# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Simulation Settings
The routing filter mitigates the propagation of unintentional copies generated by the local recovery procedures of routing protocols when a frame transmission fails because of the loss of an ACK frame. All of the figures are presented with Confidence Intervals (CI) of 95%, obtained from 20 simulations per each density value, a GHR setup combination and using different movement traces per each simulation. Table 1 summarizes the main simulation settings. In the first step, we employed MMMR [20] in the core of our routing proposal GHR to evaluate the different configurations that GHR can provide. Multi-Metric Map-aware Routing (MMMR) is a position-based, traffic-aware and delay-tolerant protocol based on the Greedy Perimeter Stateless Routing (GPSR) [8]. MMMR considers four metrics instead of only one as GPSR to select the next forwarding hop among its neighbors. These four metrics evaluate the distance to the destination, the vehicle trajectory, the vehicle density and the available bandwidth. MMMR will be in charge of scoring the neighbors and choosing the best one among them. As the second step, we used the best configurations of GHR, found with MMMR as the score algorithm, to test these configurations with a classical DTN routing protocol, named Greedy-DTN in [4], in the scoring procedure of GHR. The greedy-DTN protocol is a variation of GPSR that replaces the perimeter mode by the carry and forwarding approach. The greedy-DTN protocol uses only distance to destination to select the next forwarding node. We present this comparison to provide an idea of the role of the scoring algorithm. Even more, this shows how GHR can be easily used for any DTN position-based routing protocol that makes routing decisions in a hop by hop fashion. In addition, we have compared these two protocols, i.e., GHR-MMMR, GHR-greedy-DTN, against a modified version of AODV for VANETs called irresponsible AODV [33], which was especially adapted for VANETs because it reduces the number of route request signaling messages to establish an end-to-end path. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Map zone & \multicolumn{2}{c}{Example district of Barcelona} \\ Area & 1.5 km \(\times\) 1 km \\ Number of nodes & 100, 150, 250 vehicles \\ Vehicle density & 67, 100, 166 \(\frac{\text{veh}}{\text{km}^{2}}\) \\ Mobility generator & SUMO [24]/C4R [23] \\ Mobility model & Krauss [30] \\ Max speed & 60 km/h \\ GPS precision radius error & U(0,10) m \\ \hline Simulation time & 300 s \\ Inter-packet generation time & \(T\)\(\sim\)U(2,6) s E(\(T\)) = 4 s \\ Packet size & 1000 bytes \\ \hline Path loss model & Empirical IEEE 802.11p radio shadowing [31] \\ Fading model & Rician (LOS), Rayleigh (NLOS) \\ Power transmission & 23 dbm \\ Receiving sensing & \(-\)82 dbm (\(\sim\)400 m in LOS) \\ Routing metric algorithms & MMMR [20] Greedy-DTN [4; 32] \\ Address resolution & CAAR [28] \\ MAC specification & IEEE 802.11p \\ Bandwidth & 6 Mbps \\ CW mechanism & CW for congestion control [27] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation settings. Notice that the Euclidean distances used by the three protocols, i.e., MMMR, greedy-DTN and iAODV, are computed through the positions provided by the GPS on-board units of the vehicles. Every node includes its own position in the hello messages received by its neighbors. It is known that the GPS positions have an error, whose radius typically ranges from zero to 10 meters [34]. In our simulations, every time a node queries its position from the GPS unit, the simulator adds a uniform random error with a radius from zero to 10 meters to the exact position. This way, we mimic what happens in realistic GPS devices.