Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications
This addresses the problem of ensuring data accuracy for critical decisions in IoT applications, though it is incremental as it applies an existing method to a new context.
The paper tackles fault detection in IoT sensor nodes by applying the Dempster-Shafer Theory of Evidence to fuse sensor data, achieving high accuracy of 99.8% on benchmark data and 99.9% on laboratory data compared to other state-of-the-art methods.
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.