Adaptive Learning Schemes to Increase Fault Tolerance in IoT (KEY IJP************637)

• Preet Jain,Hanzalah Khan

Abstract

Abstract-The increasing deployment of Internet of Things (IoT) devices has brought significant challenges in terms of maintaining system reliability and ensuring fault tolerance. This paper investigates adaptive learning schemes as a strategy to improve fault tolerance in IoT networks. Unlike traditional fault tolerance methods that rely on preset conditions, adaptive learning schemes dynamically adjust in real time by analyzing patterns, learning from past failures, and anticipating potential faults. Through a comprehensive evaluation of different adaptive learning models, this study highlights the benefits, challenges, and potential improvements in IoT systemsreliability and performance. The findings suggest that adaptive learning can significantly enhance system resilience, minimize downtime, and optimize the allocation of resources, thus supporting the growing need for efficient fault tolerant IoT architectures.Keywords: IoT, Fault Tolerance, Adaptive Learning, System Reliability, Real-Time Adjustment, Resource Optimization, System Resilience.714