AI-Driven Lightweight Observability Framework for Edge Computing in IoT

Abstract

This article introduces a novel lightweight observability framework designed for IoT edge computing environments, addressing the critical challenges of monitoring distributed systems with resource constraints. The framework leverages adaptive sampling techniques, edge-local processing, and efficient log aggregation to provide comprehensive system visibility while minimizing resource overhead. Through innovative approaches in data collection, processing, and analysis, the solution enables effective monitoring of edge devices without compromising their primary functions. The framework demonstrates significant improvements in resource utilization, network efficiency, and system reliability across diverse real-world deployments, including smart agriculture and industrial automation applications. By implementing intelligent data management strategies and leveraging advanced machine learning techniques at the edge, the framework provides a scalable solution for maintaining observability in resource-constrained IoT environments while ensuring optimal performance and reliability.