Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN
This addresses efficiency issues in multi-sensor systems for applications like IoT and environmental monitoring, but it is incremental as it applies an existing DQN method to a known bottleneck.
This paper tackled the problem of data redundancy and high energy consumption in multi-sensor systems by proposing a DQN-based adaptive sampling method, which improved data quality while lowering average energy consumption and redundancy rates compared to fixed-frequency and other reinforcement learning approaches.
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.