Year: 2025
Type: conference-paper
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 QNetwork to optimize the sampling policy. Experiments on the Intel Lab Data dataset confirm that, compared with fixedfrequency 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.
Authors: Weiqiang Huang, Juecen Zhan, Yumeng Sun, Xu Han, Tai An, Nan Jiang
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411552002