Data Integrity Monitoring Method of Digital Sensors for Internet-of-Things Applications

Data are the cornerstone of digital sensors for Internet-of-Things (IoT) applications, which have been widely used in various fields from social development to human life. However, data failure because of various faults caused by the human or environmental causes may result in the sensor system failure. In order to solve this issue, an online and credible data integrity monitoring (DIM) method for digital sensors is proposed in this article. Overall, this article contributes in the following ways. First, we model the data failure by dividing it into format failure, timing failure, and value failure. Second, several heuristic rules are proposed for the detection and isolation of data failure. Third, the control mechanism named dual variable-length data monitoring window is introduced to improve the credibility of data recovery. Then, we set up a motion tracking platform based on accelerometers, gyroscopes, and magnetometers, and conduct a series of experiments, such as numerical simulation experiments, static experiments, and turntable experiments to perform the comprehensive evaluation and verification on the performance of the proposed method. The results show that the proposed method can significantly improve the data quality. Finally, the tradeoffs among the real time, computational complexity, and credibility of the proposed method are discussed, and it is expected to be applied to other digital sensor systems for IoT applications.