  Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of
the rich embedded sensors of mobile user devices. However, the traditional
server-client MCS architecture often suffers from the high operational cost on
the centralized server (e.g., for storing and processing massive data), hence
the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce
the server's cost by leveraging the user devices' computation and storage
resources. In this work, we propose a novel P2P-based MCS architecture, where
the sensing data is saved and processed in user devices locally and shared
among users in a P2P manner. To provide necessary incentives for users in such
a system, we propose a quality-aware data sharing market, where the users who
sense data can sell data to others who request data but not want to sense the
data by themselves. We analyze the user behavior dynamics from the
game-theoretic perspective, and characterize the existence and uniqueness of
the game equilibrium. We further propose best response iterative algorithms to
reach the equilibrium with provable convergence. Our simulations show that the
P2P data sharing can greatly improve the social welfare, especially in the
model with a high transmission cost and a low trading price.

Computer Science and Game Theory