Abstract
Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where P=0.012<α=0.05, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments.
Highlights
Internet of Things (IoT)-Based Small Scale AnomalyIoT has brought enormous opportunities to allow the developments of a multitude of smart applications, namely health monitoring, smart city, smart transportation, and smart industry
We find that value 76.37 is statistically significant where P = 0.012 < α = 0.05, rejecting the null hypothesis for x6 data packet
This paper presents a novel IoTDixon methodology that can work on small-size data packets obtained from the given IoT dataset
Summary
IoT has brought enormous opportunities to allow the developments of a multitude of smart applications, namely health monitoring, smart city, smart transportation, and smart industry. The detection of outliers in a small-size dataset is a trivial task This situation may be exaggerated when applied for an IoT-based system which is resource constrained in nature. We propose the IoTDixon scheme to detect point anomalies from very small-size dataset for the IoT-based environment. To propose IoTDixon scheme to detect point anomalies from small-size dataset; To integrate Dixon’s Q test as a key statistic for detection of outlier points from small-size data packets; To integrate Kolmogorov–Smirnov test statistic as the normality checker. Novelty of the work: Our work is the first ever study that uses Dixon’s Q test and Kolmogorov–Smirnov test together to find small-size anomalies in IoT-based simulated scenarios.
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