1 research outputs found
Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications
Fault detection in sensor nodes is a pertinent issue that has been an
important area of research for a very long time. But it is not explored much as
yet in the context of Internet of Things. Internet of Things work with a
massive amount of data so the responsibility for guaranteeing the accuracy of
the data also lies with it. Moreover, a lot of important and critical decisions
are made based on these data, so ensuring its correctness and accuracy is also
very important. Also, the detection needs to be as precise as possible to avoid
negative alerts. For this purpose, this work has adopted Dempster-Shafer Theory
of Evidence which is a popular learning method to collate the information from
sensors to come up with a decision regarding the faulty status of a sensor
node. To verify the validity of the proposed method, simulations have been
performed on a benchmark data set and data collected through a test bed in a
laboratory set-up. For the different types of faults, the proposed method shows
very competent accuracy for both the benchmark (99.8%) and laboratory data sets
(99.9%) when compared to the other state-of-the-art machine learning
techniques