Sensor nodes deployed in wireless sensors networks (WSN) are prone to continuous failure or disconnected from the network due to factors such as battery lifespan or failure, communication failure, hardware or software problems, and sometimes environmental effects. Once the node has failed and disconnected, new sensor nodes are redeployed in the field irrespective of where they are installed. This is time-consuming and challenging especially when deployed in an unfriendly environment. Therefore, This project simulate WSN environment for node failure prediction and monitoring model based on the machine learning (ML)technique. This will help in achieving resilient WSN by detecting a possible sensor node failure early in the network through monitoring and predictions using five traditional supervised ML Algorithms.
##Requirements:
-MATLAB R2019,R2020,R2021
-GUIDE (Graphical User Interface Design Environment).