[hpc-announce] CFP = Frontiers in the Internet of Things Journal SI - Data Analytics in IoT-Edge-Cloud Systems

Rajkumar Buyya rbuyya at unimelb.edu.au
Sun Jun 18 02:31:19 CDT 2023

          Call for Papers
Frontiers in the Internet of Things Journal

SI - Data Analytics in IoT-Edge-Cloud Systems

Details at: 

Manuscript Submission Deadline 12 June 2023
Manuscript Extension Submission Deadline 12 July 2023

Edge computing has emerged as an important and timely paradigm for 
bringing cloud services closer to the data generation sources. Often, 
these data generation sources consist of numerous sensors that are 
connected to the internet, thus forming an IoT network. It becomes 
intuitive to study a hierarchical framework consisting of IoT-edge-cloud 
for data gathering, processing, and storage for a number of modern 

By definition, an IoT framework consists of billions of devices, 
generating massive amounts of data. There is a need to gather all this 
data from the various IoT devices, process it in a timely manner, and 
generate actionable insights, so that applications' Quality of Service 
(QoS) requirements may be met. One of the advantages of incorporating an 
edge network is that it accelerates data processing and analysis versus 
the case where only cloud data centers are employed. This is due to the 
fact that edge devices are located in close proximity to the data 
generating IoT devices. However, these edge devices offer limited 
computational and storage capabilities, as opposed to the distant cloud 
data centers. Hence, an interesting tradeoff exists between capacity and 
propagation delay offered by the edge devices and the cloud data center. 
This accelerated processing can be immensely useful to applications that 
are latency sensitive.

This Research Topic focuses on data analytics on IoT-edge-cloud 
hierarchical architectures. Topics include, but are not limited to the 
- Machine learning algorithms for data analytics on IoT-edge-cloud systems
- Federated machine learning on the edge
- Computational offloading of IoT data on edge-cloud systems
- Distributed partitioning of IoT workloads on edge-cloud systems
- Audio/Video processing on IoT-edge-cloud systems
- Stream data analytics on IoT-edge-cloud systems
- Distributed query processing
- Software for IoT-edge management
- Relevant applications/case studies for IoT-edge-cloud systems
- Privacy and security of edge-cloud systems

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