Rakhshan, DavisDavisRakhshanBublitz, LucasLucasBublitzKulau, UlfUlfKulau2026-09-172026-09-172026-0622nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026https://hdl.handle.net/11420/64908Passive Acoustic Monitoring (PAM) is widely used for biodiversity assessment and environmental monitoring of wildlife and soundscapes. Long-term field deployments require sensing platforms that are compact, low-power, robust, and cost-efficient while remaining flexible for future extensions.This work presents a modular embedded acoustic sensing platform for autonomous environmental audio recording. The system separates power management, processing and data logging, and audio acquisition into dedicated subsystems connected through standardized interfaces. Audio is acquired via I²S with DMAbased buffering and periodically stored on an SD card using burst-based writing to improve energy efficiency.Evaluation includes power measurements across sampling rates from 8 kHz to 192 kHz, field deployment with automated bioacoustic analysis, and a comparison with established PAM devices. Results show that storage operations dominate energy consumption, and that duty cycling significantly improves overall energy efficiency. The proposed sensing platform provides high-quality recordings suitable for automated acoustic inference and achieves competitive hardware performance while offering greater modularity, lower cost, and improved extensibility compared to many commercial PAM alternatives.The platform provides a foundation for future low-power edge AI processing and standardized wireless communication interfaces for distributed environmental monitoring.enaudio systemsBiodiversitydata acquisitionedge AIlow power electronicsmicrocontrollerswireless sensor networksTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic Engineering::621.38: Electronics, Communications EngineeringTechnology::681: Precision Instruments and Other Devices::681.2: Testing, Measuring, Sensing InstrumentsNatural Sciences and Mathematics::577: EcologyConference Paper10.1109/DCOSS-IoT69657.2026.00151