Jul 2026· Signal, Image and Video Processing· Vol 20· 0 citations· 32 references
Computer Science
TL;DR
This paper uses the UCF-Crime dataset, which consists of actual surveillance video frames, to present an intelligent edge-cloud framework for Real-Time (RT) human activity identification, and proposes a Dynamic-DQN-based scheduler that dynamically executes computational tasks between the edge node and cloud node, powered by a Q/Q/K queuing model.
STQ-Scheduler is proposed, a secure and throughput-aware deep reinforcement learning framework that integrates high-throughput data processing, Transformer-based QoE prediction, and Proximal Policy Optimization-based resource scheduling to ensure data quality and prevent data processing from becoming a bottleneck in distributed training.
Yi-Chun Chang, Min-Wei Jiang· Fundamental Scientific Repor...· 0 citations
The findings demonstrate that DRL-driven adaptive orchestration can become a central mechanism for autonomous edge intelligence in next-generation AI-native communication infrastructures.
Amit K. Mogal, Rahul A. Patil, Sahebrao N. Shinde et al.· International Journal of Inn...· 0 citations
An online Deep Reinforcement Learning (DRL) based adaptive partition method to dynamically determine optimal partitioning decision so as to jointly accelerate DNN inference and mitigate energy consumption is developed.
Shubin Zhang, Junrong Ma, Kaikai Chi et al.· ACM transactions on sensor n...· 0 citations
A reinforcement learning-based computation offloading strategy using an improved Deep Deterministic Policy Gradient (IDDPG) algorithm that outperforms greedy offloading strategies and demonstrates improved efficiency in dynamic MEC environments.
S. Alam, Sami ud Din, Imtiaz Ali Shah et al.· International Journal of Inn...· 0 citations
The growing smart devices (SDs) in the Industrial Internet of Things (IIoT) generate complex computations that strain the performance and energy of local processing. Mobile Edge Computing (MEC) addresses this by providing nearby computing resources for low-latency offloading. However, achieving efficient computation offloading under massive device concurrency and densely distributed computation offloadings remains a key challenge. To address this, this paper constructs a multi-server MEC system model for IIoT and introduces Mean-Field Game (MFG) theory to model the offloading competition among SDs. This effectively reduces the dimensionality and complexity of multi-agent interactions. A novel Mean-Field Computation Offloading (MFCO) algorithm is proposed, which combines MFG with Rainbow Deep Q-Network under a Multi-Agent Deep Reinforcement Learning framework. By incorporating advanced components such as distributional value estimation, prioritized experience replay, multi-step learning, and dueling architecture, each SD acts as an autonomous agent, optimizing its policy based on local observations and mean-field approximations. Further enhancements include Boltzmann exploration, adaptive learning rates, and a mean Q-network structure, which improve convergence speed and training stability. Extensive simulations on a large-scale IIoT platform (100 SDs, 9 MEC servers) demonstrate that MFCO reduces computation latency and improves long-term rewards while maintaining robust server performance.
Xinmin Cheng, Chengquan Yu, Lu Gao et al.· IEEE Transactions on Green C...· 0 citations
A novel hybrid framework that combines a Graph Attention Network (GAT)-based fault prediction model with a Generative Adversarial Network (GAN)-driven task migration decision model is proposed, achieving substantial reductions in task execution time and energy consumption while improving predictive accuracy and resource efficiency.