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

arXiv: https://arxiv.org/abs/2504.15577 · HTML: https://mlsanity.com/paper/2504.15577/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction](https://mlsanity.com/paper/2504.04385/) (2025)

## Built on by (most cited first, 5 of 5)

- [Autonomous Resource Management in Microservice Systems via Reinforcement Learning](https://mlsanity.com/paper/2507.12879/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems](https://mlsanity.com/paper/2505.16248/) (2025)
- [Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models](https://mlsanity.com/paper/2506.00726/) (2025)

## Abstract (arXiv)

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.