Self-Healing Networks AI-Based Approaches for Fault Detection and Recovery

Abstract

The reliability and robustness of communication networks are critical for ensuring seamless connectivity in today's interconnected world. However, network faults and failures can disrupt services, leading to significant downtime and financial losses. [1]In response to these challenges, self-healing networks empowered by artificial intelligence (AI) have emerged as a promising solution for proactively detecting and autonomously recovering from network faults. This paper presents a comprehensive review and analysis of AI-based approaches for fault detection and recovery in self-healing networks, aiming to enhance network reliability and minimize service disruptions. It provides an overview of the key challenges associated with traditional fault detection and recovery methods in communication networks.[2] It discusses the limitations of reactive approaches that rely on manual intervention or predefined rules to identify and mitigate network faults, highlighting the need for more proactive and adaptive solutions. Subsequently, the paper explores the role of artificial intelligence, particularly machine learning and deep learning techniques, in enabling self-healing capabilities in communication networks. By leveraging vast amounts of network data, AI algorithms can learn complex patterns and anomalies indicative of network faults, enabling timely detection and response. The paper reviews various AI-based fault detection techniques, including anomaly detection, supervised learning, and reinforcement learning approaches. It examines their strengths and limitations in different network environments, considering factors such as data availability, scalability, and real-time processing requirements. Furthermore, the integration of AI-based fault detection with network monitoring systems and telemetry data sources is discussed, highlighting the importance of comprehensive data-driven approaches for accurate fault identification. In addition to fault detection, the paper investigates AI-driven recovery mechanisms for self-healing networks. It explores proactive recovery strategies, such as predictive maintenance and network reconfiguration, enabled by AI-based predictive analytics. By harnessing the power of machine learning and deep learning, communication networks can achieve greater resilience, adaptability, and autonomy in mitigating network faults and ensuring uninterrupted service delivery.