Volume: 19 Issue-01 (January-June) 2024

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IoT-BASED FAULT DETECTION AND DIAGNOSIS IN COMPUTER NETWORKS

ABSTRACT

The proliferation of Internet of Things (IoT) devices has led to a dramatic increase in the complexity and scale of computer networks, posing significant challenges for fault detection and diagnosis. This review research paper explores the application of IoT technology in the context of fault detection and diagnosis in computer networks. By leveraging the capabilities of IoT devices, such as sensors, actuators, and embedded systems, novel approaches to detecting and diagnosing faults in network infrastructure are emerging. The study begins by providing an overview of the fundamental concepts of fault detection and diagnosis in computer networks, including common types of network faults, such as link failures, congestion, and security breaches. It then examines traditional fault detection methods, such as network monitoring, packet inspection, and anomaly detection, highlighting their limitations in terms of scalability, accuracy, and real-time responsiveness. Subsequently, the paper explores how IoT-based solutions are transforming fault detection and diagnosis in computer networks. It discusses the deployment of IoT sensors and actuators to monitor network performance, collect data on network traffic, and detect anomalies in real-time. Moreover, it investigates the use of machine learning algorithms and artificial intelligence techniques to analyze IoT-generated data and identify patterns indicative of network faults. Furthermore, the review examines case studies and experimental evaluations of IoT-based fault detection and diagnosis systems deployed in various network environments, including enterprise networks, data centers, and industrial control systems. It analyzes the effectiveness and efficiency of these systems in terms of fault detection accuracy, response time, and scalability. Moreover, the paper discusses the challenges and open research questions in the field of IoT-based fault detection and diagnosis, such as data privacy concerns, interoperability issues, and the integration of IoT devices with existing network infrastructure. It also explores potential future directions for research and development in this area, including the use of edge computing, blockchain technology, and distributed ledger systems to enhance fault detection and diagnosis capabilities in computer networks. In conclusion, this review highlights the transformative potential of IoT technology in revolutionizing fault detection and diagnosis in computer networks. By harnessing the power of IoT devices and advanced analytics, organizations can improve network reliability, minimize downtime, and enhance overall system performance.

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