Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918214566412288 |
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| author | Vo, Van-Vi Nguyen, Tien-Dung Le, Duc-Tai Choo, Hyunseung |
| author_facet | Vo, Van-Vi Nguyen, Tien-Dung Le, Duc-Tai Choo, Hyunseung |
| contents | Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This framework enables timely insights in IoT environments, paving the way for scalable, low-latency data aggregation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT Vo, Van-Vi Nguyen, Tien-Dung Le, Duc-Tai Choo, Hyunseung Networking and Internet Architecture Machine Learning Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This framework enables timely insights in IoT environments, paving the way for scalable, low-latency data aggregation. |
| title | Q-Learning-Based Time-Critical Data Aggregation Scheduling in IoT |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2511.17531 |