Assessing the Feasibility and Efficacy of TinyML Based Voice-Activated LED Lighting System for Smart Village Micro-Utilities

This research paper evaluates the feasibility and efficacy of a low-cost voice-activated LED lighting system based on the Raspberry Pi Pico W microcontroller and Edge Impulse platform using embedded machine learning (ML) techniques. The proposed system aims to provide a user-friendly and energy-efficient means of integrating voice control into IoT devices, thereby improving access to clean electricity, communications, and education for residents of smart village micro-utilities. The authors describe the design and implementation of the voice-controlled lighting system and its use of ML to overcome the challenges of traditional cloud-based solutions in resource-constrained environments. The system achieves an accuracy rate of 84.30% in Edge Impulse, and the paper also documents the challenges encountered during development and the methods used to address them. The research offers a technical analysis of a practical approach to voice-activated LED lighting using the Raspberry Pi Pico W and Edge Impulse platforms with embedded ML techniques, expanding the literature on smart home technology.