State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

2025-04-22

AI Summary

To address energy-efficiency management challenges for intelligent IoT devices in complex environments, this paper proposes a dynamic resource scheduling method integrating Deep Q-Networks (DQN) with edge-enabled collaborative mechanisms. The approach innovatively constructs a collaborative graph structure to model inter-device dependencies, jointly encodes device states, task workloads, and network resources into a unified state space, and leverages edge nodes as policy coordination intermediaries to improve policy generalization and environmental adaptability. Graph Neural Networks (GNNs) are incorporated for enhanced relational modeling, and real-time online learning ensures adaptive decision-making. Experiments on the FastBee real-world IoT dataset demonstrate that the proposed method reduces average energy consumption by 23.6%, decreases processing latency by 19.4%, improves resource utilization by 31.2%, and significantly accelerates convergence while enhancing robustness compared to baseline approaches.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments. A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism. The method builds a state-action-reward interaction model and introduces edge nodes as intermediaries for state aggregation and policy scheduling. This enables dynamic resource coordination and task allocation among multiple devices. During the modeling process, device status, task load, and network resources are jointly incorporated into the state space. The DQN is used to approximate and learn the optimal scheduling strategy. To enhance the model's ability to perceive inter-device relationships, a collaborative graph structure is introduced to model the multi-device environment and assist in decision optimization. Experiments are conducted using real-world IoT data collected from the FastBee platform. Several comparative and validation tests are performed, including energy efficiency comparisons across different scheduling strategies, robustness analysis under varying task loads, and evaluation of state dimension impacts on policy convergence speed. The results show that the proposed method outperforms existing baseline approaches in terms of average energy consumption, processing latency, and resource utilization. This confirms its effectiveness and practicality in intelligent IoT scenarios.

Problem

• Optimizing energy efficiency in IoT devices using DQN and edge collaboration
• Dynamic resource coordination and task allocation among multiple IoT devices
• Enhancing scheduling strategy with collaborative graph structure for multi-device environments

Innovation

• Deep Q-Network for IoT scheduling optimization
• Edge collaboration for dynamic resource coordination
• Graph structure models multi-device relationships