Autonomous Resilience in Heterogeneous IoT Networks

Project Overview

Modern infrastructure such as smart cities and industrial IoT relies on vast networks of diverse devices operating in dynamic, complex environments. Traditional fault tolerance techniques are inadequate for managing runtime uncertainties and varied failure modes present. This research innovates by merging distributed computing and AI to create an autonomous, resilient IoT architecture with real-time root cause analysis and automated recovery capabilities.

What You Will Do

The project involves developing adaptive fault monitoring using federated learning and graph neural networks for dynamic diagnosis in multi-modal data streams. Advanced risk modeling will be conducted with Bayesian deep learning and deep reinforcement learning to quantify uncertainties and predict faults. Additionally, a trustworthy autonomous recovery framework will be designed using AI-driven decision engines combined with blockchain technology to ensure credible self-healing. Theoretical work will address scaling challenges posed by heterogeneous device diversity at large network scales.

Expected Outcomes

The project will deliver a novel resilient IoT system framework featuring autonomous self-diagnosis and self-healing capabilities with verifiable automated recovery operations. It will advance the state-of-the-art in federated learning applications and trustworthy AI-based decision making for network resilience, suitable for complex IoT deployments in smart cities and industry.

Why This Matters

Ensuring resilience in distributed IoT networks is critical as these systems become integral to urban infrastructure and industrial automation. The ability to autonomously detect, diagnose, and recover from faults in real time with trustworthy mechanisms will increase system reliability, reduce maintenance costs, and enhance safety and sustainability of future smart environments.