## 2 Related Works  
Several forwarding algorithms are available in the OppNets literature. [PERSON] et al. [16] classify forwarding algorithms according to their forwarding paradigm into three categories: (i) message-ferry-based, (ii) opportunity-based, and (iii) prediction-based. Recently, protocols based on social relationships have emerged as a fourth category. The routing protocol proposed in this work belongs to this novel category. Message-ferry-based routing algorithms carry out data distribution in a network by combining the store-and-forward mechanism with the use of additional nodes, called ferries, which act as data mules [16]. Many routing algorithms have embraced this forwarding paradigm [17; 18; 19; 20], including [PERSON] et al. [21] who proposes a routing algorithm based on the observed meetings between peers, and the visits of peers to specific geographic locations. Message-ferry-based routing algorithms share a common problem: the overhead and extra cost to control the ferries. In opportunistic-based algorithms, the nodes exchange data with each other when a contact occurs, whenever they are within communications range. The Epidemic routing algorithm, proposed in [22], is an example of an algorithm based on this forwarding paradigm. This routing algorithm assumes that, when a pair of nodes is in the same coverage area, they send all the messages stored in their buffers to each other, therefore increasing the message delivery probability. Based on this opportunistic-based paradigm, some researchers have explored the network behaviour to propose forwarding algorithms. For instance, [PERSON] and [PERSON] [23] have used some simplifying assumptions about the network behaviour to propose two forwarding algorithms. The main goal is to reduce the expected latencies from any node in the network to a particular destination in some situations. The authors take into account that forwarding algorithms for opportunistic networks should result in low-average latency, and in an efficient usage of network resources. Based on simulation results, the authors confirm that their proposed algorithms are able to improve both the latency and the delivery rate. Inspired by the Nash bargaining solution, [PERSON] et al. proposes in [24] the GameR forwarding algorithm for OppNets. This algorithm uses a utility function derived from the estimated resource utilization ratio and the history delivery predictability. According to the results obtained, GameRuses the network resources efficiently, improving the delivery ratio and decreasing the overhead on nodes under resource-constrained situations. Other proposals [25; 26; 27; 28] also based on this paradigm are included. Prediction-based algorithms are a refinement of the opportunistic-based approach. These algorithms select the next-hop node based on some metrics. These metrics make use of characteristics such as the probability of message delivery to the destination node [14; 29; 30] or the encounters' history of the receiving node [27; 31; 32]. Finally, several researchers have considered the use of some social characteristics in the design of new DTN routing protocols. In fact, most DTN applications include a huge number of mobile devices carried by humans, and, according to [PERSON] et al. [16], the behaviour representing the utilization of devices is better described by social network models. Among the social characteristics worth considering, we may include altruism [33], friendship [13; 34; 35], selfishness [35; 36], centrality, and popularity [12]. [PERSON] et al. proposed in [12] the Bubble Rap Forwarding protocol. This protocol takes the routing decisions by using the concepts of community and centrality. Essentially, it selects high centrality nodes and community members related to the message destination. In [13], [PERSON] et al. proposed the first routing algorithm based on friendship. To model the friendship among nodes, the authors considered the frequency of contacts, their longevity, and their regularity.