Metrics
Deploying and testing VANETs involves high cost and intensive labor. Hence, simulation is a useful alternative prior to actual implementation. VANET simu- lation is fundamentally different from MANET (Mobile Ad Hoc Networks) sim- ulation because, in VANETs, the vehicular environment imposes new issues and requirements, such as constrained road topology, multi-path fading and roadside obstacles, traffic flow models, trip models, varying vehicular speed and mobility, traffic lights, traffic congestion, and drivers’ behavior [BFW03]. Fortunately, the increasing popularity and attention to VANETs has prompted researchers to de- velop accurate and realistic simulation tools.
Simulation results presented in this Thesis were obtained using the ns-2 simu-
lator [FV00], modified to consider the IEEE 802.11p standard1.
Figure 2.15: Example of visibility in RAV.
Ns-2 simulator is a discrete event simulator developed by the VINT project research group at the University of California at Berkeley. The simulator was extended by the Monarch research group at Carnegie Mellon University [CMU01] to include: (a) node mobility, (b) a realistic physical layer with a radio propagation model, (c) radio network interfaces, and (d) the IEEE 802.11 Medium Access Control (MAC) protocol using the Distributed Coordination Function (DCF).
In terms of the physical layer, the data rate used for packet broadcasting in our simulations is 6 Mbit/s, as this is the maximum rate for broadcasting in 802.11p. The MAC layer was also extended to include four different channel access priorities. Therefore, application messages are categorized into four different Access Categories (ACs), where AC0 has the lowest and AC3 the highest priority. The purpose of the 802.11p standard is to provide the minimum set of specifica- tions required to ensure interoperability between wireless devices when attempting to communicate in potentially fast-changing communication environments. For our simulations, we chose the IEEE 802.11p because it is expected to be widely adopted by the industry.
The simulator was also modified to make use of the Real Attenuation and
Visibility (RAV) radio propagation model [MFT+13].
The main objective that a realistic visibility scheme should accomplish is to determine if there are obstacles between the sender and the receiver which interfere with the radio signal. In most cases, when using the 5.9 GHz frequency band (used by the 802.11p standard), buildings absorb radio waves and so communication is not possible. RAV goes one step forward by adapting the algorithm to support more complex and realistic layouts. Given a real reference map containing the street layout, RAV basically states whether two different vehicles are in line-of- sight.
narios based on real roadmaps from all over the world. C4R relies on both the OpenStreetMap [Ope12] tool to get the real roadmaps, and SUMO [KEBB12] to generate the vehicles and their movements within these scenarios. OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world, which is being built largely from scratch, and released with an open content license. The Simulation of Urban MObility (SUMO) is an open source, microscopic, space- continuous traffic simulator designed to handle large road networks. C4R is able to import maps directly from OpenStreetMaps, and make them available for being used by the ns-2 simulator.
With regard to data traffic in our simulations we consider that vehicles operate in two modes: (a) warning mode, and (b) normal mode. Warning mode vehicles inform other vehicles about their status by sending warning messages periodically with the highest priority (AC3) at the MAC layer; each vehicle is only allowed to propagate them once for each sequence number. Normal mode vehicles enable the diffusion of these warning packets and, periodically, they also send beacons with information such as their positions, speed, etc. These periodic messages have lower priority (AC1) than warning messages, and so they are not propagated by other vehicles.
Along this document, several metrics will be used to measure the performance of the different proposals. In particular, we are interested in the following perfor- mance metrics:
• Percentage of informed vehicles: This metric represents the percentage of vehicles that receive the warning messages sent by warning mode vehicles. During the warning message dissemination process, the most important ob- jective to accomplish consists on informing the highest number of vehicles in the shortest time possible, thereby we consider that this metric is a key factor to assess the dissemination process.
• Number of messages received per vehicle: This metric represents the number of messages received per vehicle, including beacons and warning messages. In particular, it gives an estimation of channel contention, and of the overhead of the dissemination scheme.
The number of messages produced by a given dissemination scheme may become very important in VANETs due to the high number of messages
sent and received by the vehicles involved in the communication process. This could increase channel contention and the frequency of collisions. • Warning notification time: This is the time required by normal vehicles
to receive a warning message sent by a warning mode vehicle. This met- ric indicates the time evolution of the dissemination process. Since several schemes could achieve a similar percentage of informed vehicles, the time required to achieve this could be critical, especially in safety applications.