Master Thesis MSTR-2020-08

BibliographyKumar, Abhishek: An Analytics Framework for the IoT Platform MBP.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 8 (2020).
69 pages, english.

The emergence of IoT has introduced a huge amount of applications that generate massive amounts of data at a high rate. This data stream needs intelligent data processing and analysis. The evolution of Smart cities and Smart industries has resulted into an ocean of data from millions of sensors and devices. Surveillance systems, telecommunication systems, smart devices, and smart cars are some examples of such systems. However, this data itself doesn’t provide any information unless it is analysed. This results into a need of analytics tools and frameworks which can efficiently analyse this data and provide with useful information. Analytics is all about inspection, transformation and modelling of data to achieve information that further suggests and assists in decision making. In a world of IoT, analytics has a crucial role to play to improve life and better manage the infrastructure in a secure, sustainable and cost effective manner. The smart sensor network serves as the base for IoT. In this context, one of the major tasks is to develop advanced analytics frameworks for the interpretation of data provided by the sensors. MBP is a platform for managing IoT environments. Sensors and devices can be registered to this platform and the status of sensors can be viewed and modified from the platform. This platform will be used to collect data from the sensors and devices connected to the platform. There are two types of mining that can be performed on raw data, one technique analyses the data on the fly as it is received (Data Stream Mining) and the other can be performed on demand on the data collected for a longer period of time (Batch Processing). Both types of analysis has its own advantages. Lambda architecture is a data analytics architecture which allows us to perform both stream analysis and batch processing on the same data. This architecture defines some practical and well versed principles of handling big data. The pattern allows us to deal with both real time and historical data, but the analysis is performed separately and does not affect each other. In this thesis, we will create an analytics framework for the MBP IoT platform based on the lambda architecture.

Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Superviser(s)Schwarz, PD Dr. Holger; Hirmer, Dr. Pascal
Entry dateJuly 20, 2020
   Publ. Computer Science