We have a special session on Edge-Fog-Cloud Machine Learning for Smart Cities Applications at the European Signal Processing Conference (EUSIPCO) 2022! Deadline for paper submission is February 20, 2022

More information under: https://sites.google.com/view/e2f2c-ml4smartcities

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.

Organizers

• Yiming Wang, Fondazione Bruno Kessler, Italy
• Dragana Bajovic, University of Novi Sad, Serbia
• Lukas Esterle, Aarhus University, Denmark
• Alexandros Iosifidis, Aarhus University, Denmark
• Lorenzo Valerio, National Research Council, Italy

We are organizing a special session on Deep Learning for visual, audio, and sensor data analysis in Smart City environments at at the International Joint Conference on Neural Networks 2022 (IJCNN-SS-1) in conjunction with IEEE World Congress on Computational Intelligence (WCCI) 2022

Organizers: Alexandros Iosifidis, (Aarhus University) and Lukas Esterle, (Aarhus University)

Submission Deadline: January 31st, 2022 (11:59 PM AoE) via Submission – WCCI2022

Scope and Topics of Interest

Recent advances in Deep Learning and high-performance computing led to remarkable solutions for visual, audio, sensor, and multi-modal data analysis problems. Deep Learning-empowered systems can nowadays achieve performance levels in various data analysis tasks which are comparable to, or even exceeding, those of humans. Even though these advancements have the potential to open new high-impact applications in Smart City environments, this promise has yet to be met. This is due to challenges in Smart City environments which go beyond the unrestricted analysis of visual, audio, and sensor data provided by Deep Learning models when run on high-end Graphics Processing Units (GPUs). The large number of sensors (like cameras, microphones, thermometers, motion sensors, etc.) available in such environments leads to the enormous size of collected data needed for effective data analytics. Rapid response and privacy requirements prohibit transfer and processing on powerful serves and require processing on the edge. However, with processing infrastructure setting restrictions in terms of processing power, battery/electric power consumption, and autonomy (embedded GPUs or low-end processors used in edge and fog computing), efficient high-performing Deep Learning models, as well as effective data fusion schemes, are required. This goes beyond the current capabilities of the state-of-the-art. Thus, novel efficient solutions are needed to successfully employ high-performing Deep Learning models in such processing platforms.

For fully exploiting Deep Learning solutions in Smart City environments setting restrictions in processing power, memory consumption, hard real-timeness, handling uncertainties in the processing outcomes, and requiring a level of interpretability, a number of challenges need to be addressed through theoretical and methodological contributions, including but not limited to:

• Lightweight Deep Learning models for visual, audio, sensor data analysis
• Deep Learning models for efficient multimodal data analysis and fusion
• Sensor time-series analysis based on Deep Learning
• Efficient Deep Learning methodologies for Internet of Things
• Deep Learning methodologies for smart cities, including Federated Learning, Transfer Learning, Domain Adaptation, Split Computing
• Deep Learning for applications in smart city environments, including smart homes, smart lighting, traffic prediction, data anonymization for visual analysis, intelligent transportation systems, vehicular networks

The Special Session will be a forum to exchange ideas and to discuss new developments in Deep Learning for visual, audio, and sensor data analysis in Smart City environments. Please consider to submit your latest research in the topic.