NeTS: JUNO2: Collaborative Research: STEAM: Secure and Trustworthy Framework for Integrated Energy and Mobility in Smart Connected Communities

NetS：JUNO2：协作研究：STEAM：智能互联社区中集成能源和移动性的安全可信框架

• 批准号: 1818901
• 负责人: Abhishek Dubey
• 金额: $ 21万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818901&HistoricalAwards=false
• 关键词: NeTS; JUNO2; Collaborative; Research; STEAM

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data. This in turn will make trustworthiness in SCC applications very challenging. To address this, a synergistic team of researchers from the US and Japan, under the JUNO2 program, will collaborate on this project, called STEAM (Secure and Trustworthy framework for integrated Energy and Mobility) to develop a framework to ensure data privacy, data integrity, and trustworthiness in smart and connected communities. The collaboration provides the project with a significant amount of automotive (transportation) data from Japan, and also access to a testbed in Japan. Although the target applications are smart mobility and smart energy (the choice is deliberate to exploit the complementary strengths of Japan and US in these two domains), the proposed techniques and solutions have wide applicability to other domains, such as smart healthcare. The novelty of the STEAM project lies in its integrated approach to handling security and trustworthiness in SCC applications. Specifically, the research team will develop innovative privacy-preserving algorithms and models for anomaly detection, trust and reputation scoring used by application providers for data integrity and information assurance. Towards that goal, they will study trade-offs between security, privacy, trust levels, resources, and performance using two exemplar applications in smart mobility and smart energy exchange in communities. Finally, they will design a modular, secure and trustworthy middleware architecture that implements privacy-preserving algorithms, resource constraints, and trustworthiness of data sources or content and decision-making schemes. The project has access to smart meter data from Texas, California, and Ireland and a large volume of automobile data from Japan. The evaluation plan includes integration of the project's anomaly detection and trustworthy decision-making algorithms into a smart vehicle route planning application and a transactive energy system in a plug-in electric vehicle testbed in Japan.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据驱动分析、物联网（IoT）和网络物理系统（CPS）的快速发展推动了智能和互联社区（SCC）应用的不断发展，包括智能交通和智能能源。然而，在没有适当安全机制的情况下部署这些技术解决方案，会使它们容易受到数据完整性和隐私攻击，正如最近发生的大量事件所观察到的那样。如果处理不当，这种攻击不仅会削弱SCC的运营，还会影响客户愿意共享数据的程度。这反过来又会使SCC应用程序的可信度变得非常具有挑战性。为了解决这个问题，来自美国和日本的一个协同研究小组将根据JUNO2计划，在这个名为STEAM（综合能源和移动安全与可信框架）的项目上进行合作，以开发一个框架，以确保智能和互联社区的数据隐私、数据完整性和可信度。此次合作为该项目提供了大量来自日本的汽车（运输）数据，并进入了日本的一个测试平台。尽管目标应用程序是智能移动和智能能源（该选择是为了充分利用日本和美国在这两个领域的互补优势），但所提出的技术和解决方案也广泛适用于其他领域，例如智能医疗保健。STEAM项目的新颖之处在于其在SCC应用程序中处理安全性和可信度的集成方法。具体来说，研究团队将开发创新的隐私保护算法和模型，用于异常检测、信任和声誉评分，供应用程序提供商用于数据完整性和信息保障。为了实现这一目标，他们将研究安全、隐私、信任水平、资源和性能之间的权衡，使用智能移动和社区智能能源交换的两个范例应用。最后，他们将设计一个模块化、安全和可信的中间件体系结构，实现隐私保护算法、资源约束以及数据源或内容和决策方案的可信度。该项目可以访问来自德克萨斯州、加利福尼亚州和爱尔兰的智能电表数据以及来自日本的大量汽车数据。评估方案包括将项目的异常检测和可信决策算法集成到日本插电式电动汽车试验台的智能车辆路线规划应用和交互能源系统中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Toward urban vehicle mobility modeling in Japan

日本城市车辆移动建模

• DOI: 10.1145/3313237.3313298
• 发表时间: 2019
• 期刊: Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering
• 作者: Yamaguchi, Hirozumi

Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems

学习应急响应系统大地理区域的事件预测模型

• 发表时间: 2021
• 期刊: IEEE Conference on Smart Computing. SmartComp 2021.
• 作者: Vazirizade, Sayyed;Mukhopadhyay, Ayan;Pettet, Geoffrey;El Said, Said;Baroud, Hiba;Dubey, Abhishek
• 通讯作者: Dubey, Abhishek

Automatic Deadline-Oriented Sampling Method for Coarse-grained Stream Processing

粗粒度流处理的面向截止时间的自动采样方法

• 发表时间: 2019
• 作者: Sunyanan Choochotkaew;Hirozumi Yamaguchi;Teruo Higashino
• 通讯作者: Teruo Higashino

Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge

• DOI: 10.1109/percomw.2019.8730577
• 发表时间: 2019-03
• 期刊: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
• 作者: Geoffrey Pettet;Saroj Sahoo;A. Dubey

On Decentralized Route Planning Using the Road Side Units as Computing Resources

• DOI: 10.1109/icfc49376.2020.00009
• 发表时间: 2020-04
• 期刊: 2020 IEEE International Conference on Fog Computing (ICFC)
• 作者: J. P. Talusan;Michael Wilbur;A. Dubey;K. Yasumoto