Special Session at the European Signal Processing Conference (EUSIPCO) 2022

Scope and Topics of Interest

To harness the power of vast amount of real-time data streams from smart cities applications, Edge-to-fog-to-cloud (E2F2C) processing has emerged as a novel paradigm where the processing of data occurs at each of the three architectural tiers – edge, fog and cloud, and also “en-route” at the participating devices along a given E2F2C data path. To achieve this in practical applications, in-depth studies and novel approaches are needed on the interface between machine learning and deep learning, the underlying hardware - accounting for the emergent and powerful edge processing devices such as edge GPUs, and the large-scale software orchestration relying on resource virtualization.

The special session seeks original contributions and review papers in, but not limited to, the following topics:

• Distributed machine learning

• Federated learning

• Just-in-time deep learning models (e.g. early exiting, dynamic computation graphs)

• Collaborative Edge Computing with machine/deep learning

• E2F2C offloading mechanisms

• Resource-efficient ML/DL at the edge

• Machine Learning for Internet of Things

• Multi-modal data analysis (e.g. visual, audio, sensor signals)

• Applications of machine learning for smart city analytics and decision making

The aim of this special session is to bring together and disseminate state-of-the-art research contributions that address E2F2C processing in the context of smart cities, including the analysis and design of novel algorithms and methodologies, innovative smart cities applications with E2F2C processing, and enabling technologies, etc. Please consider to submit your latest research in the topic.

The Special Session is supported by the EU H2020 project Multimodal Extreme Scale Data Analytics for Smart Cities Environments (MARVEL) under GA No 957337.

For inquiries concerning this Special Session please feel free to contact us at ai [at] ece.au.dk or dbajovic [at] uns.ac.rs