Crowd sensing pertains to the monitoring of large-scale phenomena that cannot be easily measured by a single individual. For example, intelligent transportation systems may require traffic congestion monitoring and air pollution level monitoring. These phenomena can be measured accurately only when many individuals provide speed and air quality information from their daily commutes, which are then aggregated spatio-temporally to determine congestion and pollution levels in smart cities. In this paper we study three classes of a network game where each user decides its level of participation to the crowdsensing: (i) public good, (ii) information sharing, (iii) resource sharing. We examine the contribution level of users via Bayesian game models, where we have analyzed the equilibrium strategies, equilibrium payoff and the role of user-centric information on their behavior in terms of participation to the cloud. We also analyzed the possibility for users with power-hungry devices to serve the cloud by means of throughput sharing strategies from other users.