# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 5 Evaluation  
### Methods  
We evaluate the performance of MP-RDV through simulation and compared it with RDV and SADV. The main criteria in comparison is the basic communication performance measurement such as delivery ratio and delay under variations of vehicle density and data traffic amount. In addition, as a criterion for DTN routing, we measured the traffic load due to packet copies as well. We designed a scenario with the Kyoto city map in which we simulate the behavior of vehicles and packet delivery. We also measure the fault tolerance of MP-RDV under road failure; we designed a failure scenario in which roads are partially blocked, pretending a traffic accident occurred at the intersection. We developed our own simulator with C++ language to simulate several routing protocols in relatively sparse vehicular networks. Because we evaluate the performance of MP-RDV in a sparse scenario in which networks behave as a DTN, we do not care about PHY and MAC protocols. Thus, in our simulation, we just assume that a certain amount of packet exchanges is possible when a vehicle meets another vehicle or a static node, as is usually assumed in evaluation of DTN schemes. We obtained a Kyoto roadmap data set in Open Street Map [28] and removed small streets from it to retrieve major roads as shown in Figure 10. In this map, we have 104 intersections in total, at all of which we place traffic signals and static nodes. As a result, static nodes are placed only at major intersections in the Kyoto city map. To generate vehicle traffic over the map, we used a traffic simulator SUMO [29]. The map includes 20 road ends, at all of which we generate vehicles with a constant time interval. We set the maximum speed of vehicles as 50 km/h. In SUMO, we adopt a probabilistic mobility model; in every intersection, 90% of vehicles go straight, and 5% turn right and left, respectively. If vehicles are not allowed to go straight at an intersection, 50% of them turn left and the rest right. Each vehicle has a buffer (i.e., a transmission queue) that holds 128 data packets. We assume that each static node has a transmission queue for each previous hop (remember that RDV distinguishes previous hops to select vehicles to pass packets), the size of which is 1280 packets each. Nodes and vehicles have the communication range 50 m, i.e., vehicles work as _intersection mode_ if they are within the range of a static node, and as _road mode_ otherwise. In RDV and MP-RDV, every node updates its statistics and the routing tables every time the node exchanges messages with a vehicle. This assumption offers the most sensitive updates of the statistics and the routing table, while we can take choices of less frequent updates to reduce the computational load. We set the expected delivery ratio \(P=99\%\). As for other parameters of RDV and MP-RDV, for which detailed descriptions are seen in [13], we set values as follows: We set the capacity of multi-carry messages per vehicle \(M_{MC}=20\) (i.e., a vehicle deletes a multi-carry message randomly when the loaded messages exceed this value). We set the maximum number of hops for a 1-carry node \(N_{carry}=10\) (i.e., _1-carry_ links must be within 10-hops long). We set the minimum expected packet delivery ratio to be a relay node \(T_{SC}=70\%\) (i.e., 70% delivery ratio is required to establish _1-carry_ links). Figure 9: Illustration on computing delay metrics: The delay metric from \(n_{b}\) to \(n_{d}\) is computed as the sum of the waiting delay at \(n_{b}\) and the transportation delays on \((n_{b},n_{c})\) and \((n_{c},n_{d})\). For SADV, as preliminary tests, we measured the average delay of each link, i.e., each pair of neighbor static nodes with 1-hop distance, and used the fixed values in the shortest-path computation. We set TTL as the length of the shortest path plus 4, which allows using at most 4-hop longer paths than the shortest one as detour routes.