The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications

2024-11-29

arXiv.org

AI Summary

To address scalability bottlenecks in distributed urban sensing systems—particularly concerning spatiotemporal alignment of heterogeneous multi-source sensors, real-time data fusion, and edge processing—this paper proposes SASS, a distributed sensing architecture tailored for city-scale applications. SASS introduces a novel three-layer service abstraction: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing—each semantically clear and composable. It integrates timestamp calibration, cross-camera spatiotemporal alignment, a lightweight edge collaboration framework, and an event-driven fusion middleware. Experiments demonstrate an 88% reduction in synchronization error, end-to-end latency ≤50 ms, >10% improvement in pedestrian and vehicle detection accuracy, and over 10× increase in system throughput. Deployed and validated in real-world parking lots and urban intersections, SASS effectively supports latency-critical intelligent applications, including pedestrian safety alerting and adaptive traffic control.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Distributed Sensing Systems
• Real-time Data Processing
• Urban Management

Innovation

• Distributed Sensing System
• Time Synchronization
• Multi-source Information Fusion