Published Paper Details:

Fault tolerance is a crucial requirement in embedded systems, particularly in critical applications such as aerospace, automotive safety, healthcare, and industrial automation. These systems must function reliably under extreme conditions while minimizing failure risks. Traditional fault-tolerant techniques, including Triple Modular Redundancy (TMR), checkpointing, and error correction codes (ECC), have limitations in terms of computational overhead, resource constraints, and adaptability to dynamic faults.This paper explores advanced fault-tolerant mechanisms, focusing on AI-driven fault prediction, adaptive redundancy management, and real-time self-healing techniques. A novel AI-based fault-tolerant embedded system is proposed and compared against existing methods, demonstrating higher fault detection accuracy (98%), reduced system recovery time (12ms), and lower computational overhead (18%). Furthermore, future research directions, including quantum computing, edge-based fault tolerance, and neuromorphic computing, are discussed. The study highlights the need for standardized fault-tolerance evaluation frameworks to enhance reliability in safety-critical embedded applications.