### Routing Unfairness in OppNets  
Mtibaa and Harras [19] define fairness as the relative equality in the distribution of resource usage among neighbouring nodes in the network. A routing protocol is said to be absolutely fair if it subjects every node in the network to equal amount of burden, in terms of resource expenditure. The authors also ascertain that absolute fairness degrades the performance of OppNets. This can be explained by the \"small world\" nature of the network [20], in which relatively few devices (referred to as \"popular nodes\") experience significantly more encounters than others [21; 22], thereby causing large variances in routing abilities among nodes. Popular nodes often belong to very prominent individuals (e.g., politicians) or users that travel around more frequently than others (e.g., deliverymen). Despite the trade-off between fairness and network performance, fair routing is important in order to ensure user collaboration. While users may allocate different amounts of storage space to the technology, their devices are roughly homogeneous [23] in terms of other core resources, such as energy and processing power. Hence, popular nodes are likely to expend their resources at much higher rates, which may leave their users dissatisfied. The risk is that such users may respond by withdrawing participation, and as [PERSON] and [PERSON]'s [19] \"popular nodes removal\" experiment have shown, this will cause drastic degradation of network performance. OppNet routing protocols are based on techniques that select relay nodes according to: similarity in movement patterns (e.g., speed) or contextual information (e.g., user occupation); social characteristics (e.g., node centrality); encounter-based properties (e.g., frequency of encounters); location-based information (e.g., location visiting frequency); or a combination of these. Unfortunately: prominent individuals often have higher contextual information similarity with others; only relatively few individuals possess high social connectedness; prominent individuals encounter a significantly higher number of people; and the number of individuals that visit many different locations is limited. Hence, these utilities are biased towards popular nodes and makes OppNet routing protocols too unfair for sensed data collection. Ensuring fairness in OppNets for sensed data collection and guaranteeing acceptable throughput is a challenging task. To be suitable for sensed data collection, fair routing solutions need to cope with relevant characteristics of urban environments, such as high degrees of spatial locality [16; 24]. Since encounter opportunities between nodes from different geographical regions are limited under higher degrees of spatial locality, forwarding utilities become more biased towards popular nodes. Under these conditions, fair routing solutions require forwarding utilities that can maximize the contribution of less popular nodes in order to relieve the burden on popular nodes. It is also important for nodes to be able to compare their burden when they encounter each other in order to make fair routing decisions. This requires a means of determining and measuring node burden from locally available information. Apart from being distributed, such burden measures also need to cope with the highly dynamic nature of the network. Unfortunately, the existing fair routing solutions are unaware of spatial locality, lack forwarding utilities that fully utilize less popular nodes and rely on scenario-specific burden measures. As a result, fairness and throughput are degraded, especially in scenarios for sensed data collection. Therefore, with respect to achieving fairness without compromising achievable throughput in sensed data collection scenarios, the aforementioned issues bring about the motivating questions of this paper, which are as follows:  
* How to design a forwarding utility that can increase the contribution of less popular nodes in the presence of spatial locality?