Abstract: Microseismic monitoring technology, enabled by intelligent node-based acquisition devices, can provide accurate characterization of the depth, thickness, geometry, and lithological properties of underground rock formations, and has significant application potential in fields such as coal mine resource exploration and geological disaster early warning. To address the limitations of conventional wired microseismic acquisition nodes in coal resource monitoring-such as cumbersome cabling, sparse deployment, and difficulty in ensuring long-term stable operation-this study proposes a wireless distributed acquisition scheme based on edge computing. By integrating a signal-triggering algorithm, the system enables on-demand transmission of valid payload data, thereby enhancing the accuracy and reliability of field monitoring. A wireless distributed microseismic acquisition node was developed through hardware–software co-design and optimization. At the hardware level, the node integrates a 32-bit high-precision analog-to-digital converter, a low-noise sampling circuit, a WiFi-based data transmission architecture, and GPS nanosecond-level time synchronization. In addition, efficient DC-DC conversion and an IP5389 fast-charging management module are incorporated to optimize energy management. At the software level, a modular architecture is adopted to support data acquisition, storage, wireless transmission, status monitoring, and data processing.Field tests conducted in the Qinshui Coalfield demonstrate that the developed node can operate continuously and reliably under complex environmental conditions. Using standard sinusoidal signals generated by a signal generator as the reference, comparative tests show that the overall amplitude error of the acquired signal relative to the full-scale range is below 0.1%. The GPS time synchronization accuracy reaches 10 ns, while the wireless transmission system exhibits reliable communication performance. The results indicate that the proposed node provides significant advantages in deployment flexibility, data quality, and energy replenishment capability, offering strong technical support for engineering-oriented, high-density coal resource monitoring.