Vehicle-Assist Resilient Information and Network System for Disaster Management

Vehicle-Assist Resilient Information and Network System for Disaster Management
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用于灾害管理的车辆辅助弹性信息和网络系统

DOI:
10.1109/tetc.2017.2693286
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发表时间:
2017-07
影响因子:
5.9
通讯作者:
Zhuang Weihua
Zhuang Weihua
中科院分区:
计算机科学2区
文献类型:
--
作者:
Li Peng;Miyazaki Toshiaki;Wang Kun;Guo Song;Zhuang Weihua

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在大的灾难之后,由于现有网络基础设施的严重损坏,受损区域可能失去联系。与此同时,为了收集受灾信息和发布救援指示,对灾区的网络连接将产生很高的要求。在本文中,我们设计了一个车辆辅助弹性信息和网络系统的灾害管理,尽管互联网不可用。它包含三个主要组件:(1)智能手机应用程序,提供SOS报告,生命和医疗资源请求/提供以及安全道路导航功能;(2)移动的站,帮助智能手机应用程序和服务器之间的数据交换;(3)地理分布的服务器,收集用户数据,进行分布式数据分析,并做出灾害管理决策。由于车辆辅助网络是连接孤立的智能手机和服务器的关键,我们继续研究移动的站的调度问题。鉴于一些灾害管理任务,如传感,信息收集和消息传播,我们提出了在线算法,调度移动的站的灾害管理任务的目标是最大限度地提高总重量完成的任务,没有任何知识,未来的任务到来。我们推导出我们提出的算法的竞争比,并进行广泛的性能评估模拟。
After big disasters, a damaged area can be out of contact because of severe damage of existing network infrastructures. Meanwhile, high demands for network connections to the disaster area will arise to collect damage information and disseminate rescue instructions. In this paper, we design a vehicle-assist resilient information and network system for disaster management, despite of the Internet unavailability. It contains three main components: (1) smartphone apps that provide functions of SOS reporting, life and medical resources request/provision, and safe road navigation; (2) mobile stations that assist data exchange between smartphone apps and servers; (3) geo-distributed servers that collect user data, conduct distributed data analysis, and make disaster management decisions. Since the vehicle-assist network is critical to connect isolated smartphones and servers, we continue to study the scheduling problem of mobile stations. Given a number of disaster management tasks, such as sensing, information collection, and message dissemination, we propose online algorithms that schedules mobile stations for disaster management tasks with the objective of maximizing the total weight of finished tasks, without any knowledge of future task arrivals. We derive the competitive ratio of our proposed algorithms and conduct extensive simulations for performance evaluation.
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