Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

EdgeRL-surveillance: adaptive decision making for resource-constrained video analytics using reinforcement learning

Sandhya Rani Nallola Vadivel Ayyasamy
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.

DOI Semantic Scholar
View source

Similar papers

Open access Jul 2026

STQ-Scheduler: A Secure and Throughput-Aware Deep Reinforcement Learning Framework for QoE-Driven Resource Scheduling in Distributed Video Streaming Systems

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 di...

Yi-Chun Chang, Min-Wei Jiang · 0 citations
Open access Aug 2026

Adaptive Edge Resource Management Through Deep Reinforcement Learning Techniques

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. · 0 citations
#edge computing Open access Aug 2026

DAPart: An Online DRL-based Adaptive Partition Framework for DNN Inference Acceleration and Energy Conservation in Edge Computing

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.

Shu-Bin Zhang, Junrong Ma, Kai-Kai Chi et al. · 0 citations
Open access Jul 2026

Decentralized Task Offloading for Multi-User Mobile Edge Computing with Machine Learning

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. · 0 citations
2026

MFCO: A Rainbow DQN-Enhanced Mean-Field Approach for Computation Offloading in IIoT Using Mobile Edge Computing

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 of...

Xinmin Cheng, Chengquan Yu, Lu Gao et al. · 0 citations
Open access Jul 2026

Mobility-aware task scheduling based on artificial intelligence in edge-cloud systems

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 resourc...

Jong-Bu Lim · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.