TinyML-driven Sensor Nodes for Energy-efficient Acoustic Event Detection in Pervasive Acoustic WSNs

Abstract

The process of sensing and transmitting acoustic signals by pervasive acoustic wireless sensor networks (PAWSNs) poses considerable energy challenges. These problems may be mitigated by filtering only relevant acoustic events from the sensor network. By reducing the number of acoustic events, the frequency of communication may be decreased, thereby enhancing energy efficiency. Although traditional machine learning models are capable of predicting relevant acoustic events by being trained on suitable data sets, they are impractical for direct implementation on resource-limited acoustic sensor nodes. To address this issue, this research introduces TinyML-based acoustic event detection (AED) models which facilitate efficient real-time processing on microcontrollers with scarce hardware resources. The study develops several TinyML models using an environmental dataset and evaluates their accuracy. These models are then deployed in hardware to assess their performance in terms of AED. Thanks to such an approach, only predicted events that exceed a certain threshold are transmitted to the base station via router nodes, which reduces the transmission burden, thus improving energy efficiency of PAWSNs. Real-time experiments confirm that the proposed method significantly improves energy efficiency and boosts node lifetime.