DASC: Towards a road Damage-Aware Social-media-driven Car sensing framework for disaster response applications
DASC: Towards a road Damage-Aware Social-media-driven Car sensing framework for disaster response applications
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DOI：
10.1016/j.pmcj.2020.101207
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发表时间：
2020-09-01
影响因子：
4.3
通讯作者：
Wang, Dong
中科院分区：
文献类型：
--
作者：
Rashid, M. T.;Zhang, Daniel (Yue);Wang, Dong
虽然车辆传感器网络（VSN）已经赢得了利用内置于汽车中的传感器的移动的感测范例的地位，但是它们具有有限的感测范围，因为汽车驾驶员仅机会性地发现新事件。相反，社会感知正在成为一种新的感知范式，其中有关物理世界的测量是从人类那里收集的。与VSN相比，社会感知更普遍，但其关键限制之一在于其不一致的可靠性，这源于不可靠的人类传感器提供的数据。在本文中，我们提出了DASC，一个道路损坏感知社会媒体驱动的汽车传感框架，利用社会传感和VSN的集体力量，可靠的灾难响应应用。然而，将车辆传感器网络与社会感知相结合带来了一系列新的挑战：（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)如何解决不一致的可用性（例如，汽车司机是理性的行为者？(iii)如何在事先对灾难造成的道路损坏知之甚少的情况下，有效地引导汽车到达活动地点，同时还要处理物理世界和社交媒体的动态？DASC框架通过建立一种新颖的混合型社交车传感系统来解决上述挑战，该系统采用来自博弈论、反馈控制和马尔可夫决策过程（MDP）的技术。特别是，DASC提取从社交媒体发出的信号，发现道路损坏，以有效地将汽车驾驶到目标区域，以验证紧急事件。我们实现和评估DASC在一个著名的车辆模拟器，可以模拟真实的世界灾难响应的情况。实际应用的结果表明，DASC在检测精度和效率方面优于当前基于VSN的解决方案。(c)2020 Elsevier B.V.保留所有权利。
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. (c) 2020 Elsevier B.V. All rights reserved.