AI-ORCHESTRATED EDGE CLUSTERS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL IOT

Abstract

This study investigates AI-orchestrated edge Clusters for Predictive Maintenance in Industrial IoT using a secondary qualitative data analysis approach. The findings reveal three central themes: resource-efficient orchestration, predictive analytics optimization, and ethical operational governance. Literature reviews confirm that AI orchestration enhances predictive accuracy, reduces latency, and promotes sustainability across industrial networks. The study concludes that AI-orchestrated edge systems provide a scalable, resilient solution for predictive maintenance while highlighting the need for standardized frameworks and energyefficient orchestration models for future industrial deployment.