Optimization of mobility sampling in dynamic networks using predictive wavelet analysis

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.