Analysis of Dynamic Knowledge Graph Construction and Clustering for Effective Knowledge Management in Machine-to-Machine Communication

Abstract

An era of interconnected devices that exchange data has emerged due to machine-to-machine (M2M) communication, a key component of the Internet of Things (IoT). This study explains how dynamic knowledge graph construction improves knowledge management in M2Mcommunication networks. In M2M communication, devices continuously generate and exchange data, creating a complex and dynamic information network. A dynamic knowledge graph is a promising solution for managing and addressing this level of complexity. The knowledge graph evolves in real time to capture M2M network relationships, entities, and data flows. M2M communication with dynamic knowledge graphs has many benefits. It begins with a broad overview of network components and their relationships. The structured format helps understand and make decisions by representing devices, their attributes, and their contextual relationships. The knowledge graph can also scale easily to support the rapid growth of devices and data in M2M networks. A dynamic knowledge graph lets M2M networks route data intelligently. Context-aware decisions reduce latency and improve network efficiency. The knowledge graph helps M2M networks detect and analyze anomalies and patterns. Detecting deviations from expected behavior improves security and proactive network maintenance, ensuring its integrity and reliability. Efficient knowledge management requires dynamic knowledge graphs in M2M communication networks. The data used for the proposed work is collected from the World Wide Web Consortium (W3C). It provides valuable insights into using technologies to improve learning and knowledge management. The dataset is comprehensive and useful for studying dynamic knowledge graphs and clustering in M2M. This enhances M2M networks' reliability and intelligence in the IoT era..