# Improving Traffic Load Distribution Fairness in Mobile Social Networks
###### Abstract  
Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most \"social\" nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node's sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node's periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance. f aircle 20222022022022202  
performance [10]. In addition, the poor traffic load balancing also results in unfair delivery success rate among individuals, where messages from popular individuals can reach the destinations with a high probability, but individuals with few social connections will experience in low delivery success [11]. This variance of the delivery rate becomes a deterrent for nodes to participate in the message forwarding. Ultimately, the unfairness of traffic load makes popular nodes easy target of attacks [12]. Unbalanced traffic distribution across network nodes leading to traffic congestion in social networks has been extensively studied in several areas [13; 14; 15]. In [13], (data) traffic congestion during crowd disaster was thoroughly discussed. In that crowd management scenario, mobile devices carried by individuals is used to detect and inform to the crowd managers about the crowd density. However, in crowded areas traffic can increase dramatically within a short period of time, and, in turn, traffic congestion starts to occur, making the crowd managers fail to handle the crowd. In [14], traffic in social networks was investigated in various applications, ranging from vehicular traffic in urban environments to data traffic in Internet of Things and human-machine networks. In these settings, local failures such as traffic congestion in some parts of networks might provoke a cascade of failures throughout systems. Machine learning approaches were therefore nominated to address such issues. In [15], pocket switched networks were proposed to transfer data between users' mobile devices. Such opportunistic networks exploit human mobility to enable a store-carry-forward mechanism to deliver messages from sources to destinations. In each contact, social-based routing algorithms [7; 8; 9] typically select popular nodes (individuals) as the best relays in the network, resulting in unbalanced traffic distribution across nodes and traffic congestion in the most central nodes. Social-based routing algorithms are a class of utility-based routing algorithms. In such schemes, heuristic methods are used to determine the \"quality\" (utility) of a node as a relay. Each node \(i\) retains \(U_{i}(j)\), a utility function that denotes the likelihood of \(i\) delivering a message to \(j\). The utility function can be based on some different parameters, such as contact history, mobility model, social relations, etc. [PERSON] et al. [16] categorised utility functions into two types: destination-dependent (DD) and destination-independent (DI).