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In Vehicular Ad-hoc NETworks (VANETs), it is important to consider the quality of the path used to forward data packets. Because of the fluctuating conditions of VANETs, stringent requirements have been imposed on routing protocols and thus complicating the entire process of packet delivery. To determine which path is the best, a routing protocol relies on a path assessment mechanism. In this paper, the problem of link quality estimation in VANET networks is addressed. Based on the information gathered from the packet decoding errors at the physical layer, a novel link quality estimator is proposed. The proposed link quality estimator named LSENN for Link State Estimator based on Neural Networks, has been tested under realistic physical layer and mobility models for reactivity, accuracy and stability evaluation.

Data rate management algorithms aim to perform a proper selection of the signal modulation and the coding rate to avoid the corruption of data bits. This paper describes a preliminary investigation on the bit corruption pattern related to the IEEE 802.11p standard. Measurements have been acquired with an experimental test-bed made up with a couple of software radios to perform white-box tests. Software radios are stationary and operate on the same channel without disturbances coming from concurrent communication. The aim of this experimental test-bed is to represent a static scenario where vehicles are stationary such as a crossroad situation. The data analysis shows that a data length reduction as an impact as much as a decrease of the data rate. A deeper analysis of the data bit corruption distribution highlights that some bits are more corrupted than others, rejecting the independent and identically distributed assumption for some situations. This opens a perspective to design algorithms dealing with multiple constraints, even if they are NP-complete.