RAIS

RAIS aspires to provide for 14 Early Stage Researchers (PhD students) a world-class training within a broad spectrum of subjects establishing a fertile inter-disciplinary research and innovation community that will advance:

• Wearable Technology: Wearable Sports Sensing and Quantified-self Devices and Accompanying Middleware memory
• Block-chain Powered IoT: Decentralized Block-chain Powered IoT Platforms (generating hundreds of billions of transactions per day) for Big Data Mining
• Real-time Edge Analytics: Real-time Edge Analytics And Predictive Modelling To Capture A Broad Range Of Sports-related Data And Trends (e.g., activities and contextual information), Critical In A Variety Of Application Settings.

Research areas:

• Distributed Sensing Infrastructure & Networking for Internet of Sports:
  This research area aims at designing a distributed and decentralized platform for efficiently capturing, monitoring, storing and sharing data from quantify-self sensors, mobile phones and other wearable devices. This research area will be the foundation of the RAIS infrastructure.
• Security, Privacy, and Trust for Wearable Devices:
  Naturally, sensors and devices of RAIS infrastructure will collect, process, and store sensitive information of a very personal nature such as health signals, daily habits, places visited, people communicated with, etc. Thus, it is of paramount importance to develop mechanisms for protecting the integrity of both the devices and the collected information against what users might perceive as unauthorized use, which is the main goal of this research area. Furthermore, privacy protection mechanisms are explored, including novel algorithms for risk assessment, access control and policy enforcement, as well as algorithms for trust management and privacy preserving data sharing.
• Data Mining and Edge Analytics for Sports and Wellbeing:
  Within this research area we focus on developing new technologies on Big Data Analytics on the Edge, Data Stream Processing, as well as Graph streaming and Distributed and Decentralized privacy preserving Machine Learning algorithms.
• Predictive Analytics for Internet of Sports Knowledge Extraction:
  This research area focuses on developing real-time predictive analytics and feedback applications combined with the development of a wellbeing framework for Internet of Sports. It also explores the gender differences in long-term habit formation in exercise, revealing the gender aspects in social influence in exercise.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement Innovative Training Networks (ITN) RAIS No 813162