# Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN

arXiv: https://arxiv.org/abs/2504.09344 · HTML: https://mlsanity.com/paper/2504.09344/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C](https://mlsanity.com/paper/2407.14151/) (2024)
- [A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning](https://mlsanity.com/paper/2502.09086/) (2025)
- [Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework](https://mlsanity.com/paper/2503.11125/) (2025)
- [A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks](https://mlsanity.com/paper/2502.16484/) (2025)
- [A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization](https://mlsanity.com/paper/2503.06888/) (2025)
- [Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM](https://mlsanity.com/paper/2503.11084/) (2025)
- [Automated UI Interface Generation via Diffusion Models: Enhancing Personalization and Efficiency](https://mlsanity.com/paper/2503.20229/) (2025)

## Built on by (most cited first, 6 of 6)

- [Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models](https://mlsanity.com/paper/2504.19436/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)
- [DeepSORT-Driven Visual Tracking Approach for Gesture Recognition in Interactive Systems](https://mlsanity.com/paper/2505.07110/) (2025)
- [Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift](https://mlsanity.com/paper/2512.12816/) (2025)

## Abstract (arXiv)

Multi-sensor systems are widely used in the Internet of Things, environmental monitoring, and intelligent manufacturing. However, traditional fixed-frequency sampling strategies often lead to severe data redundancy, high energy consumption, and limited adaptability, failing to meet the dynamic sensing needs of complex environments. To address these issues, this paper proposes a DQN-based multi-sensor adaptive sampling optimization method. By leveraging a reinforcement learning framework to learn the optimal sampling strategy, the method balances data quality, energy consumption, and redundancy. We first model the multi-sensor sampling task as a Markov Decision Process (MDP), then employ a Deep Q-Network to optimize the sampling policy. Experiments on the Intel Lab Data dataset confirm that, compared with fixed-frequency sampling, threshold-triggered sampling, and other reinforcement learning approaches, DQN significantly improves data quality while lowering average energy consumption and redundancy rates. Moreover, in heterogeneous multi-sensor environments, DQN-based adaptive sampling shows enhanced robustness, maintaining superior data collection performance even in the presence of interference factors. These findings demonstrate that DQN-based adaptive sampling can enhance overall data acquisition efficiency in multi-sensor systems, providing a new solution for efficient and intelligent sensing.