Computer Science > Networking and Internet Architecture
arXiv:2411.19714 (cs)
[Submitted on 29 Nov 2024 (v1), last revised 13 Jan 2025 (this version, v2)]
Title:The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications
Authors:Navid Salami Pargoo,Mahshid Ghasemi,Shuren Xia,Mehmet Kerem Turkcan,Taqiya Ehsan,Chengbo Zang,Yuan Sun,Javad Ghaderi,Gil Zussman,Zoran Kostic,Jorge Ortiz
View a PDF of the paper titled The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications, by Navid Salami Pargoo and 10 other authors
View PDFHTML (experimental)Abstract:As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications-focusing on challenges like pedestrian safety and adaptive traffic management-depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration.
We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence.
| Subjects: | Networking and Internet Architecture (cs.NI); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2411.19714 [cs.NI] |
| (orarXiv:2411.19714v2 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2411.19714 arXiv-issued DOI via DataCite |
Submission history
From: Navid Salami Pargoo [view email][v1] Fri, 29 Nov 2024 14:02:00 UTC (28,572 KB)
[v2] Mon, 13 Jan 2025 02:43:47 UTC (28,572 KB)
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View a PDF of the paper titled The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications, by Navid Salami Pargoo and 10 other authors
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