An Intelligent Monitoring Framework for Distributed Resource Systems Using Hybrid Machine Learning and IoT

Abstract

The decentralized nature of resource structure, notably such as store facilities (e.g., stores), environmental reservoirs and infrastructure devices, commonly does not operate engaged monitoring that may lead to unnoticed degradation and threatening situations [1], [2]. A generalized Internet of Things (IoT) – Artificial intelligence (AI) Infrastructure for predictive tracking of typical distributed instinctive resource environments is proposed in this paper. The architecture introduced has integrated multivariate sensors, edge cloud communication and hybrid machine learning methodologies, adaptive persistence mechanisms with a clear objective to strengths real time detection and early risk identification [3], [4]. It also presents a decision tree learning & recurrent neural network-based hybrid that can be useful for non linear trends in sensor data and time variations [5], [6]. The framework is adaptable across different areas like keeping an eye on environmental storage, managing resources that take a lot of manpower, and building resilience in infrastructure systems