Generating pedestrian maps of disaster areas through ad-hoc deployment of computing resources across a DTN
Generating pedestrian maps of disaster areas through ad-hoc deployment of computing resources across a DTN
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DOI：
10.1016/j.comcom.2016.12.003
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
E. M. Trono;Manato Fujimoto;H. Suwa;Yutaka Arakawa;K. Yasumoto
中科院分区：
文献类型：
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作者：
E. M. Trono;Manato Fujimoto;H. Suwa;Yutaka Arakawa;K. Yasumoto
制作灾区步行地图是应对行动的重要组成部分。地图帮助应急人员做出决策，并显示疏散人员前往避难所的路线。然而，灾难可能会破坏通信基础设施，使基于云的地图服务无法访问。响应者求助于纸质地图，这很难分享，也不能推荐路线。在这项研究中，我们提出了一个数字行人地图生成系统的灾害。为了实现该系统，我们解决了这些挑战：（1）如何在没有基于云的计算资源的情况下收集所需的数据并生成地图，（2）如何在没有连续的端到端网络的情况下在系统内共享消息，以及（3）如何平衡地图推理任务的负载。对于（1），GPS轨迹由探索该区域的响应者收集。然后，收集的数据被发送到计算节点：部署在灾区的商品工作站，进行处理。对于（2），系统建立了一个延迟容忍网络，该网络使用流行病路由在较短范围内进行通信，并使用响应车辆作为数据渡轮在较长范围内进行通信。对于（3），我们提出了一种负载平衡启发式算法，它使用渡轮航线时间表和有关计算节点负载的统计信息来确定如何卸载地图推理任务。我们通过实验和模拟来评估我们的系统，并表明在需要处理大量数据的极端情况下，它可以将生成和提供地图片段所需的时间减少大约两个小时。
Generating pedestrian maps of disaster areas is an important part of response operations. Maps aid responders in decision-making and show routes that lead evacuees to refuges. However, disasters can damage communication infrastructures, rendering Cloud-based mapping services inaccessible. Responders resort to paper maps, which are difficult to share and cannot recommend routes. In this study, we present a digital pedestrian map generation system for disasters. To realize the system, we addressed these challenges: (1) how to collect the required data and generate the map without Cloud-based computing resources, (2) how to share messages within the system without continuous, end-to-end networks, and (3) how to balance the load of map inference tasks. For (1), GPS traces are collected by responders exploring the area. Then, collected data are sent to Computing Nodes: commodity workstations that are deployed in the disaster area, for processing. For (2), the system establishes a Delay-Tolerant Network that uses Epidemic Routing to communicate across shorter-ranges and uses response vehicles as data ferries to communicate across longer-ranges. For (3), we propose a load balancing heuristic, which uses ferry route timetables and statistical information about the load of Computing Nodes to determine how to offload map inference tasks. We evaluate our system through experiments and simulations and show that it decreases the time needed to generate and deliver pieces of the map by approximately two hours in an extreme case with large quantities of data have to be processed.