A Privacy-Preserving TinyML-Driven IoT Edge Architecture for Low-Latency Smart Sensing and Autonomous AI-Based Control

Abstract

As smart sensing applications grow rapidly, the IoT edge architectures need to support low latency, make decisions with little memory, power, and communication resources while preserving the privacy of the data. But traditional cloud-based IoT solutions come with transmission delay, increased energy consumption, and privacy issues because of the constant transfer of raw data. In this paper, we propose a privacy-preserving TinyML-driven IoT edge architecture that enables real-time smart sensing and autonomous AI-based control. The proposed framework includes on-device sensor pre-processing, lightweight TinyML inference, adaptive model selection, encrypted feature-level communication, trust-aware decision validation, and local control execution. Raw data streams from the sensors are also processed locally, and only compact encrypted features or a summary of the decisions are sent if necessary to minimize privacy exposure. The experimental evaluation reveals that the proposed architecture has an accuracy of 97.4%, an F1 score of 96.9%, and a secure-event detection rate of 98.1% and reduces the inference latency by 14.8%, the energy consumption by 4.1 mJ per inference and the amount of data transmitted by 74.5% compared to conventional edge-cloud processing. Results show that the proposed architecture is a scalable, privacy-aware, and energy-efficient solution for real-time autonomous IoT control of smart environments with limited resources.