Implement the RSSI Localization Algorithm for Monitoring in Mines by Using Wireless Sensor Networks

Due to the frequent occurrence of accidents in subterranean environments, the real-time tracking of underground workers' whereabouts holds utmost importance to ensure their safety during emergencies and rescue operations. An enhanced strategy for precise localization is imperative in this context. Therefore, a comprehensive exploration of node positioning algorithms within Wireless Sensor Networks (WSNs) is crucial to guarantee the safety of coal mine operations. This study introduces an innovative localization technique that relies on an entropy-weighted approach to refine the Received Signal Strength Indication (RSSI) measurements. The primary objective is to establish a more accurate distance measurement, achieved through a novel RSSI correction model known as the Entropy-weighted model. Subsequently, a genetic algorithm is employed to precisely determine the coordinates of the target node. The proposed technique is simulated using MATLAB, yielding promising results. The simulations clearly indicate that the algorithm effectively mitigates the adverse effects of environmental factors such as diffraction, multipath interference, and obstructions on the localization process. Furthermore, it significantly outperforms conventional methods in terms of accuracy, successfully meeting the stringent demands for precise personnel tracking in underground mining networks.