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The deployment of large-scale Internet of Things (IoT) systems is steadily increasing in industrial automation, agriculture, smart buildings, and future telecommunication environments such as 6G. These systems often rely on wireless multi-hop communication among battery-powered, resource-constrained embedded devices. While such networks are flexible, fast to deploy, and cost-efficient, maintaining their long-term reliability remains a major challenge. IoT nodes frequently suffer from temporary power loss, energy depletion, memory faults, or intermittent connectivity. Due to the decentralized nature of wireless multi-hop networks, failures of individual nodes can have significant effects on neighboring nodes and on the overall network performance. To ensure robust operation, it is essential to detect failures early and enable the network to recover from them autonomously.

We address this problem by combining lightweight system-level checkpointing mechanisms with digital twin technology. Lightweight checkpointing allows IoT devices to store minimal snapshots of their internal state that can be restored after unexpected power interruptions or software faults. Digital twins, on the other hand, maintain an up-to-date virtual representation of the network and its nodes. By synchronizing relevant node-level state with the digital twin, failures can be detected, reconstructed, and compensated for at the network level.

Based on this approach, we develop different variants of self-healing mechanisms for IoT multi-hop networks. These mechanisms detect failures through inconsistencies between the physical node and its digital twin representation, trigger recovery procedures using checkpointed state, and dynamically reconfigure communication paths to maintain network functionality. The goal is to reduce downtime, prevent cascading failures, and ensure stable operation even under unreliable power conditions and heterogeneous device capabilities.

With this project, we aim to contribute to the development of resilient IoT infrastructures for future 6G environments and cyber-physical systems. Our methods will support long-term, reliable, and energy-efficient operation of large-scale IoT deployments.