Smart City Gnosys
Smart city article details
| Title | Urban Sensing For Human-Centered Systems: A Modular Edge Framework For Real-Time Interaction |
|---|---|
| ID_Doc | 60171 |
| Authors | Pargoo N.S.; Ghasemi M.; Xia S.; Turkcan M.K.; Ehsan T.; Zang C.; Sun Y.; Ghaderi J.; Zussman G.; Kostic Z.; Ortiz J. |
| Year | 2025 |
| Published | HumanSys 2025 - Proceedings of the 2025 3rd International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems, 2025 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2025 Workshops |
| DOI | http://dx.doi.org/10.1145/3722570.3726890 |
| Abstract | Urban environments pose significant challenges to pedestrian safety and mobility. This paper introduces a novel modular sensing framework for developing real-time, multimodal streetscape applications in smart cities. Prior urban sensing systems predominantly rely either on fixed data modalities or centralized data processing, resulting in limited flexibility, high latency, and superficial privacy protections. In contrast, our framework integrates diverse sensing modalities, including cameras, mobile IMU sensors, and wearables into a unified ecosystem leveraging edge-driven distributed analytics. The proposed modular architecture, supported by standardized APIs and message-driven communication, enables hyper-local sensing and scalable development of responsive pedestrian applications. A concrete application demonstrating multimodal pedestrian tracking is developed and evaluated. It is based on the cross-modal inference module, which fuses visual and mobile IMU sensor data to associate detected entities in the camera domain with their corresponding mobile device. We evaluate our framework's performance in various urban sensing scenarios, demonstrating an online association accuracy of 75% with a latency of ≈39 milliseconds. Our results demonstrate significant potential for broader pedestrian safety and mobility scenarios in smart cities. © 2025 Copyright held by the owner/author(s). |
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