# Improving Traffic Load Distribution Fairness in Mobile Social Networks
###### Abstract
In DD, node utility is dependent of the destination; i.e., node \(i\) is an optimal relay for one destination \(d_{1}\), yet node \(j\) is the best one for another \(d_{2}\), or \(U_{i}(d_{1})>U_{j}(d_{1})\), but \(U_{i}(d_{2})<U_{j}(d_{2})\) for \(d_{1}\
eq d_{2}\). DD functions could be based on last-contact, social similarity, or correlated mobility pattern, with the given destination. However, destination-dependent (DD) imposes a large overhead on nodes, since the nodes should keep a single entry for each peer in the network. As opposed to DD, node utility in DI is independent of any destination, for example, a single node may be the best carrier for most/all destinations in the network, or in general it holds that \(U_{i}(d_{1})>U_{j}(d_{1})\) then \(U_{i}(d)>U_{j}(d)\) for most/all \(j,d\). Instances of nodes which are better relays for all destinations would be those with many connections to others (e.g., hub nodes in scale-free networks), nodes with many acquaintances (e.g., popular nodes in social networks), or nodes with high mobility (e.g., cars or buses in vehicular delay-tolerant networks). Nevertheless, destination-independent (DI) imposes a higher forwarding overhead on better relays, leading to poorer fairness in both traffic load distribution and utilisation of the nodes' resources. This paper proposes a framework called _Traffic Load Distribution Aware_ (hereafter, TraLDA), aiming to improve fairness in forwarding of social-based routing algorithms. Here, we introduce a novel computation of node (global) popularity in the entire network. This utility metric is obviously independent of the message destination, and it may contribute to a traffic load imbalance across nodes, as mentioned in [16]. In TraLDA, we consider two different popularities in the calculation of node popularity, namely inherent popularity and social-relations popularity. Inherent popularity is based solely on the node's sociability level, and in TraLDA it is computed using the Kalman prediction [17] which considers the periodicity in human behaviour. The works in [18; 19] confirmed that human activities typically exhibit some periodicity. Consequently, the calculation of node popularity in mobile social networks should consider this property. Social-relations popularity, on the other hand, reflects the social benefit of connections with popular nodes, and spreads the popularity of these nodes to their lower ranking acquaintances. Finally, we apply the TraLDA's node popularity computation on the classical, yet prominent social-based routing algorithms SimBet [20] and BubbleRap [21], and next investigate the performance improvements of these routing schemes, particularly in the trade-off between forwarding fairness and efficiency. SimBet and BubbleRap basically combine two different utility metrics to decide node fitness as relay to a given destination: the one which is dependent of the destination (i.e., similarity and social community in SimBet and BubbleRap, respectively), and the other one which is independent of the destination (i.e., betweenness centrality and global popularity in SimBet and BubbleRap, respectively). In the present case, TraLDA focuses on improving the calculation of global popularity and betweenness centrality in BubbleRap and SimBet, respectively. The followings are the main contributions we made in this paper:  
1. To increase fairness in forwarding of social-based routing algorithms in mobile social networks, we propose TraLDA, a framework of traffic load distribution aware. We offer a new method for calculating node global popularity, a function of both node inherent and social-relations popularity. 2. The inherent popularity of a node is solely determined by the node's own mobility pattern or sociability level in the network, and in TraLDA it is computed using the Kalman prediction which accounts for the regularity (periodicity) of human behaviour. 3. Node social-relation popularity, on the other hand, represents the advantages of connections with more popular or central nodes (individuals).