Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing - 2018


Mobile crowdsensing (MCS) may be a new paradigm of sensing by making the most of the rich embedded sensors of mobile user devices. But, the ancient server-shopper MCS design often suffers from the high operational price on the centralized server (e.g., for storing and processing large data), hence the poor scalability. Peer-to-peer (P2P) information sharing will effectively reduce the server's value by leveraging the user devices' computation and storage resources. During this work, we propose a novel P2P-based mostly MCS design, where the sensing knowledge is saved and processed in user devices regionally and shared among users in a very P2P manner. To supply necessary incentives for users in such a system, we tend to propose a high quality-aware data sharing market, where the users who sense information can sell information to others who request information but not wish to sense the info by themselves. We tend to analyze the user behavior dynamics from the sport-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We tend to any propose best response iterative algorithms to achieve the equilibrium with provable convergence. Our simulations show that the P2P information sharing can greatly improve the social welfare, particularly within the model with a high transmission price and an occasional trading value.

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