Communication-efficient Embedded FFT Processing for Acoustic Telemetry in LPWAN-based Beehive Monitoring Systems

Abstract

Low-power acoustic telemetry remains a significant challenge in Internet of Things monitoring systems deployed in remote environments. This paper presents an embedded fast Fourier transform framework for communication-efficient acoustic monitoring of honey bee colonies. Instead of transmitting raw audio streams, the proposed approach extracts compact spectral descriptors directly on an embedded sensing node and transmits only a small feature vector using a low-power cellular network. The framework is evaluated using labeled queenright and queenless colony recordings. The proposed solution targets resource-constrained ESP32 class IoT nodes operating over LTE-M and NB-IoT networks. The analysis covered such parameters as dominant frequency, peak amplitude, mean spectral amplitude, spectral centroid, spectral entropy, and band energy extracted from the 200 - 400 Hz band. The results showed that dominant frequency alone did not significantly differentiate colony states at the file level, while mean spectral amplitude remained statistically significant. Queenless recordings also exhibited higher dominant frequency variability. The proposed approach reduces the transmitted payload by more than three orders of magnitude while remaining compatible with resource-constrained ESP32 class IoT devices. The results demonstrate that the extraction of embedded acoustic features is a practical method for scalable smart beehive monitoring under strict memory, power, and bandwidth constraints.