Topic Editors

Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Prof. Dr. Albert Zomaya
School of Information Technologies, Building J12, The University of Sydney, Sydney, NSW 2006, Australia
Communications Department, Universitat Politècnica de València, 46022 Valencia, Spain
Prof. Dr. David Bader
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Department of Complex Systems, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland
Dr. Maria Ganzha
Department of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, Warsaw, Poland
ENSIAS, Mohammed V University, Rabat 10130, Morocco
Prof. Dr. Sébastien Roland Marie Joseph Rndineau
Department of Electrical Engineering, University of Brasilia, Brasília 70910-900, Brazil
Dr. Sri Niwas Singh
ABV-Indian Institute of Information Technology & Management, Gwalior MP-474015, India
Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan

Towards Edge-Cloud Continuum

Abstract submission deadline
closed (31 October 2023)
Manuscript submission deadline
closed (31 January 2024)
Viewed by
2235

Topic Information

Dear Colleagues,

The Internet of Things (IoT) is beginning to bring about fundamental changes to all sectors of society and the economy. However, realization of the IoT vision requires data processing (stream, static, or both) to be in an “optimal location” within the edge–cloud continuum. Here, it is assumed that far-edge/nano-edge devices produce and actuate data; edge/fog applications consist of “heterogeneous intermediate devices”, where data can be processed, cloud/HPC facilities deliver “unlimited” processing capabilities, while all of them jointly (and supported by resources/services/data orchestration) constitute the edge–cloud continuum. In this context, future IoT deployments will have to manage processes as they materialize in multi-stakeholder, multi-cloud, federated, large-scale ecosystems.

Here, the key challenges within the field are related to the fact that it will be necessary to jointly leverage progress of multiple enabling technologies, e.g., 5G/6G networking, privacy and security, distributed computing, artificial intelligence, trust management, autonomous computing, distributed/smart applications, data management, etc. Moreover, intelligent (autonomous) orchestration of physical/virtual resources and tasks will have to be realized within the confines of the ecosystem (e.g., complete tasks in optimal location, close to where data is produced). To achieve this, among other components, frugal AI is needed to facilitate self-awareness and decision support across heterogeneous ecosystems. Finally, it is also absolutely necessary for resource management to consider the CO2 footprint of the ecosystem and efficiently deploy data and tasks in the case of multi-owner, heterogeneous sources of renewable energy.

In this context, this Topic entitled “Towards the Edge–Cloud Continuum” invites contributions addressing theoretical and practical aspects of the following topics (this list is, obviously, not exhaustive):

  • IoT architectures for domain-agnostic user-aware, self-aware, (semi-)autonomous edge–cloud continuum platforms, including proposals for novel decentralized topologies, ad hoc resource federation, and time-triggered behaviors
  • Foundations for the next generation of higher-level (meta) operating systems, facilitating efficient use of computing capacity across the edge–cloud continuum
  • Resource-aware AI, including frugal AI, bringing intelligence to the edge–cloud continuum platforms (and ecosystems)
  • Cognitive frameworks leveraging AI techniques to improve the optimization of infrastructure usage and services and resource orchestration
  • Efficient streaming of big data processing within large-scale IoT ecosystems
  • Interoperability solutions for multi-user edge–cloud continuum platforms, capable of coping with systematically increasing complexity of connecting vast numbers of heterogeneous devices
  • Federated data spaces approach for improved data governance, sovereignty and sharing
  • Privacy, security, trust and data governance in competitive scenarios
  • CO2 footprint reduction and efficient use of green energy in edge–cloud continuum ecosystems
  • Practical aspects of resource and services orchestration within highly heterogeneous, large-scale edge–cloud continuum ecosystems
  • Intent-based networking and its application to IoT, which will then be applied to the continuum
  • Swarm intelligence for IoT’s edge–cloud continuum
  • Data autonomy and data governance
  • Data sovereignty and data economy

Dr. Marcin Paprzycki
Prof. Dr. Albert Zomaya 
Prof. Dr. Carlos Enrique Palau Salvador
Prof. Dr. David Bader
Dr. Marek Bolanowski
Dr. Maria Ganzha
Prof. Dr. Mohamed Essaaidi 
Prof. Dr. Sébastien Roland Marie Joseph Rndineau
Dr. Sri Niwas Singh 
Dr. Yutaka Watanobe
Topic Editors

Keywords

  • edge computing
  • cloud computing
  • edge–cloud continuum
  • continuum architecture
  • practical aspects of continuum implementation
  • intent-based networking
  • federated data spaces
  • data autonomy, governance, sovereignty and economy
  • efficient realization of the edge–cloud continuum
  • privacy, security and trust in edge–cloud continuum ecosystems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
2.3 3.7 2008 15 Days CHF 1600
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400
Information
information
3.1 5.8 2010 18 Days CHF 1600
Network
network
- - 2021 18.2 Days CHF 1000
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600

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Published Papers (1 paper)

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24 pages, 3038 KiB  
Article
Data Fusion for Cross-Domain Real-Time Object Detection on the Edge
by Mykyta Kovalenko, David Przewozny, Peter Eisert, Sebastian Bosse and Paul Chojecki
Sensors 2023, 23(13), 6138; https://doi.org/10.3390/s23136138 - 4 Jul 2023
Cited by 1 | Viewed by 1065
Abstract
We investigate an edge-computing scenario for robot control, where two similar neural networks are running on one computational node. We test the feasibility of using a single object-detection model (YOLOv5) with the benefit of reduced computational resources against the potentially more accurate independent [...] Read more.
We investigate an edge-computing scenario for robot control, where two similar neural networks are running on one computational node. We test the feasibility of using a single object-detection model (YOLOv5) with the benefit of reduced computational resources against the potentially more accurate independent and specialized models. Our results show that using one single convolutional neural network (for object detection and hand-gesture classification) instead of two separate ones can reduce resource usage by almost 50%. For many classes, we observed an increase in accuracy when using the model trained with more labels. For small datasets (a few hundred instances per label), we found that it is advisable to add labels with many instances from another dataset to increase detection accuracy. Full article
(This article belongs to the Topic Towards Edge-Cloud Continuum)
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