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sensors that send environmental information to a server or to other devices. The information received by the server can be part of a business service provided to a final customer or can be part of operating services needed to fulfil other business life-cycle activities (fault management, activity mediation, billing...). These unattended devices, connecting in an automatic way to other devices or to a central server, are part of the IoT, where thousands if not millions of these devices provide a complex and inter-twined network. Needless to say, that IoT will be one of the mainstreams of automation and service delivery in the following years and their growth and importance is increasing rapidly. From the point of view of an IoT Service Operator (namely a telecommunications operator) it is extremely useful to know in advance the probability distribution of wireless devices connectivity. Forecasting how likely is for a wireless device to send or not to send information during a period of time in the future is important, in order to: 1) anticipate business impact, 2) accommodate maintenance activity periods to lower the impact in connectivity, 3) enhance infrastructure to reduce risk for highly important services and associated devices. This paper shows the results of applying different machine learning algorithms to forecast the activity/no-activity (on/off) of a wireless device connection, based on its past activity. For us, the activity of a device in a certain period is a binary value; either it is “on” when the device has sent data during that period, or, “off” when, otherwise, the device has not sent any data during that period. A large group of wireless devices with heterogeneous connectivity patterns has been used. The results of applying the various methods to real wireless connections are compared (no simulated data was used in this work). The prediction accuracy has been used as the performance indicator for the analysis, obtaining its mean value throughout several hours of prediction. The paper is organized as follows: Section 2 describes the followed methodology. Section 3 describes the results obtained from the different methods. Section 4 presents related works and finally, sections 5 and 6 provide the discussion and conclusions. 2. Materials and Methods The objective has been to identify the best algorithm able to predict the on/off connection activity for periods of one hour in a foreseen window of two days (48 hours). We have considered the device to have connection activity as far as it sent any data during the one-hour period. 2.1. Methodology and tools The steps followed are the typical ones of data science, namely: • Perform a preliminary exploration/analysis of the available historical data • Propose the prediction methods to use • Pre-process the historical data • Establish possible validation methods • Define the forecasting accuracy comparison method (so called cost/utility function, in order to minimize forecasting error) which allows to benchmark the results of the different methods • Obtain results from different methods • Analyze results, propose adjustments or new methods to explore Doctoral Thesis: Novel applications of Machine Learning to NTAP - 96PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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