Analyzing Methodological Approaches for Mobile Sink Prediction and Trust-Aware Routing for IoT-Wireless Sensor Networks

This paper elaborates on the Mobile Sink Prediction module for the Internet of Things-Wireless Sensor Networks (IoT-WSN). It proposes a learning mechanism, maybe involving Deep Long Short-Term Memory (LSTM), for forecasting mobile sink locations. Moreover, an optimization algorithm is used to tune system performance depending on the predicted sink locations, e.g., energy efficiency and network lifetime. The adaptive routing algorithms are developed with the help of evolutionary optimization and deep learning techniques to optimize data transmission paths based on the inferred sink locations and network conditions. The paper further examines the trust-aware model, underlining that trust management is key to network security and robust communication in wireless sensor networks. An evaluation of the performance of the trust-based routing algorithm is performed via this statistical demonstration, and throughput and residual energy are considered as this result is supplied for the information about the effectiveness of secure and energy-efficient data transmission in IoT-WSN systems.