Performance, Energy Efficiency, and Security of Distributed rTPNN for TinyML-Enabled Smart Homes

Tiny Machine Learning (TinyML) brings AI capabilities to low-power edge devices, enabling local data processing without cloud reliance. This paper evaluates a distributed Recurrent Trend Predictive Neural Network (rTPNN) deployed on ESP8266 and ESP32 microcontrollers within a smart home IoT network. The rTPNN achieves superior accuracy with fewer parameters than standard models while incurring negligible computational costs. Energy profiling demonstrates that constant WiFi connectivity extends battery life eight-fold compared to periodic reconnection. However, security assessments reveal that constant connectivity is highly susceptible to Denial of Service (DoS) saturation, and parameter injection can degrade accuracy by over 70%. These results substantiate the feasibility of rTPNN as a TinyML for sustained operation while highlighting the critical need for codesigned security mechanisms.