Tutorial Title: Pervasive AI for IoT Applications

Amr Mohamed, Qatar University, Qatar

Aiman Erbad, Hamad B. Khalifa University, Qatar

Ahmed Refaey, Manhattan College, USA

Date/Time: Monday, June 28, 2020 - 9:00 am, Room: TBA

Abstract: Traditional cloud-based IoT architectures suffer from many issues, including scalability, communication and computational efficiency, in addition to privacy. This motivated the need for new emerging trends such as Edge, Fog, and Pervasive Computing, where we merge hierarchical computing with efficient communication, leveraging learning-based distributed optimization, in order to resolve many of the issues highlighted above.

In this tutorial, we will highlight the motivation behind pervasive AI models for Internet of Things (IoT) applications, and cyber-physical systems (CPS), in light of traditional cloud-based architectures. We will discuss state-of-the-art contributions we have recently published regarding distributed inference/classifications in IoT, and multi-drone systems, taking into consideration privacy and mobility of network users. We will also cover recent contributions regarding distributed learning scenarios using multi-agents and federated learning architectures that address heterogeneous user data to improve the learning performance, and outcomes in distributed networks.