# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 2 Related Literature  
Fairness is important in many areas of human lives, e.g., sociology, economics and politics, and it is also true in technologies. In computer engineering, distinct computer resources should be shared equally amongst all processes and threads. In computer networking, all nodes require to attain the bandwidth and quality of service (QoS) equitably. In [23], fairness challenges and issues in wireless networks are thoroughly discussed, and some trade-offs between fairness and performance are reviewed. [PERSON] and [PERSON] [10] studied the trade-offs between fairness and efficiency of social-based routing algorithms in mobile social networks. They found that excluding popular nodes on the message forwarding significantly degrades the delivery efficiency. We [24] also showed that absolute traffic load fairness leads to the deterrent of delivery efficiency; yet, high delivery efficiency results in unfairness of traffic load. To overcome the problem, fair routing algorithms have been proposed for mobile social networks [11; 25; 26; 27]. [PERSON] et al. [11] introduced a fair routing strategy based on packet priority to improve fairness in success rate among nodes. [PERSON] et al. [25] proposed FSMF, a fair social aware message forwarding to solve the issues of imbalanced traffic load distribution as well as unfair delivery rate. [PERSON] et al. [26] proposed FairRoute that combines social strength and buffer queue length as the routing metrics to fairly distribute the traffic load among nodes. [PERSON] and [PERSON] [27] presented CafRep, an adaptive congestion aware forwarding strategy that diverts the traffic from congested nodes (popular nodes) to less congested nodes (unpopular nodes). Indeed, fair routing algorithms in distributed, intermittently connected wireless networks such as mobile social networks are more complex than those in conventional networks, such as the Internet, since: (i) negotiation and compromise amongst autonomous nodes is more complicated, for example non-cooperative nodes may be reluctant to help other nodes in forwarding; and (ii) due to the lack of knowledge about the global states, routing decisions are made solely based on nodes' local information. For the first issue, the impact of selfish nodes on delivery performance and resource consumption fairness has been investigated in [28]. In addition, to increase fairness in forwarding an incentive or a credit was applied on the routing decisions in [25]. Finally, in [29] a game theoretic approach is used to support fair cooperation among nodes in opportunistic networks. For the second issue, current works of fair routing schemes searched for proper nodes' locally available information to ensure a better fairness and efficiency trade-off. Furthermore, there are two sorts of node local knowledge which are commonly used to improve traffic fairness and reduce congestion: (i) buffer statistics and (ii) social measures. For the former case, some algorithms consider node burden, inferred from the node's buffer queue length, as the forwarding metric to achieve a balanced traffic distribution. For example, FOG [10] and GreBurD [30] prioritise nodes with higher residual buffer space as suitable relays to distribute load away from the congested nodes; CaffRep [27] defines node retentiveness, calculated as an expected weighted moving average of the node's remaining storage, as the congestion heuristic to detect storage congestion in popular nodes. For the latter case, on the other hand, researchers search for better social network measures for improving fairness in forwarding of social-based routing schemes.