SocialCar: A Task Allocation Framework for Social Media Driven Vehicular Network Sensing Systems

Md. Tahmid Rashid, Daniel Yue Zhang, Dong Wang

Abstract: Vehicular sensor networks (VSNs) have become a reliable sensing instrument utilizing sensors built into cars. However, VSNs have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is protruding as a new sensing paradigm where measurements about the physical world are collected from humans. In this paper, we develop SocialCar, a task allocation framework for social media driven vehicular network sensing systems that exploits the collective powers of social sensing and VSNs for a robust sensing application. However, integrating VSNs with social sensing introduces a new set of challenges. The first challenge is leveraging the noisy and unreliable social signals to route vehicles to the regions of interest. The second challenge is the inconsistent availability attributed to car drivers being rational actors causing churn in the system. To address the above challenges, this paper introduces a bottom-up game-theoretic task preference model along with a top-down dynamic incentive control mechanism to allow car drivers to be selectively dispatched to event locations and verify the truthfulness of reported events. The results on a real-world disaster recovery case study show that SocialCar significantly outperforms current VSNs based solutions in detection effectiveness and deadline hit rate.