Mobile Ad Hoc Network Protocols Based on Dissimilarity Metrics by M. Güneş D. G. Reina J. M. Garcia Campos & S. L. Toral

Author:M. Güneş, D. G. Reina, J. M. Garcia Campos & S. L. Toral
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

75

11.94

0.74

0.72

0.72

0.71

0.75

100

16.44

0.79

0.77

0.76

0.76

0.80

125

19.72

0.82

0.80

0.79

0.79

0.83

150

23.73

0.84

0.82

0.82

0.81

0.85

175

27.47

0.87

0.85

0.84

0.84

0.87

200

31.15

0.89

0.87

0.87

0.86

0.88

As the density level increases, the correlation between a dissimilarity distance and the Euclidean distance also increases. Beyond 75 nodes, all dissimilarity metrics are highly correlated to the Euclidean distance (Pearson coefficient higher than 0.75). That means that for medium and high density networks the presented dissimilarity metrics are good estimators of the Euclidean distance. Among the discussed metrics, the Sokal distance shows in general the highest correlation to the Euclidean distance. In contrast, the Kulczynski distance shows the least correlation to the Euclidean distance. However, in any case, the obtained results demonstrate the correlation between the dissimilarity distances and the Euclidean distance.

Figure 4.2 shows the results for the Sokal distance obtained via simulations with a network consisting of 100 nodes (the network density was 0.001 nodes/m2). Figure 4.2 depicts the histograms of the Euclidean distance among nodes in the simulations, the scatter plot between the Sokal distance and the Euclidean distance, and the QQ-Plot between the distributions of the Sokal distance and the Euclidean distance. The QQ-Plot is particularly useful to study the relationship between both distributions. As it can be seen from the graph there is a high linear relationship between the Sokal distance and the Euclidean distance in the range of [0.2, 0.9]. This means that we can use the Sokal distance to estimate the Euclidean distance with high precision in this range.

Fig. 4.2Results for the Sokal distance, based on simulations with a network consisting of 100 nodes and communication range r = 250 m

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