Master Thesis MSTR-2019-35

BibliographyHill, Stefan: Scalable IoT platforms.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 35 (2019).
141 pages, english.

In today’s world the internet is connecting not only people but things. The computing concept of the ”Internet of Things (IoT)”describes the idea to connect everyday physical objects to the internet. IoT platforms provide the backbone for applications in areas like Smart Home, Connected Vehicles and Industrial IoT. In this thesis, we explore the question of the best IoT platform with a focus on reliability, scalability and heterogeneity. To answer this question, we search the market for IoT platforms, prototypes and proposals, examine them based on our comparison model and rate the platforms in a five star system. The criteria for the parts of the comparison model include replication, availability, authentication and authorization, encryption, security incidents and audits, development and market longevity for reliability, hosting, Edge and Fog Computing, limits of the infrastructure and network and load balancing for scalability, device restrictions, messaging and device protocols, programming languages and flexibility for heterogeneity as well as usability, pricing models and unique selling points. We discover that most criteria do not differ in the used technologies or algorithms, but if they is implemented or not. Despite there is a low level of standardization, most criteria is implemented in a similar way across the platforms. The overall best rated platform is Microsoft Azure IoT Hub with 4.25 out of 5.0 stars, followed by IBM Watson IoT (3.88 stars). The prototype platform OceanConnect by Huawei shows promising results as well (3.0 stars).

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Aiello, Prof. Marco; Georgievski, Dr. Ilche
Entry dateAugust 7, 2019
   Publ. Computer Science