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Fault detection in sensor nodes is a pertinent issue that has been animportant area of research for a very long time. But it is not explored much asyet in the context of Internet of Things. Internet of Things work with amassive amount of data so the responsibility for guaranteeing the accuracy ofthe data also lies with it. Moreover, a lot of important and critical decisionsare made based on these data, so ensuring its correctness and accuracy is alsovery important. Also, the detection needs to be as precise as possible to avoidnegative alerts. For this purpose, this work has adopted Dempster-Shafer Theoryof Evidence which is a popular learning method to collate the information fromsensors to come up with a decision regarding the faulty status of a sensornode. To verify the validity of the proposed method, simulations have beenperformed on a benchmark data set and data collected through a test bed in alaboratory set-up. For the different types of faults, the proposed method showsvery competent accuracy for both the benchmark (99.8%) and laboratory data sets(99.9%) when compared to the other state-of-the-art machine learningtechniques.

Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications