|Autor:||H. Füßler, M. Torrent Moreno, M. Transier, R. Krüger, H. Hartenstein, W. Effelsberg||Links:|
|Quelle:||ACM International Conference on Mobile Computing and Networking (MOBICOM), Cologne, Germany, August 2005|
The mobility of the nodes in a Mobile Ad-Hoc Network (MANET) is a crucial factor in the performance studies of communication protocols for these kind of networks. For this reason, researchers usually use a randomized node movement model, such as the Random Way-Point Model , in the process of designing or analyzing the behavior of their protocols. Since movement is not very predictable in these scenarios, they serve as a “worst case assumption” of node mobility concerning communication protocol performance in the sense that a positive correlation between performance in an RWP scenario and an arbitrary scenario exists. Additionally, RWP is an analytically well-understood mobility scheme and the movements can be generated very easily with tools complementary to most of the common network simulators. Vehicular Ad-Hoc Networks (VANETs) are one type of MANETs that is recently attracting the attention of both industry and academia. VANETs are characterized by the—usually—streetbound scenarios due to the special kind of their nodes, i.e., vehicles. Recent research  though, shows that protocol performance in VANETs is quite different from performance in RWP scenarios and specializing protocols in these scenarios can be both challenging but also beneficial. In this work, we present a set of real scenarios of one of the common situations in vehicular environments, i.e., highways. Additionally, an analysis from a connectivity perspective has been performed intending to support the design and/or study of communication protocols tailored to this specific environments. Starting from reality-audited highway movement data generated for the FleetNet project , we have created a set of node movement traces especially for the use with network simulators like ns-2 . In addition, we provide a tool, called HWGui, for the visualization and computation of the set of statistics dealing (a) with the movements itself and (b) with the communication consequences assuming a Unit Disk Graph  radio connectivity model. A screen shot of HWGui can be found in Figure 1 showing a highway scenario with nodes moving from left to right. The yellow lines depicts the “being in each others range” probability of two cars. For further information on the software and for downloading the movement traces and the software, please refer to [1, 4]. The next section will briefly outline statistical examples.