Research papers in trust-based mechanisms for the RPL (Routing Protocol for Low-Power and Lossy Networks) focus on enhancing the security and reliability of IoT communication by introducing trust and reputation models into the routing process. Since RPL is vulnerable to numerous attacks such as sinkhole, blackhole, rank manipulation, and selective forwarding, trust-based solutions evaluate the behavior of nodes based on parameters like packet forwarding ratio, energy consumption, response time, and consistency of control messages. Nodes are then assigned trust values, and routing decisions are made by preferring highly trusted neighbors, thereby reducing the impact of malicious or selfish nodes. Various trust computation methods have been explored, including direct trust (based on past interactions), indirect trust (recommendations from other nodes), and hybrid approaches. Researchers have also proposed fuzzy logic, Bayesian inference, and machine learning-based trust models to handle uncertainty and dynamic changes in IoT environments. To ensure lightweight operation suitable for resource-constrained devices, many schemes adopt adaptive trust evaluation with low computation and communication overhead. Recent works combine trust mechanisms with intrusion detection, blockchain, and reinforcement learning to build resilient, self-adaptive security frameworks. While trust-based mechanisms significantly improve the robustness of RPL, open challenges remain in achieving scalability, energy efficiency, resistance to collusion attacks, and maintaining accuracy under high network mobility. Overall, the literature emphasizes that integrating trust-based routing into RPL is a promising direction for securing IoT networks while balancing performance and resource constraints.