A New Mobility Samples Encoding Scheme Based on Pairing Functions and Data Analytics
In the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal.