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

2025-04-12

AI Summary

To address high data redundancy, excessive energy consumption, and poor adaptability of multi-sensor systems under fixed-frequency sampling in dynamic environments, this paper proposes a context-aware adaptive sampling method based on Deep Q-Networks (DQN). We formulate joint sampling across heterogeneous sensors as a Markov Decision Process (MDP), jointly optimizing data quality, energy consumption, and redundancy rate. By leveraging deep reinforcement learning, the approach enables robust, environment-aware decision-making under varying sensing modalities and interference conditions. Experimental evaluation on the Intel Lab dataset demonstrates that our method significantly improves data quality compared to fixed-rate sampling, threshold-based triggering, and existing RL-based approaches—while reducing average energy consumption by 18.7% and redundancy rate by 32.4%. These results validate the efficiency and feasibility of intelligent, adaptive sensing in resource-constrained multi-sensor systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Learning

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Optimize multi-sensor sampling to reduce redundancy and energy
• Balance data quality and consumption using DQN reinforcement learning
• Enhance robustness in heterogeneous sensor environments with adaptive sampling

Innovation

• DQN-based adaptive sampling for multi-sensor systems
• Reinforcement learning optimizes data quality and energy
• MDP modeling enhances robustness in dynamic environments