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Review of Methods to Predict Connectivity of IoT Wireless Devices
Manuel López Martín, Antonio Sánchez-Esguevillas and Belén Carro

Services related to Internet of Things (IoT) demand agile anticipation and response to eventual lack of service continuity. Machine learning methods may obtain predictions of IoT wireless sensors and devices connectivity patterns, enabling telcos to adjust maintenance periods, plan network upgrades, estimate outages risk and best allocate customer value to connection resources.

This article analyses how different algorithms forecast near/medium term connectivity of IoT wireless devices, based on their historical activity. The study considers time-series algorithms (Hidden Markov Model, exponential smoothing, AutoRegressive Integrated Moving-Average (ARIMA)), non-time-series algorithms (logistic regression, bayesian logistic regression, random forest, Gradient Boosting Method), mixed approaches (ARIMA with eXogenous covariates (ARIMAX)) and combinations of classifiers. Real obfuscated data obtained from a telecommunications operator is employed. Results present very advantageous prediction performance of IoT connectivity wireless devices, with an accuracy of over 90 percent most of the time, and even higher for the best performing algorithms.

Keywords: Machine learning algorithms; Internet of Things; time-series prediction; wireless sensors; wireless devices.

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