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员与应​​急管理部门之间及时、可靠的沟通。这种能力还应与快速收集目击者数据相结合，以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一种很有前途的工具，可以通过人群的大量参与来收集信息。然而，在紧急情况下，此类众包解决方案应该是稳健且有弹性的，因为网络可能会拥堵或底层蜂窝基础设施被损坏或暂时丢失。此外，应对收集的大量数据进行敏捷分析，并辅之以必要的设备，以便能够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颖的框架来解决这些挑战，该框架集成了各种技术和工具，用于对基于大规模众包的应急响应系统进行建模和操作。该项目通过集成基于云的快速处理收集的数据和通过设备到设备（D2D）通信和网络切片增强系统。该项目的综合研究和教育计划调查（i）通过移动应用程序和管理流程进行大规模关键数据收集，用于紧急事件的调查，（ii）对收集到的异构数据进行近实时处理用于提取关键信息（例如视频和照片中的人脸）的云计算环境，(iii) 采用基于 D2D 的通信作为补充组件，以提高系统在网络基础设施拥塞和故障时的恢复能力，以及 (iv)利用全球环境进行网络创新（GENI）网络切片作为紧急响应中时间敏感通信的专用带宽，并增强系统的广域恢复能力。该项目为国家优先事项的应急准备铺平了道路；并支持智能互联社区的进步。预期的增强功能将加快对紧急情况的响应，挽救人们的生命并降低公共安全运营成本。

项目成果

User satisfaction aware maximum utility task assignment in mobile crowdsensing

移动群智感知中的用户满意度感知最大效用任务分配

• DOI: 10.1016/j.comnet.2020.107156
• 发表时间: 2020-05-01
• 期刊: Comput. Networks
• 作者: F. Yucel;E. Bulut
• 通讯作者: E. Bulut

Is Crowdcharging Possible?

众筹可以吗？

• DOI: 10.1109/icccn.2018.8487418
• 发表时间: 2018-07-01
• 期刊: 2018 27th International Conference on Computer Communication and Networks (ICCCN)
• 作者: E. Bulut;Steven M. Hern;ez;ez;Aashish Dhungana;B. Szymański
• 通讯作者: B. Szymański

Time-dependent Stable Task Assignment in Participatory Mobile Crowdsensing

参与式移动群智感知中的时间相关稳定任务分配

• DOI: 10.1109/lcn48667.2020.9314829
• 发表时间: 2020-11-16
• 期刊: 2020 IEEE 45th Conference on Local Computer Networks (LCN)
• 作者: F. Yucel;E. Bulut
• 通讯作者: E. Bulut

Deep Self-Organizing Maps for Visual Data Mining

用于视觉数据挖掘的深度自组织映射

• DOI: 10.1109/hsi.2018.8430845
• 发表时间: 2018-07-01
• 期刊: 2018 11th International Conference on Human System Interaction (HSI)
• 作者: Chathurika S. Wickramasinghe;Kasun Amarasinghe;Daniel L. Marino;M. Manic
• 通讯作者: M. Manic

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: 2019-01
• 期刊: 2019 IEEE Radio and Wireless Symposium (RWS
• 作者: Chowdhury, Md Moin;Bulut, Eyuphan;Guvenc, Ismail
• 通讯作者: Guvenc, Ismail