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III. PAPERS PAPER 1 Review of methods to predict connectivity of IoT wireless devices Manuel Lopez Martin, Antonio Sanchez-Esguevillas and Belen Carro Dpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain ; mlopezm@acm.org ; antoniojavier.sanchez@uva.es; belcar@tel.uva.es Abstract: 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. 1. Introduction With the advent of IoT there is an exponential growth of wireless sensors and wireless devices in general that need a wireless connection to send and/or receive information. These wireless devices may act alone, e.g. a wearable like a smartwatch or smartband connecting to a smartphone or belong to a complex system like a smart home, e.g. a temperature sensor sending outdoor temperature to the residential gateway that controls the whole smart home system. Therefore, there is a huge number of wireless devices connecting on a frequent basis (periodic or aperiodic) to some central collecting server, many of these devices being wireless Doctoral Thesis: Novel applications of Machine Learning to NTAP - 95PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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