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Collision Prediction for a Low Power Wide Area Network using Deep Learning Methods
Shengmin Cui,Inwhee Joe 한국통신학회 2020 Journal of communications and networks Vol.22 No.3
A low power wide area network (LPWAN) is becominga popular technology since more and more industrial Internet ofthings (IoT) applications rely on it. It is able to provide long distance wireless communication with great power saving. Given thefact that an LPWAN covers a wide area where all end nodes communicate directly to a few gateways, a large number of devices haveto share the gateway. In this situation, chances are many collisionscould occur, leading to waste of limited wireless resources. However, many factors affecting the number of collisions that cannotbe solved by traditional time series analysis algorithms. Therefore,deep learning methods can be applied here to predict collisions byanalyzing these factors in an LPWAN system. In this paper, wepropose long short-term memory extended Kalman filter (LSTMEKF) model for collision prediction in the LPWAN in terms of thetemporal correlation which can improve the LSTM performance. The efficacies of our models are demonstrated on the data set simulated by LoRaSim.