We present a lightweight and adaptive clustering framework for large-scale mMTC scenarios in 5G/6G networks. The approach combines low-complexity initial cluster formation, a drift-aware reclustering mechanism that reacts to spatial changes, and dynamic Cluster Head (CH) reelection based on residual energy, load, or time. A dedicated mobility handler ensures stable connectivity for moving nodes. Implemented in OMNeT++, the framework achieves significant improvements in execution time, network lifetime, delay, and energy efficiency compared to recent state-of-the-art protocols, making it highly suitable for scalable and resilient next-generation IoT deployments.
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