Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing

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.

移动众包感知（MCS）是一种利用移动用户设备中丰富的嵌入式传感器进行感知的新模式。然而，传统的服务器 - 客户端MCS架构往往因集中式服务器的高昂运营成本（例如，用于存储和处理海量数据）而面临可扩展性差的问题。对等（P2P）数据共享可以通过利用用户设备的计算和存储资源有效地降低服务器成本。在这项工作中，我们提出了一种新颖的基于P2P的MCS架构，其中感知数据在本地用户设备中保存和处理，并以P2P方式在用户之间共享。为了在这样的系统中为用户提供必要的激励，我们提出了一个质量感知的数据共享市场，在这个市场中，感知数据的用户可以将数据出售给那些请求数据但自己不想感知数据的用户。我们从博弈论的角度分析用户行为动态，并刻画博弈均衡的存在性和唯一性。我们进一步提出最佳响应迭代算法，以实现具有可证明收敛性的均衡。我们的模拟结果表明，P2P数据共享可以极大地提高社会福利，特别是在传输成本高且交易价格低的模型中。