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7. PAPERS SUMMARY The following sections provide a summary of the main points of interests of the papers: 7.1 Paper 1: Review of methods to predict connectivity of IoT wireless devices Manuel Lopez Martin, Antonio Sanchez-Esguevillas and Belen Carro. Review of methods to predict connectivity of IoT wireless devices Ad Hoc & Sensor Wireless Networks, Volume 38, Number 1-4 (2017), p. 125-141 1.034 Q4 2 (https://scholar.google.es/citations?user=3RSZbOYAAAAJ&hl=es) Published, July-2017 http://www.oldcitypublishing.com/journals/ahswn-home/ahswn-issue- contents/ahswn-volume-38-number-1-4-2017/ahswn-38-1-4-p-125-141/ 7.1.1 Objectives The main objective of this study has been the identification of machine learning techniques to forecast the on-off activity state of a large number of IoT mobile devices, using time-series and non-time-series methods. All the results are based on data from real IoT mobile devices. Additional goals of the study have been the analysis of the connectivity behaviour of real IoT devices and the creation of a generic dataset structure to be used by all the algorithms. 7.1.2 Datasets For this work we have employed a dataset composed of real IoT samples from a multinational operator. The data has been formatted to be used by both time-series and non-time-series algorithms. The dataset is the result of 6214 devices with 30 days of historical data, with a highly heterogeneous activity among the devices. The devices were active a 58% of the time (in average). 7.1.3 Models We have considered two groups of models: Authors Title Journal Impact Factor Quartile #Citations Status Link Doctoral Thesis: Novel applications of Machine Learning to NTAP - 67PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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