Abstract-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.

This paper solves the problem of long-term revenue maximization of a spectrum database operator, through joint pricing of spectrum resources and admission control of secondary users. A unique feature that we consider is the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and consider the optimal solutions under both static and dynamic prices. In the case of static pricing, we constrain the prices to be time-independent while allowing the admission control policies to be time dependent. We show that in most cases a stationary (time independent) admission policy is in fact optimal in this case. We further look at the general case of dynamic pricing, where both the prices and admission control policies can be time dependent. We show that the flexibility of dynamic pricing can significantly improve the operator's revenue (by more than 30%) when secondary users have high demand elasticities.

No abstract

No abstract

No abstract