BDD: Disaster Preparation and Response via Big Data Analysis and Robust Networking

BDD：通过大数据分析和强大的网络进行灾难准备和响应

• 批准号: 1461886
• 负责人: Guoliang Xue
• 金额: $ 30万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461886&HistoricalAwards=false
• 关键词: BDD; Disaster; Preparation; Response; via

Disasters are events with dire consequences, requiring multiple-agency responses and resources beyond the capability of a single community. Natural disasters, such as the 2011 Great East Japan Earthquake, can threaten the lives of many people and cause inordinate economic losses. Communication is critical to disaster preparation, response, and recovery, but may be damaged during the disaster. In this project, researchers from the US and Japan study novel approaches to disaster preparation, response and recovery using survivable communication networks and big data analysis of social media data. This collaborative effort involves expertise in disaster research, social media mining and big data analysis, network science, wireless communications, and machine learning, to examine resilient network architecture and algorithms, data collection and analysis before the disaster, and decision making and information dissemination during the disaster. The resilient network incorporates both wired and wireless communications to deal with multiple disaster-induced failures, aiming for efficient algorithms serving emergency applications. State-of-the-art data collection and analysis techniques will help build an important knowledge base in proactive preparation for disasters. Real time decision making and information dissemination during a disaster can assist disaster response and recovery effectively. The proposed research aims to provide valuable guidance for disaster preparation, response, and recovery for both the US and Japan, and spearhead a new research direction in survivable communication network design and big data analysis. This project provides a conducive environment to further research collaboration of big data analysis and disaster relief between the US and Japan. Graduate students will be jointly trained in this international research project to actively collaborate in carrying out the proposed research tasks. Special efforts will be made to engage minority students and underrepresented groups.

灾难是具有可怕后果的事件，需要多个机构做出反应，并且提供超出单个社区能力的资源。 2011 年东日本大地震等自然灾害可能威胁许多人的生命并造成过度的经济损失。通信对于灾难准备、响应和恢复至关重要，但在灾难期间可能会受到损害。在该项目中，来自美国和日本的研究人员利用可生存的通信网络和社交媒体数据的大数据分析来研究灾难准备、响应和恢复的新方法。这项合作涉及灾难研究、社交媒体挖掘和大数据分析、网络科学、无线通信和机器学习方面的专业知识，以检查弹性网络架构和算法、灾难前的数据收集和分析以及灾难期间的决策和信息传播。该弹性网络结合了有线和无线通信来处理多种灾难引起的故障，旨在提供服务于紧急应用的高效算法。最先进的数据收集和分析技术将有助于建立主动防灾准备的重要知识库。灾难发生时的实时决策和信息发布可以有效协助灾难响应和恢复。该研究旨在为美国和日本的灾难准备、响应和恢复提供有价值的指导，并引领可生存通信网络设计和大数据分析的新研究方向。该项目为美日之间进一步开展大数据分析和救灾研究合作提供了有利的环境。研究生将在该国际研究项目中接受联合培训，积极合作开展拟议的研究任务。我们将特别努力吸引少数族裔学生和代表性不足的群体。

项目成果

Load Balancing for Interdependent IoT Microservices

• DOI: 10.1109/infocom.2019.8737450
• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
• 作者: Ruozhou Yu;Vishnu Teja Kilari;G. Xue;Dejun Yang

Capacity-aware cost-efficient network reconstruction for post-disaster scenario

• DOI: 10.1109/pimrc.2016.7794910
• 发表时间: 2016-09
• 期刊: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
• 作者: Xiaoyan Wang;Hao Zhou;L. Zhong;Yusheng Ji;K. Takano;S. Yamada;G. Xue

Population-Aware Relay Placement for Wireless Multi-Hop Based Network Disaster Recovery

• DOI: 10.1109/glocom.2017.8253922
• 发表时间: 2017-07
• 期刊: GLOBECOM 2017 - 2017 IEEE Global Communications Conference
• 作者: L. Zhong;Yusheng Ji;Xiaoyan Wang;S. Yamada;K. Takano;G. Xue

The Critical Network Flow Problem: Migratability and Survivability

• DOI: 10.1109/tnet.2017.2747588
• 发表时间: 2017-09
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Ruozhou Yu;G. Xue;Xiang Zhang

The Fog of Things Paradigm: Road toward On-Demand Internet of Things

• DOI: 10.1109/mcom.2018.1701140
• 发表时间: 2018-09
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Ruozhou Yu;G. Xue;Vishnu Teja Kilari;Xiang Zhang