The adaptive learning scheme to increase fault tolerance of IOT

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S.PRIYA DHARSHINI; S.POONGODI; S.K.RAVICHANDARAN

The adaptive learning scheme to increase fault tolerance of IOT

Authors: S.PRIYA DHARSHINI, S.POONGODI, S.K.RAVICHANDARAN

Unique Paper ID: 182269
Volume: 12
Issue: 2
PageNo: 1780-1785

Keywords: Fault Tolerance Adaptive Learning IoT (Internet of Things) Anomaly Detection Edge Computing

Abstract:
The Internet of Things (IoT) has revolutionized the way devices interact and exchange data, enabling smarter environments across various sectors such as healthcare, agriculture, and smart cities. However, the large-scale and heterogeneous nature of IoT systems makes them highly susceptible to faults—ranging from sensor failures and network disruptions to data anomalies—which can severely impact system reliability and performance. To address this challenge, this paper proposes an adaptive learning scheme designed to enhance fault tolerance in IoT environments. The scheme employs machine learning techniques, particularly reinforcement learning and anomaly detection models, to continuously monitor device behavior and network conditions. By learning from past fault patterns and system responses, the scheme can dynamically adapt to new and unforeseen faults, minimizing system downtime and improving resilience. Additionally, it incorporates lightweight algorithms suitable for edge computing, reducing dependency on centralized systems and improving real-time fault recovery. Experimental results demonstrate that the proposed scheme effectively identifies and mitigates faults with minimal latency and high accuracy. This adaptive approach not only improves system stability but also extends the operational lifetime of IoT devices. The study highlights the importance of intelligent fault management for the reliable functioning of next-generation IoT networks.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 2
PageNo: 1780-1785

The adaptive learning scheme to increase fault tolerance of IOT

Available:https://ijirt.org/article?manuscript=182269

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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