The Applications of Deep Neural Network in Edge Computing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 2164
Special Issue Editors
Interests: data science; edge computing; digital health
Special Issue Information
Dear Colleagues,
The evolution of Deep Neural Networks (DNNs) has revolutionized various domains of artificial intelligence, from image recognition to natural language processing. When coupled with the expanding field of Edge Computing, where data processing occurs closer to the data source rather than in distant data centers, DNNs present a transformative potential. Edge Computing aims to reduce latency, preserve bandwidth, and upgrade privacy and security by processing data locally. The integration of DNNs into Edge Computing environments enables real-time, efficient, and intelligent decision-making in numerous applications, ranging from autonomous vehicles to smart city infrastructure. This convergence is particularly crucial as the Internet of Things (IoT) era matures, demanding more sophisticated, decentralized computing paradigms.
This Special Issue aims to explore the cutting-edge developments, existing challenges, and future directions regarding the deployment of Deep Neural Networks in Edge Computing scenarios. It will highlight innovative research that showcases how DNNs can enhance the capabilities of Edge Computing devices, making them smarter, more efficient, and capable of autonomous decision-making. This Special Issue aligns with the journal's scope, addressing novel technological advancements in computer science, and focusing on real-world applications, theoretical challenges, and the synergy between emerging computing paradigms and artificial intelligence.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Deep Neural Networks (DNNs) in IoT: Applications and challenges of implementing DNNs in various IoT scenarios.
- Optimization of DNNs for Edge Devices: Techniques and strategies for optimizing DNN architectures for resource-constrained Edge devices.
- Edge-based AI Services: Development of AI-driven services and applications powered by DNNs at the Edge.
- Real-time Analytics: Implementing DNNs for real-time data processing and decision-making in Edge environments.
- DNNs for Autonomous Systems: Use of DNNs in Edge Computing for autonomous vehicles and robotics.
- Energy-efficient Deep Learning: Approaches for reducing the power consumption of DNNs in Edge Computing.
- Privacy-preserving Techniques for DNN: Utilizing DNNs at the Edge for enhancing data privacy and security.
- Integration Challenges: Addressing the challenges in integrating DNNs with existing Edge Computing infrastructures.
Dr. Samiya Khan
Dr. Korhan Cengiz
Guest Editors
Manuscript Submission Information
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Keywords
- deep neural networks
- edge computing
- Internet of Things (IoT)
- real-time data processing
- AI optimization for edge
- autonomous decision-making
- privacy and security in AI
- energy-efficient machine learning
- AI services at the edge
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