Machine Learning at the Edge: Empowering IoT Devices with Intelligent Decision-Making

Abstract

This paper explores the transformative role of machine learning (ML) at the edge of the Internet of Things (IoT) ecosystem. It investigates how ML algorithms and models are being deployed directly on IoT devices, enabling real-time, intelligent decision-making without the need for constant connectivity to centralized cloud servers. The paper delves into the applications of edge ML across various domains, such as smart cities, industrial automation, and healthcare, emphasizing its potential to enhance efficiency and responsiveness.

Moreover, it addresses the unique challenges associated with deploying ML at the edge, including resource constraints, security, and privacy considerations. The paper envisions a future where edge ML becomes ubiquitous, driving innovation in IoT and paving the way for autonomous, intelligent devices. As the convergence of ML and IoT continues to shape the technological landscape, this paper offers valuable insights for researchers, engineers, and policymakers seeking to harness the potential of edge-based machine learning.