Fog/Edge/Cloud Computing in the Internet of Things
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 3607
Special Issue Editors
Interests: distributed systems; resource optimisation; Internet of Things; machine learning
Interests: network security; blockchain; machine learning; IoT
Special Issue Information
Dear Colleagues,
The Internet of Things (IoT) is driving a revolutionary transformation by introducing network connectivity into traditional devices, making objects from home appliances to industrial tools smart. This paradigm shift is empowered by various sensors with data collection, processing, and transmission capabilities, as well as powerful servers that provide computing and storage capabilities.
In recent years, fog/edge computing has been emerging as complementary computing paradigms that leverage computing and storage resources at the network edge to decrease the latency of artificial intelligence (AI) applications, protect data privacy, improve workload scheduling performance, etc. The combination of fog, edge, and cloud computing adds flexibility in the performance of computation or storage-intensive tasks (e.g., image/video recognition, content caching, anomaly detection). In IoT, users or service providers can choose to run different IoT application tasks at the edge for faster processing or upload them to the cloud infrastructures for more robust results based on specific requirements.
This Special Issue aims to address issues in the state-of-the-art fog/edge/cloud computing approaches and techniques applicable to the Internet of Things, providing cross-disciplinary ideas to address present and future challenges. Topics of interest include, but are not limited to, the following:
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Fog/edge/cloud computing-based IoT frameworks and architectures design.
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Distributed network communication protocols for fog/edge/cloud computing in IoT.
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Fog/edge/cloud computing for IoT data processing, modelling, and analysis.
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Workload scheduling in fog/edge/cloud computing-based IoT applications.
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Privacy preserving for fog/edge/cloud computing in IoT.
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Anomaly detection for fog/edge/cloud computing in IoT.
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Hardware-assisted design for fog/edge/cloud computing in IoT.
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Network traffic prediction and optimization for fog/edge/cloud computing in IoT.
Dr. Jia Hu
Prof. Dr. Hui Lin
Dr. Zi Wang
Guest Editors
Manuscript Submission Information
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Keywords
- fog/edge/cloud computing
- Internet of Things (IoT)
- workload scheduling and optimization
- network communication protocol
- data processing, modelling, and analysis
- anomaly detection
- network traffic prediction and optimization
- security and privacy in IoT
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