# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 5 Simulation and Discussion
### Simulation Results and Discussions
#### 5.2.2 SimBet vs. SimBet-TLDA  
For the last TraLDA's analysis, we now consider SimBet routing [20]. SimBet uses two distinct social properties, namely betweenness centrality and social similarity, to calculate  
Figure 11: (**left**) Social impact factor (\(\xi\)) vs. GINI index, and (**right**) social impact factor (\(\xi\)) vs. delivery latency of Bubble-TLDA for Reality (\(F_{th}=150\) ks) and Sassy (\(F_{th}=2\) ks). A social impact factor represents the contribution of neighbours’ influence on the node popularity. A higher \(\xi\) leads to a lower GINI index, but somewhat increases the delivery delay. Figure 10: (**left**) The traffic load distribution among nodes in Reality for BubbleRap, and (**right**) for Bubble-TLDA (\(\xi=0.8\), \(F_{th}=150\) ks). Clearly, the improved node popularity calculation of TraLDA on BubbleRap significantly reduces the traffic load in the hub nodes, while increasing the relay traffic in majority of non-hub nodes. node utility to a given destination. Both the SimBet's utility metrics are calculated based on a binary model of a social connection, where a value of \"1\" denotes that a pair of nodes have known each other, and \"0\" otherwise. The binary social relationships may create a substantial issue in forwarding fairness, since a node having large contacts with other nodes will always be considered as the popular nodes regardless of time. Using the graph with binary links, SimBet computes node betweenness centrality based on an ego-centric network approach, since the global network topology information is unavailable for nodes in MSNs. Node social similarity, on the other hand, is calculated as the number of common encountered nodes between a pair of nodes. In the end, the SimBet utility of a node is computed as the weighted combination of betweeness centrality and similarity, with a parameter \(\alpha\) which balances the two metrics' respective relevance. However, for SimBet-TLDA, we modify the calculation of node betweenness of SimBet with the calculation of node global popularity of TraLDA using (16). Eventually, in Figures 12 and 13 we depict the delivery performances of SimBet and SimBet-TLDA for Reality and Sassy, respectively. For TraLDA's social-relations parameters, namely friendship threshold (\(F_{th}\)) and social impact factor (\(\xi\)), we again consider the similar settings used in the previous investigation of Bubble-TLDA. Moreover, for both SimBet and SimBet-TLDA we choose a weighting parameter \(\alpha=0.5\), assigning an equal importance to the global popularity and social similarity utilities in both Reality and Sassy. The major purpose of SimBet-TLDA is to enhance traffic load balancing across network nodes, and as seen in Figures 12 and 13 (lower-left), the GINI index performance of SimBet-TLDA can outperform that of SimBet. As previously stated, using binary relationships to calculate node centrality makes a node's utility relatively constant over time, ignoring the dynamics of human behaviour. As a result, majority of network traffic is directed to the most central nodes (hub nodes), creating a traffic imbalance in the network. A node's centrality in SimBet-TLDA, however, is determined by considering both the periodicity of human activities as well as the centrality of the neighbours of the nodes. This can reduce the traffic in the most central nodes and distributes the traffic more equitably across the network nodes, indicated by the reduce of GINI index in both mobility scenarios. Figure 12: Performance evaluation of SimBet and SimBet-TLDA (\(\xi=0.8\)) for the Reality mobility scenario. As in the case of BubbleRap, in this case TraLDA is also able to substantially reduce the SimBet’s GINI index, without much affecting other delivery performances. Furthermore, the GINI index reduction in SimBet-TLDA is more obvious in the case of lower friendship thresholds (\(F_{th}\)).