## 2 Related Work  
The key human behavior properties in mobile social networks were thoroughly identified in [12]. Social ties measure relationship strength through interaction frequency, contact time, social homogeneity (preference similarity), or social neighborhood for nodes with significant ties, while community structure represents clustered nodes linked by relationships and interests measured by a clustering coefficient describing community interconnectedness, centrality categories node importance via the number of ties, information spread speed, and location/tie importance. Bubble Rap protocol uses a community structure of social networks and forwards via central nodes having high social interaction in their communities, showing better delivery performance than the classical Prophet protocol [13]. SimBet protocol is based on nodes' similarities, the strength of ties, and the betweenness of nodes, which is the node's ability to connect nodes [14]. Dlife, a more realistic social-aware routing algorithm, monitors real social interactions of users in their daily lives by using the dynamic nodes' behavior and strength of social ties measured by contact duration between nodes for the selection of the message-forwarding carrier [15]. SocialCast routing exploits social interaction metrics using users' interests, relations strength, and mobility patterns [16]. Authors in [17] use social relations analysis to identify which data to disseminate to whom and what will be more cooperative and reliable to transmit. In [18], a social preference-aware forwarding scheme based on social information and contact probability and a buffer replacement policy for the message preferences shows a good performance with acceptable delay. Ref. [19] presents a friendship-based routing that utilizes users'features and relationships by presenting a social pressure metric that measures friendship between nodes by their long and frequent contact during periods, ensuring a better message-delivery rate. On the other hand, to prevent nodes' selfishness when nodes behave selfishly, i.e., to exploit the network with little or without participation in the delivery process, trustworthiness became a critical factor of each node before sharing information and establishing new relationships. In that context, ref. [20] introduced trustworthiness between nodes based on node and path trust levels to improve reliability and performance. Trust measurement uses intimacy and cooperation social metrics, along with a sliding window, to track the recent behavior of nodes and eliminate long ones in the decision of routing and message forwarding to ensure a reliable route for data dissemination. They achieved high packet delivery and transmission delay but lost the advantage when the network topology changed and became bigger. Ref. [21] improved routing adaptability and handled network selfishness by using trust degree and threshold concepts, where nodes with greater social relationships will be selected as the next hop candidate to transfer data. The trust thresholds adjust the delivery ability and deal with various network selfishness. Ref. [22] improved packet delivery based on thresholds of a composite trust, including QoS trust (connectivity and energy metrics) and social trust (cooperation degree), for next-hop message selection. This composite trust approach achieves high delivery with acceptable delay and overhead. Ref. [23] measured contact time, frequency, and regularity, so better frequent and longer contact will be more trustworthy in the delivery process. The solution achieves a better delivery ratio and delay than the classical Bubble Rap protocol. The authors in [24] presented a trusted routing based on social similarity to deal with selfish and malicious nodes using a threshold-based approach. At the same time, ref. [25] handled misbehaving nodes using social relations to establish a trusted node list used later in routing, along with identity trust enforced by a distributed key management scheme. Ref.