Open Data Platforms for the Industrial Internet of Things (IoT)

Pervasive Technologies
Thesis Code: 

Thesis Type: 5-6 months Master Thesis (Laurea Magistrale) for Computer Science, Computer Engineering students or related.

General Knowledge of Database Technologies, Programming in Distributed Environments. Good design and programming skills in Java are required. Previous interest/experience in developing IoT applications and/or working with Big Data systems or Machine Learning libraries e.g. TensorFlow is a plus.

The Internet-of-Things (IoT) is a vision in which every physical object – enriched with communication capabilities – acquires an electronic identity and acts as a source of information. The rapid uptake of IoT technologies in different application domains is causing a tremendous increase in the amount of data being collected, stored and processed, above all in Industrial Manufacturing and Process Industries domain. In order to handle the increasing amount of data in sustainable fashion, many industrial players are deploying scalable data platforms based on open technologies, both using in-premises or cloud-oriented systems.

The student(s) will be involved in analyzing, designing and deploying a reference platform for real-time processing of IoT data built upon open-source technologies and/or cloud-based platforms, responding to requirements derived from real industrial scenario. Selected student(s) will be involved in the development of a proof-of-concept Machine Learning application working over large, real-time data flows. The proposed Machine Learning application will be deployed and evaluated using the proposed platform.

Note: this thesis project can also be potentially developed by a team of 2 or 3 students, each focusing on separate aspects of the problem (Algorithms, Data Platforms, etc.). Students will be jointly tutored by researchers of the ACE and PerT Areas.

Contacts: send a resume specifying the thesis code and title to and/or