The field of mobility prediction has been widely investigated in the recent past, especially the reduction of the coverage radius of cellular networks, which led to an increase in hand-over events. Changing the cell coverage very frequently, for example, may lead to service disruptions if a predictive approach is not deployed in the system. Although several works examined mobility prediction in the new-generation mobile networks, all of these studies focused on studying the time features of mobility traces, and the spectral content of historical mobility patterns was not considered for prediction purposes as yet. In the present study, we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency. The proposed approach lies in the mobility analysis in the frequency domain, to extract hidden features of the mobility process. Thus, we proposed a new methodology to determine the spectral content of mobility traces (considered as signals) and, thus, the appropriate sampling frequency, which can provide numerous advantages. We considered several types of mobility models (e.g. pedestrian, urban, and vehicular), containing important details in the time and frequency domains. Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.

Effects of sampling frequency on node mobility prediction in dynamic networks: A spectral view

Fazio, Peppino
Conceptualization
;
2022

Abstract

The field of mobility prediction has been widely investigated in the recent past, especially the reduction of the coverage radius of cellular networks, which led to an increase in hand-over events. Changing the cell coverage very frequently, for example, may lead to service disruptions if a predictive approach is not deployed in the system. Although several works examined mobility prediction in the new-generation mobile networks, all of these studies focused on studying the time features of mobility traces, and the spectral content of historical mobility patterns was not considered for prediction purposes as yet. In the present study, we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency. The proposed approach lies in the mobility analysis in the frequency domain, to extract hidden features of the mobility process. Thus, we proposed a new methodology to determine the spectral content of mobility traces (considered as signals) and, thus, the appropriate sampling frequency, which can provide numerous advantages. We considered several types of mobility models (e.g. pedestrian, urban, and vehicular), containing important details in the time and frequency domains. Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5006449
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