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

## TL;DR

This paper introduces a sensor fault detection method for IoT applications using Dempster-Shafer Theory of Evidence, achieving high accuracy in identifying faulty sensors from benchmark and lab data.

## Contribution

It applies Dempster-Shafer Theory to sensor fault detection in IoT, demonstrating superior accuracy over existing machine learning methods.

## Key findings

- 99.8% accuracy on benchmark data
- 99.9% accuracy on laboratory data
- Effective in detecting various sensor faults

## Abstract

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.