Recently, crowd sensing [7, 19, 20] has attracted a lot of attentions. Bulut et al.  introduce a friendship-based routing scheme, where a novel metric is used to accurately detect the quality of friendship and make the forwarding decisions. Lee et al.  present the task distribution models and improve the efficiency of data sensing. Sheng et al.  design a probabilistic mode and discuss the scheduling methods, when the GPS can not provide accurate location information for sensing coverage. Guo et al.  use the passive interference power received on mobile devices to sense the volume of wireless throughput of users, which is non-intrusive way without requiring data from private devices. An et al.  consider the credible interaction issues between users and introduce a crowdsourcing assignment method based on the users’ social relationship cognition to make sure the crowdsourcing tasks to be assigned to credible users. Guo et al.  present a group-aware mobile crowd sensing system which can facilitate group formation and management by using users’ online and offline social behaviors. However, these methods have not discussed how to encourage mobile individuals to contribute data during information dissemination.
There have been many studies on evaluating the performance of the information dissemination in mobile networks. Talasila et al.  provide a scheme to improve the location reliability of mobile crowd sensed data, where the trust is bootstrapped in the system with image processing techniques validating a fewer photos submitted by users. Wen et al.  present a quality-driven based incentive scheme for the crowd sensing system, where the participant is rewarded based on the quality of sensed data instead of working data. Sun et al.  propose a model for unicast epidemic message forwarding in the MSN, which considers the message validity. Wu et al.  present a theoretical model to evaluate the impact of people’s behaviors on information propagation. Wang et al.  study the biggest speed and distance that the data packed can have when it is disseminated in a mobile opportunistic network. Both the one-copy case and the multiple-copy case have been studied in the small scale and lager scale mobile network. Fan et al.  propose a data broadcasting method in the MSN which uses the users’ movement and community information to decide the broadcast routes. However, all these models have not considered the selfishness to explore the social nature of the networks in the information dissemination.
As for the node selfishness, Mei et al.  introduce two forwarding protocols for mobile wireless network based on the features of selfish individuals. Li et al.  present a routing performance by using a social selfishness-aware routing algorithm in delay tolerant networks. Li et al.  develop a model to investigate the impact of selfish behaviors on the performance of multicast in delay tolerant networks through a 3-D continuous time Markov chain. Hernndez-Orallo et al.  introduce a collaborative watchdog mechanism with fast diffusion of selfish nodes awareness to detect selfish nodes and present a model to evaluate the cost of the detection. Choi et al.  study the impact of users’ selfish for the data replica allocation in MSNs and develop a selfish node detection algorithm. Li et al.  study the node’s selfishness in the mobile network and divide the selfishness into three classes which are node-selfishness, intrinsic selfishness, and extrinsic selfishness. Although the selfishness has been studied extensively, they are not specially designed for mobile social networks or crowd sensing.