Zhang et al., 2025
| Publication | Publication Date | Title |
|---|---|---|
| El Haber et al. | UAV-aided ultra-reliable low-latency computation offloading in future IoT networks | |
| Tran-Dang et al. | Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues | |
| Thantharate et al. | ADAPTIVE6G: Adaptive resource management for network slicing architectures in current 5G and future 6G systems | |
| Nguyen et al. | DRL‐based intelligent resource allocation for diverse QoS in 5G and toward 6G vehicular networks: a comprehensive survey | |
| Zhang et al. | Enhancing UAV-assisted vehicle edge computing networks through a digital twin-driven task offloading framework | |
| Pasandideh et al. | An improved particle swarm optimization algorithm for UAV base station placement | |
| Wang et al. | A reinforcement learning approach for online service tree placement in edge computing | |
| Nguyen et al. | Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications | |
| Afrasiabi et al. | Reinforcement learning-based optimization framework for application component migration in nfv cloud-fog environments | |
| Gupta et al. | Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey | |
| Allaoui et al. | Reinforcement learning based task offloading of IoT applications in fog computing: algorithms and optimization techniques | |
| Ahmed et al. | MARL based resource allocation scheme leveraging vehicular cloudlet in automotive-industry 5.0 | |
| Gong et al. | Dynamic resource allocation scheme for mobile edge computing | |
| Tahmasebi-Pouya et al. | A reinforcement learning-based load balancing algorithm for fog computing | |
| Karimi et al. | Intelligent and decentralized resource allocation in vehicular edge computing networks | |
| Yuan et al. | Integrated route planning and resource allocation for connected vehicles | |
| Chen et al. | Vehicular Edge Computing Networks Optimization via DRL-Based Communication Resource Allocation and Load Balancing | |
| Adly | Integrating vehicular technologies within the IoT environment: a case of Egypt | |
| Mokhtar | AI-enabled collaborative distributed computing in networked uavs | |
| Ghahari-Bidgoli et al. | An efficient task offloading and auto-scaling approach for IoT applications in edge computing environment | |
| Du et al. | Task placement and resource allocation for UAV and edge computing supported transportation systems | |
| Khatua et al. | Dew Computing-Based Sustainable Internet of Vehicular Things | |
| Kharchenko et al. | Packet losses in SAGIN with artificial intelligence | |
| Li et al. | A vehicular edge computing content caching solution based on content prediction and D4PG | |
| Ullah et al. | Optimizing vehicular edge computing: graph-based double-DQN approaches for intelligent task offloading |