Temporal Convolutional Network-Based Time-Series Forecasting for Cluster Head Prediction in Energy-Constrained WSN-Based IoT

In Wireless Sensor Networks-based Internet of Things environments, efficient Cluster Head (CH) selection remains a fundamental challenge, as the limited energy resources of sensor nodes critically affect network lifetime and service sustainability. Existing clustering strategies are often reactive, overlooking temporal dependencies in CH election processes, which leads to unnecessary energy dissipation and increased communication overhead. To address this gap, we propose a predictive clustering paradigm that leverages Temporal Convolutional Networks combined with Bayesian Optimization (TCN-BO). The core novelty of this work lies in formulating CH election as a time series forecasting problem and applying deep temporal learning to proactively guide clustering decisions, an aspect not explored in prior AI-based approaches. Unlike conventional methods, our approach explicitly models the temporal evolution of CH roles in the form of binary time series, enabling proactive CH election planning instead of reactive selection. The proposed TCN-BO framework achieves high-accuracy forecasting of CH sequences (MAPE = 10.04%) and translates these predictions into effective energy-aware cluster formation.This work introduces a novel research direction by integrating deep temporal learning into clustering protocols, thereby bridging prediction and network management. Extensive experiments demonstrate that our method reduces communication overhead, improves energy balancing, and significantly enhances network longevity compared to existing clustering strategies.