Creating Intelligent and Adaptive Systems for Energy – Efficient Smart Home Appliances Using Tiny Machine Learning

Abstract

The rapid proliferation of smart home appliances has intensified global energy demands, necessitating innovative solutions that balance intelligence with sustainability. This research proposes a novel framework for energy – efficient smart home systems using Tiny Machine Learning (TinyML) to enable real – time, adaptive, and privacy – preserving intelligence on ultra – low – power embedded devices. While existing approaches rely on cloud – dependent AI introducing latency, privacy risks, and high energy costs this work advances on – device TinyML to create self – optimizing appliances that dynamically adjust their behavior based on user patterns, environmental conditions, and energy constraints. The study addresses three critical gaps in current systems namely, Static model architectures that cannot adapt to real – world variability, Energy – inefficient deployments due to lack of hardware – aware optimizations and Absence of collaborative learning in microcontroller-scale devices. The methodology integrates, context-aware neural networks that autonomously switch between optimized sub – models (1-bit to 8-bit quantization) using reinforcement learning, energy – bounded execution policies leveraging dynamic voltage / frequency scaling (DVFS) and intermittent computing for energy – harvesting scenarios and a lightweight federated learning framework enabling privacy-preserving knowledge sharing across appliances without raw data exposure. This research contributes to sustainable computing by redefining how smart homes leverage embedded AI, with broader implications for IoT, Industry 4.0, and green technology. The proposed framework will be released as open – source tools to accelerate TinyML adoption, alongside patent-pending techniques for adaptive edge intelligence.