FAULT-TOLERANT CLUSTERING PROTOCOL FOR IOT WITH COLD AND HOT STANDBY REDUNDANCY

IoT networks are becoming a more unsteady and heterogeneous setting in which frequent failure of devices, multi-modal noisy signals and uneven workloads diminish the stability of the cluster and the availability of the system. Although the current developments in the area of IoT analytics are rather fast, the currently used clustering protocols seldom incorporate adaptive redundancy, as well as do not resolve the problem of large-scale, error-prone deployment. The ability of this gap limit to be used in critical applications like smart cities, healthcare monitoring, and industrial IoT. The objective of this study was to create a robust and fault-tolerant clustering protocol, which is able to learn stable cluster-based structures in addition to dynamically maintaining cold and hot standby redundancy to ensure high availability with low overhead. A federated experimental model was adopted with publicly available IoT data (telemetry, network flows, acoustic faults, and ambient sensing), in which clustering was achieved with the help of Federated Graph-Contrastive Clustering (FGCC), and redundancy choices were made with the assistance of reinforcement learning. Devices were tested with injected failures, partitions, and variation of workloads to test the availability, MTTD, MTTR, clustering accuracy, and computational cost. The FT-CoH model was proposed to perform better, making ARI 0.78, availability 99.1, and the reduction of MTTR to 2.1 seconds, which is better in comparison to k-means, deep clustering, and federated baselines. These results show that adaptive cold happens to be much more efficient in terms of distributing fault tolerance in the IoT by means of multimodal federated clustering, which is additionally complemented by adaptive cold-hot standby management. In general, FT-CoH offers a reliable and scalable smart framework of IoT systems of the next generation, and the prospects it brings to autonomous monitoring, smart infrastructure, and critical IoT applications have been high.