US Ignite: Collaborative Research: Focus Area 1: Rapid and Resilient Critical Data Sourcing for Public Safety and Emergency Response

US Ignite：合作研究：重点领域 1：公共安全和应急响应的快速且有弹性的关键数据采购

• 批准号: 1647217
• 负责人: Eyuphan Bulut
• 金额: $ 38万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647217&HistoricalAwards=false
• 关键词: US; Ignite; Collaborative; Research; Focus

In public safety and emergency response, the key for a successful recovery is on-time and reliable communication between the people reporting the incident and the emergency management authorities. Such capability should further be coupled with rapid collection of eyewitness data for successful investigation of criminals or root causes of the incident. Recently, crowd sourcing applications have proven to be promising tools to gather information with tremendous participation from the crowd. In emergency scenarios, however, such crowd sourcing solutions should be robust and resilient as the network could be congested or the underlying cellular infrastructure is damaged or temporarily lost. Moreover, an agile analysis of the massive amount of data collected should be supported with necessary equipment so that time-sensitive feedback could be provided and shared between different agencies. This project addresses these challenges by introducing a novel framework that integrates various technologies and tools for modeling and operation of large-scale crowd sourcing-based emergency response systems.The project enhances the current systems by integrating a cloud-based rapid processing of collected data and augmenting the system by device-to-device (D2D) communications and network slicing. The project's integrated research and education plan investigates (i) large-scale critical data collection via a mobile app and management process to be used in the investigation of an emergency incident, (ii) near-real-time processing of the gathered heterogeneous data in a cloud computing environment for critical information extraction such as faces of people in the videos and photos, (iii) adoption of D2D-based communication as a complementary component to improve system resilience in case of congestion's and failures in network infrastructure, and (iv) utilization of Global Environment for Network Innovations (GENI) network slices as dedicated bandwidth for time-sensitive communication in emergency response as well as to enhance wide area resilience of the system. The project paves the way towards emergency preparedness which is a national priority; and supports progress toward smart and connected communities. The anticipated enhancements expedite the response to emergency cases, save people's lives and reduce public safety operation costs.

在公共安全和应急响应中，成功恢复的关键是报告事件的人与应急管理当局之间及时可靠的沟通。这种能力还应与迅速收集目击者资料相结合，以便成功调查罪犯或事件的根本原因。最近，众包应用程序已被证明是有前途的工具，收集信息的巨大参与的人群。然而，在紧急情况下，这样的众包解决方案应该是健壮和有弹性的，因为网络可能拥塞或者底层蜂窝基础设施被损坏或暂时丢失。此外，对收集到的大量数据进行灵活分析，应配备必要的设备，以便提供及时的反馈，并在不同机构之间共享。该项目通过引入一种新的框架来解决这些挑战，该框架集成了各种技术和工具，用于大规模基于人群资源的应急响应系统的建模和操作。该项目通过集成基于云的快速处理收集的数据并通过设备到设备（D2 D）通信和网络切片来增强系统，从而增强了当前系统。该项目的综合研究和教育计划调查（i）通过移动的应用程序和管理流程收集大规模关键数据，用于调查紧急事件，（ii）在云计算环境中对收集的异构数据进行近实时处理，以提取关键信息，如视频和照片中的人脸，（iii）采用基于D2 D的通信作为补充组件，以在网络基础设施拥塞和故障的情况下提高系统弹性，以及（iv）利用全球网络创新环境（GENI）网络切片作为用于时间的专用带宽，在紧急反应中保持灵敏的通信，以及加强系统的广域复原能力。该项目为应急准备铺平了道路，这是一个国家优先事项;并支持向智能和互联社区发展。预期的改进将加快对紧急情况的反应，拯救人们的生命，并降低公共安全运营成本。

项目成果

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

• DOI: 10.1109/access.2021.3064819
• 发表时间: 2021-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Wickramasinghe, Chathurika S.;Marino, Daniel L.;Manic, Milos
• 通讯作者: Manic, Milos

User satisfaction aware maximum utility task assignment in mobile crowdsensing

• DOI: 10.1016/j.comnet.2020.107156
• 发表时间: 2020-05
• 期刊: Comput. Networks
• 作者: F. Yucel;E. Bulut

Loss-Aware Efficient Energy Balancing in Mobile Opportunistic Networks

• DOI: 10.1109/globecom38437.2019.9014073
• 发表时间: 2019-12
• 期刊: 2019 IEEE Global Communications Conference (GLOBECOM)
• 作者: Aashish Dhungana;E. Bulut

Trajectory Optimization in UAV-Assisted Cellular Networks under Mission Duration Constraint

任务持续时间约束下无人机辅助蜂窝网络的轨迹优化

• DOI: 10.1109/rws.2019.8714567
• 发表时间: 2019
• 期刊: 2019 IEEE Radio and Wireless Symposium (RWS
• 作者: Chowdhury, Md Moin;Bulut, Eyuphan;Guvenc, Ismail
• 通讯作者: Guvenc, Ismail

Charging Skip Optimization with Peer-to-Peer Wireless Energy Sharing in Mobile Networks

移动网络中点对点无线能量共享的充电跳过优化

• DOI: 10.1109/icc.2018.8422711
• 发表时间: 2018
• 期刊: 2018 IEEE International Conference on Communications (ICC
• 作者: Dhungana, Aashish;Arodz, Tomasz;Bulut, Eyuphan
• 通讯作者: Bulut, Eyuphan