Building An Anomaly Detection Engine (ADE) For IoT Smart Applications

Data Analytics is by far the component with more added value in Internet of Things (IoT) networks. One aspect of data analytics is anomaly detection within data points received in some cases in real time that help to conduct predictive maintenance, weather monitoring or cyber security forensics for instance. Although there exists a number of web dashboards that allow IoT users to visualize data in time domain and perform statistical analysis, anomaly detection is often absent else if present not that straightforward, reliable and accurate. The development and implementation of Anomaly Detection Engine (ADE) poses a number of challenges that are in fact addressed in this paper. The research work exposes the multifaceted aspect of IoT networks and applications based on real life use cases and the difficulties engendered in mounting an ADE from both software system engineering and network convergence perspectives. Moreover a comparative description of diverse time series models adopted in anomaly detection is undertaken. It was noticed that there is neither one size fit all solution nor a plug n play alternative and that the unsupervised mode in machine learning as a model for time series analysis is the most versatile and efficient technique for IoT analytics developers.