论文标题
DASC：朝着灾难响应应用程序的道路损害造成的社交媒体驱动的汽车传感框架
DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications
论文作者
论文摘要
尽管车辆传感器网络（VSN）赢得了使用在汽车中内置的传感器的移动传感范式的地位，但由于汽车驾驶员只能在机会上发现新事件，因此它们的传感范围有限。相反，社会感知正在成为一种新的传感范式，其中从人类那里收集了有关物理世界的测量。与VSN相反，社会感知更加普遍，但其主要局限性之一在于，其不可靠的人类传感器贡献的数据不一致。在本文中，我们提出了DASC，这是一种道路损害的社交媒体驱动的汽车传感框架，该框架利用了社交感应和VSN的集体力量来获得可靠的灾难响应应用程序。但是，将VSN与社会感知整合到了一系列新的挑战：i）如何利用嘈杂和不可靠的社会信号将车辆路由到准确的感兴趣区域？ ii）如何解决由汽车驾驶员是理性演员引起的不一致的可用性（例如，流失）？ iii）如何有效地指导汽车到事件地点，而对灾难造成的道路损害的知识很少，同时还可以处理物理世界和社交媒体的动态？ DASC框架通过建立一种新型混合社交车辆传感系统来应对上述挑战，该系统采用了游戏理论，反馈控制和马尔可夫决策过程（MDP）的技术。特别是，DASC蒸馏器信号从社交媒体发出，并发现道路损失，以有效地将汽车驱动到目标区域以验证紧急事件。我们在著名的车辆模拟器中实施和评估DASC，该模拟器可以模拟现实世界中的灾难响应方案。现实世界应用的结果表明，在检测准确性和效率方面，DASC优于当前基于VSN的解决方案。
While vehicular sensor networks (VSNs) have earned the stature of a mobile sensing paradigm utilizing sensors built into cars, they have limited sensing scopes since car drivers only opportunistically discover new events. Conversely, social sensing is emerging as a new sensing paradigm where measurements about the physical world are collected from humans. In contrast to VSNs, social sensing is more pervasive, but one of its key limitations lies in its inconsistent reliability stemming from the data contributed by unreliable human sensors. In this paper, we present DASC, a road Damage-Aware Social-media-driven Car sensing framework that exploits the collective power of social sensing and VSNs for reliable disaster response applications. However, integrating VSNs with social sensing introduces a new set of challenges: i) How to leverage noisy and unreliable social signals to route the vehicles to accurate regions of interest? ii) How to tackle the inconsistent availability (e.g., churns) caused by car drivers being rational actors? iii) How to efficiently guide the cars to the event locations with little prior knowledge of the road damage caused by the disaster, while also handling the dynamics of the physical world and social media? The DASC framework addresses the above challenges by establishing a novel hybrid social-car sensing system that employs techniques from game theory, feedback control, and Markov Decision Process (MDP). In particular, DASC distills signals emitted from social media and discovers the road damages to effectively drive cars to target areas for verifying emergency events. We implement and evaluate DASC in a reputed vehicle simulator that can emulate real-world disaster response scenarios. The results of a real-world application demonstrate the superiority of DASC over current VSNs-based solutions in detection accuracy and efficiency.
