Optimizing IoT-enabled WSN routing strategies using whale optimization-driven multi-criterion correlation approach employs the reinforcement learning agent

Wireless Sensor Networks (WSNs) are rapidly integrating into various fields due to their sensing capabilities, making them ideal for connecting multiple users to the Internet of Things (IoT) for real-time applications. However, these networks face limitations in delay and energy, impacting the efficiency of routing protocols. For devices to operate effectively over extended periods, optimizing these protocols is crucial. Addressing this, our study introduces a machine learning-optimized routing protocol tailored for IoT-enabled WSNs. This protocol efficiently manages IoT devices, emphasizing energy efficiency and mobility. We present the Whale Optimization-Driven Multi-Criterion Correlation (WOD-MCC) method, which consistently updates routing strategies based on insights from energy consumption and traffic patterns, ensuring effective routing decisions. This method evaluates device energy levels and node access to reduce data congestion and energy consumption while preserving the connectivity of IoT devices. For the performance evaluation of the proposed WOD-MCC, energy efficiency, scalability, and network connectivity are emphasized. In terms of data delivery, energy conservation, and delay reduction, our findings indicate that the WOD-MCC method outperforms existing protocols.