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About this Research Topic

Abstract Submission Deadline 10 March 2023
Manuscript Submission Deadline 12 June 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 applications.

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 following:
- 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

Keywords: Data Analytics, Edge Computing, Cloud Computing, Machine Learning, Computational Offloading


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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 applications.

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 following:
- 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

Keywords: Data Analytics, Edge Computing, Cloud Computing, Machine Learning, Computational Offloading


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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