Masterarbeit MSTR-2017-49

Khan, Muhammad Arsalan: Designing a context-aware discovery service for IoT devices.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 49 (2017).
133 Seiten, englisch.

Internet of Things (IoT) is in a continuous expansion phase, from millions of devices to tens of billions in upcoming years, which will have major impacts on infrastructure, business models, and industry standards throughout the entire IT ecosystem. It is expected that several diverse devices to invade by 2020. Depending on different application domains, IoT applications require devices, sensors, middlewares, networks and other enabling technologies to be integrated e.g., the high-level central control of IoT applications can be deployed on the cloud while others are running close to the "edge", forming a unified, scalable and feasible system. One of the important integration aspects in IoT ecosystem is discovering devices and sensors based on a particular context regardless of their heterogeneity. In this thesis, we propose Context-Aware Discovery Service for Internet of Things (CADsIoT) that deals with devices and sensors installed in IoT environments, streamlining the process of registration, management and dynamic discovery of devices based on contextual information. CADsIoT allows device and sensor registration, attaching them with particular context and leverage subscription features to enable dynamic discovery based on the attached context. Additionally, real-time notifications are triggered when new devices are discovered. For the validation of our concept, we discuss the requirements and a descriptive motivation scenario, which is followed by a discussion of the prototypical implementation. The prototype consists of CADsIoT Core, a Representational State Transfer (REST) based middleware and Navigator, an Android mobile application as a client.

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Abteilung(en)Universität Stuttgart, Institut für Architektur von Anwendungssystemen
BetreuerLeymann, Prof. Frank; Breitenbücher, Dr. Uwe; Képes, Kálmán
Eingabedatum29. Mai 2019
   Publ. Institut   Publ. Informatik