The recent advancements in the research area of cyber-physical systems (CPS) and edge computing are significantly pushing the boundaries of intelligent and automated systems. A notable trend is the integration of multi-access edge computing (MEC) with space-air-ground architectures, enabling more deterministic and efficient resource allocation in industrial settings. This approach not only enhances the quality of service (QoS) for Internet of Things (IoT) devices but also addresses the complexities and uncertainties inherent in dynamic environments. Additionally, the use of deep reinforcement learning (DRL) and generative AI models is becoming prevalent for optimizing resource management and service function chain (SFC) provisioning in 5G core networks, demonstrating superior performance in terms of acceptance ratios, end-to-end delays, and throughput maximization. Furthermore, the development of adaptive and distributed frameworks for UAV-based multi-task processing and edge caching strategies is addressing the challenges of resource-constrained, infrastructure-less environments, significantly improving task efficiency and response times. These innovations collectively underscore a shift towards more intelligent, decentralized, and adaptive systems that can dynamically respond to changing network conditions and service demands.
Intelligent and Adaptive Systems in Cyber-Physical and Edge Computing
Sources
Space-Air-Ground Integrated MEC-Assisted Industrial Cyber-Physical Systems: An Online Decentralized Optimization Approach
Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments
Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks
Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction
GenAI Assistance for Deep Reinforcement Learning-based VNF Placement and SFC Provisioning in 5G Cores
ReinFog: A DRL Empowered Framework for Resource Management in Edge and Cloud Computing Environments
Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks