# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 2 Related Literature
For example, FairRoute [26] improves the calculation of pairwise tie strength based on the short-term and long-term relationships; SimBet [20] adds connection strength information to the routing metrics to offload traffic from popular nodes; Socially-Aware Prediction (SAP) [31] estimates future contacts based on the node (social) similarity, and forwards messages to nodes with a higher similarity with the destinations, thus reducing messages forwarded to globally popular nodes. As opposed to [20; 26; 31], which focus on improving the calculation of destination-dependent (DD) utility metrics, our proposed scheme TraLDA chooses to improve the computation of node popularity in the network, since as noted in [16], this destination-independent (DI) utility metric primarily contribute to the traffic imbalance among nodes in mobile social networks. In social network analysis, [PERSON] [32] proposed three distinct centrality measures to identify the importance of nodes (individuals) in social networks, namely degree centrality, betweenness centrality and closeness centrality. Degree centrality is the number of direct neighbours or friends a node has; betweenness centrality is the number of shortest paths connecting any two nodes that pass through a given node; and closeness centrality is the average distance (proximity) between a node and all other nodes in the network. [PERSON]'s centrality metrics have been widely used to detect nodes which are capable of disseminating information in mobile social networks; for example, BubbleRap [21] and SimBet [20] consider degree centrality and betweeness centrality, respectively, computed in a distributed, ad-hoc fashion to determine node popularity. In BubbleRap, node degree is calculated as the cumulative average of total number of distinct peers encountered by the node in all previous time windows. In SimBet, node betweenness centrality is computed based on a binary model of a social relation, i.e., a value of \"1\" means two nodes know each other and \"0\" otherwise. However, we argue that the node popularity or centrality calculations in BubbleRap and SimBet do not cope with the dynamics of a social network. Furthermore, as confirmed in [18; 19] human activity typically exhibits a regularity (periodicity) pattern. Considering this matter, as our first contribution in this paper, we propose a novel method to calculate node inherent popularity at a given time interval based on the Kalman prediction [17] which takes into account the node's periodicity behaviour. Nevertheless, [PERSON]'s centrality measures typically disregard the influence of the neighbours. The authors of [33] argued that a node's importance in the social network should also be determined by the importance of its neighbours. In [34], the authors studied a strategy to find persons that are able to spread advertisements as far as possible in a social network. They showed that a person that receives high respects from her friends, her advertisements will be highly probable to spread over the social network quickly. In addition, [PERSON] and [PERSON] [35] integrated the concept of social networks and IoT to determine the reputation of IoT objects. They proposed a formula to calculate reputation of an object in a social Internet of Things based on the well-known Google PageRank. In that technique, the reputation of an object is determined by the level of trust it obtains from other IoT objects. Almost similar, [PERSON] et al. [36] attempted to introduce concepts and behaviours of social networks into the IoT settings. In that work, to measure the reputation of an IoT object, the authors defined Impact Degree, calculated as the average trust degree that the object receives from the other objects in its scope (neighbourhood). Meanwhile, from the social network theory, there exist centrality measures that consider a richer range of direct and indirect influence of neighbours, such as the [PERSON]'s prestige measure [37].