] is an emerging networking paradigm that has content at the centre of the networking functions, shifting from the curre...
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