This study provides an in-depth examination and preliminary results of a unique methodology for improving the efficiency and security of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs). The methodology provided consists of two interconnected modules: the Mobile Sinks Prediction Module and the Trust-Aware Model. The first module focuses on routing using an IoT-based WSN, beginning with the initialization of inputs and simulation settings. The definition of WSN plot dimensions, defining node energy levels, determining the number of nodes and mobile sinks, and designing a prediction algorithm are all critical stages. In the optimization phase, the Modified Adaptive Cuckoo Search Algorithm is used to build a predictive model for mobile sink positions. The second module introduces a Trust-Aware Model, stressing network parameter initialization, direct and indirect trust value calculation, and a hybrid routing algorithm based on complete trust values. Variables are specified and updated, and statistical techniques are used to evaluate performance. The stages work together to analyze and evaluate IoT-based WSNs, addressing issues, such as routing efficiency, security, and adaptability. The findings of this study shed light on the efficacy of the proposed methodology in optimizing IoT-based WSNs and providing dependable and secure communication.