The optimal and dynamic Trickle algorithm for RPL (Routing Protocol for Low-Power and Lossy Networks) is a focused research area in IoT networking aimed at improving the efficiency, reliability, and adaptability of control message dissemination in constrained networks. Research papers in this domain explore enhancements to the traditional Trickle algorithm to optimize parameters such as transmission intervals, suppression mechanisms, and redundancy constants, with the goal of reducing control overhead, energy consumption, and convergence time. Key contributions include dynamic interval adjustment based on network density and topology changes, adaptive suppression to mitigate message collisions, and integration with machine learning or fuzzy logic for intelligent parameter tuning. Recent studies also address challenges such as scalability, network heterogeneity, mobility, and security of control messages in RPL-based IoT networks. By refining the Trickle algorithm, research in this area aims to enable robust, energy-efficient, and high-performance routing for large-scale, low-power IoT deployments.