Diagnosis of Faults in Wireless Sensor Networks Through Machine Learning Approach

Wireless sensor networks (WSNs) are susceptible to errors because they are deployed in hazardous and unpredictable environments. WSNs have many challenges when it comes to the identification of faults. Due to the variety of deployments and the limited resources of the sensors, it is difficult to precisely identify and diagnose faults in WSNs. In order to find the solution to this problem, supervised machine learning-based techniques are used to inspect the data from sensors to classify commonly occurring faults such as offset, drift, gain, data loss, stuck, and out-of-bounds at the sensor node level. The above-given faults were reproduced in normal data extracted from a reliable dataset. The simulation results show that the extra trees-based machine learning approach outperformed the random forest.