AI-Driven Optimization for Sustainable Edge-Cloud Continuum
This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Special Issue Information
Dear Colleagues,
The rapid proliferation of Internet of Things (IoT), 5G/6G networks, and data-intensive applications has accelerated the evolution of the edge-cloud continuum, where computation seamlessly spans from resource-constrained edge devices to powerful cloud infrastructures. While this paradigm enables low-latency services and scalable intelligence, it also introduces significant challenges in terms of energy consumption, resource allocation, system efficiency, and sustainability.
Artificial Intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a transformative enabler for optimizing the edge-cloud continuum. AI-driven approaches can dynamically manage resources, improve energy efficiency, enhance service quality, and support sustainable computing infrastructures.
This Special Issue aims to bring together cutting-edge research and innovative solutions that leverage AI to optimize the performance, efficiency, and sustainability of edge-cloud systems. We invite original research articles, surveys, and application-driven studies addressing theoretical advancements, system architectures, and real-world deployments.
Scope and Topics
Topics of interest include, but are not limited to:
- AI for Resource Optimization
- AI-based task scheduling and workload allocation
- Intelligent resource provisioning in edge-cloud environments
- Reinforcement learning for dynamic orchestration
- Federated learning for distributed optimization
- Energy Efficiency and Sustainability
- Green AI for edge-cloud systems
- Energy-aware computing and scheduling
- Carbon footprint reduction in distributed infrastructures
- Renewable energy integration in edge/cloud computing
- Edge Intelligence and Distributed AI
- Collaborative intelligence between edge and cloud
- Model partitioning and split learning
- On-device AI and lightweight models
- Privacy-preserving AI in edge environments
- Network Optimization and Communication Efficiency
- AI-driven network slicing and traffic management
- Adaptive communication protocols
- Latency-aware optimization strategies
- 5G/6G-enabled edge-cloud systems
- Security, Privacy, and Trust
- Secure AI frameworks for edge-cloud continuum
- Privacy-preserving data analytics
- Trust management and anomaly detection
- Blockchain-enabled edge-cloud systems
- Applications and Use Cases
- Smart cities and intelligent transportation
- Industrial IoT and Industry 4.0
- Healthcare and remote monitoring
- Autonomous systems and robotics
- System Architectures and Frameworks
- Edge-cloud orchestration platforms
- Serverless and microservices in edge-cloud systems
- Digital twins for system optimization
- Benchmarking and performance evaluation
Prof. Dr. Zhou Zhou
Prof. Dr. Jemal Abawajy
Prof. Zhongsheng Wang
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- edge computing
- artificial intelligence
- federated learning
- resource optimization
- task scheduling
- energy efficiency
- 5G/6G networks
- IoT systems
- privacy-preserving computing
- secure edge computing
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Published Papers
Algorithms - ISSN 1999-4893