False Alarm Reduction in WSN Surveillance Application through ML techniques

To enhance target detection and tracking in real-world environments, it is crucial to consider the impact of weather and other environmental factors on sensor readings in addition to minimizing false alarms. This goal can be achieved through the implementation of machine learning algorithms that incorporate these parameters as predictors. To demonstrate the superiority of a machine learning-based solution in reducing false alarms and improving target detection accuracy in Wireless Sensor Networks (WSNs), the performance of algorithms such as K-Nearest Neighbors, Logistic Regression, Decision Trees, and Random Forest is compared. This paper incorporates environmental factors, such as weather, to improve target detection accuracy in real-world scenarios. The simulation results demonstrate the effectiveness of the proposed approach in reducing false alarms in WSNs using machine learning algorithms.