## 5 Simulation Results
### Data Dissemination Results  
Now, the goodness of the obtained dissimilarity metric is evaluated under different probabilistic data dissemination schemes such as p-persistence algorithm, polynomial and irresponsible algorithm. The normalized Reachability (Re) and the number of redundant messages are used as performance metrics. The Re metric measures the percentage of vehicles that receive the broadcast message generated by the source nodes [24; 30]. The normalization of both metrics is done by dividing by the results of flooding scheme, which is used as an upper bound. Re should be as high as possible since it is always a major requirement of an efficient data dissemination scheme. Regarding the number of redundant messages, it is desirable to achieve a low redundancy since it is related to the congestion of the network. In previous works [24; 30], the p-persistence based on dissimilarity metric algorithm has been demonstrated to be the most appropriate for VANET scenarios with medium density levels such as the ones used in this letter. Therefore, p-persistence algorithm [18] is used as a baseline data dissemination algorithm to test the obtained metric. Algorithm 2 contains the pseudocode of the p-persistence algorithm based on the dissimilarity metric. _DMik_ represents the dissimilarity metric between vehicles \(i\) and \(k\), which is calculated using (6). ```
1:Whenever a message \(g\) is received
2:If \(g\) is new:
3:Retrieve neighboring list from \(g\)
4:Calculate \(a_{ik}\), \(a_{i}\), \(a_{k}\)
5:Calculate \(p\) as \(p=DM_{ik}\)
6:If \(p\geq\) Rand [0,1]
7:Include neighboring list in \(g\)
8:Rebroadcast \(g\)
9:Else:
10:Eliminate \(g\)
11:End if
12:Else:
13:Eliminate \(g\)
14:End if
```  
**Algorithm 2** P-persistence algorithm based on dissimilarity metric. Figure 5: Euclidean distance versus dissimilarity metric for 120 vehicles. Figures 6 and 7 show the obtained simulation results. The results are obtained by averaging out 20 different trials. Up to 30 random source nodes are selected in each trial [28]. It can be observed that the p-persistence algorithm based on the obtained dissimilarity metric achieves better results in terms of \(\mathbf{R}\mathbf{e}\) than in the cases that used classical dissimilarity metrics and the Euclidean distance. The percentage of improvement with respect to the best classical dissimilarity metric (Sokal-Sneath) is within the interval [0.7, 6]. In addition, the obtained new metric clearly outperforms the results of the Euclidean based p-persistence. Regarding redundancy, the proposed approach outperforms flooding and it is closed to Sokal-Sneath metric. Regarding polynomial scheme, Algorithm 3 contains the pseudocode used in this work. Again, _DMik_ represents the dissimilarity metric between vehicles \(i\) and \(k\), which is calculated using (6). The value of \(\mathbf{b}\) is 2 since higher values of \(\mathbf{b}\) have been already tested, showing low performances [30]. Figure 8 shows the obtained simulation results in terms of \(\mathbf{R}\mathbf{e}\). It can be observed that the majority of classical dissimilarity metrics do not  
```
1:Whenever a message \(g\) is received
2:If \(g\) is new:
3: Retrieve neighboring list from \(g\)
4: Calculate \(\mathbf{a_{ik}}\), \(\mathbf{a_{i}}\), \(\mathbf{a_{k}}\)
5: Calculate \(p\) as \(p=\left(DM_{ik}\right)^{b}\)
6: If \(p\geq\) Rand [0,1]
7: Include neighboring list in \(g\)
8: Rebroadcast \(g\)
9: Else:
10: Eliminate \(g\)
11: End if
12: Else:
13: Eliminate \(g\)
12: End if
```  
**Algorithm 3** Polynomial algorithm based on dissimilarity metric. Figure 8: Comparison of Reachability results for the polynomial scheme based on the different dissimilarity metric. Figure 9: Comparison of redundancy results for the polynomial scheme based on the different dissimilarity. With regard to the irresponsible scheme, Algorithm 4 shows the actual implementation of the scheme based on dissimilarity metric.