Decentralizing Intelligence: Transitioning Machine Learning from Cloud Infrastructures to Edge Microcontrollers in Mechatronic Systems
Year: 2026
Type: article
Abstract: : The integration of artificial intelligence into physical hardware has traditionally relied on cloud computing infrastructure, introducing significant latency, bandwidth constraints, and power dependencies. This paper explores the architectural shift from cloud-dependent machine learning to Edge AI (TinyML). By deploying quantized machine learning models directly onto resource-constrained microcontrollers, mechatronic systems achieve real-time, autonomous decision-making capabilities. This paradigm shift is essential for the development of resilient physical infrastructure and decentralized hardware networks. Key Topics & Technologies: Hardware Architecture: Microcontrollers (ATmega2560, ESP32), Mechatronic Actuators, IoT Sensors. Software & ML Pipeline: Edge Impulse, TinyML, Model Quantization (8-bit integer scaling), C++ Library Deployment. Core Concepts: Decentralized physical infrastructure, autonomous fluid management, multimodal sensor fusion, localized offline intelligence.
Author: Abdulwahab, Hayatullah
Institution: Ahmadu Bello University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7172275756
FWCI:
Cites:
Cited by:
Related to: