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Improving a Smart Environment with Wireless Network User Load Prediction

Over the years, wireless networks have been suffering a significant increase in the number of connected users, and dealing with this increase is extremely important in terms of economy and quality of service. In this work, a prediction model is proposed to improve this relationship, focusing on predicting the number of connected users to the wireless network. Our model consists of a particle swarm optimizer applied to the parameters of the Multilayer Perceptron neural network. The model was evaluated with real mobility data obtained from wireless networks with a total of more than twenty thousand users. The predictions made by the model allow allocating the network bandwidth efficiently, generating savings in the available resources. In fact, the simulated results indicate an average coefficient of determination of 94.08% and average savings of 67.31 % of the total available bandwidth.