The increasing interest in road safety improvement poses researchers attention on Vehicular ad Hoc Networks (VANETs). Their ability to handle vehicles communication through broadcasting and to increase traffic configuration awareness makes them a powerful means to achieve road security. Smart and compact representation of VANETs is then needed in order to collect the most useful information and to reach as much expressiveness as possible. Starting from a meaningful signal-based representation, our goal in this paper is to find affinities between different vehicular networks by identifying similarities between the corresponding waves. To this aim, we detect some parameters characterizing the signal and combine them with specific alignment techniques to obtain a similarity measure. We also experiment our approach by comparing the results against known signal similarity methodologies, such as the Dynamic Time Warping. © 2019 IEEE.
Network Signal Comparison Through Waves Parameters: A Local-Alignment-Based Approach / Balzano, W.; Murano, A.; Sorrentino, L.; Stranieri, S.. - Institute of Electrical and Electronics Engineers Inc.:(2019). (Intervento presentato al convegno 5th IEEE International Symposium on Measurement Networking, M&N 2019) [10.1109/IWMN.2019.8805047].
Network Signal Comparison Through Waves Parameters: A Local-Alignment-Based Approach
Balzano, W.;Murano, A.;Sorrentino, L.;Stranieri, S.
2019
Abstract
The increasing interest in road safety improvement poses researchers attention on Vehicular ad Hoc Networks (VANETs). Their ability to handle vehicles communication through broadcasting and to increase traffic configuration awareness makes them a powerful means to achieve road security. Smart and compact representation of VANETs is then needed in order to collect the most useful information and to reach as much expressiveness as possible. Starting from a meaningful signal-based representation, our goal in this paper is to find affinities between different vehicular networks by identifying similarities between the corresponding waves. To this aim, we detect some parameters characterizing the signal and combine them with specific alignment techniques to obtain a similarity measure. We also experiment our approach by comparing the results against known signal similarity methodologies, such as the Dynamic Time Warping. © 2019 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
