Abstract
Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.
| Original language | English |
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| Title of host publication | Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 148-154 |
| Number of pages | 7 |
| ISBN (Electronic) | 9798350320565 |
| DOIs | |
| State | Published - 2023 |
| Event | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 - Waikoloa, United States Duration: Jan 3 2023 → Jan 7 2023 |
Publication series
| Name | Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
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Conference
| Conference | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
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| Country/Territory | United States |
| City | Waikoloa |
| Period | 01/3/23 → 01/7/23 |
Funding
This research was supported in part by an appointment to the Oak Ridge National Laboratory sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education, the U.S. Department of Energy, and Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program( SULI), and the Texas A&M University-Kingsville, Office of University Programs, Science and Technology Directorate, Department of Homeland Security Grant Award # 2012-ST -062-000054. Notice: This manuscript has been authored by UTBattelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan). Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This research was supported in part by an appointment to the Oak Ridge National Laboratory sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education, the U.S. Department of Energy, and Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program(SULI), and the Texas A&M University-Kingsville, Office of University Programs, Science and Technology Directorate, Department of Homeland Security Grant Award # 2012-ST -062-000054.