Smart City Gnosys
Smart city article details
| Title | Leveraging Ambient Sensing For The Estimation Of Curiosity-Driven Human Crowd |
|---|---|
| ID_Doc | 35030 |
| Authors | Das A.; Narayan K.; Chakraborty S. |
| Year | 2022 |
| Published | SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings |
| DOI | http://dx.doi.org/10.1109/SysCon53536.2022.9773844 |
| Abstract | Identification and characterization of human crowd formulation have been a topic of immense interest in recent times due to its applicability in a wide range of smart-city applications covering infrastructure automation to targeted advertising. The core idea is to extract the dynamics and associated behavioural patterns of mass gatherings within an environment through a continuous remote monitoring of the crowd. In general, the existing approaches heavily rely on computer vision and image processing based algorithmic tools and techniques to address this problem or mandate the crowd entities to carry a smartphone with them. However, considering the ubiquitous design goals of futuristic smart applications, camera and smartphone driven active sensing is not suitable to honour users' right to privacy by requiring an active user participation. In this work, we introduce a novel approach towards measuring the spatiotemporal significance of an object in terms of the curious crowd it has attracted over the others. The proposed approach utilizes a set of passive sensors and Wireless signal properties for the necessary estimation. We validate the idea using a room-scale testbed with rigorous experimentation in a real-world scenario. The low cost solution has minimal invasive footprints towards privacy and is capable to reach beyond 90% of accuracy for this measurement. © 2022 IEEE. |
| Author Keywords | Ambient; Crowd; CSI; IoT; Passive; Sensing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
59717  | 0.873 | Rashid M.T.; Wang D. | Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne Sensing | Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference, 2021-October (2021) |
| 41975 | 0.87 | Huang W. | Ph.D. Forum: A Study On Real-Time Crowdedness Sensing And Pedestrian Tracking In Multi-Environment | SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems (2024) |
| 41420 | 0.869 | Prochazka J.; Plasilova A. | Passive Mobile Crowdsensing For Determining The Volume Of Passengers In Public Transport | Proceedings of 2023 2nd International Conference on Informatics, ICI 2023 (2023) |
| 16695 | 0.857 | Mathew S.S.; El Barachi M.; Kuhail M.A. | Crowdpower: A Novel Crowdsensing-As-A-Service Platform For Real-Time Incident Reporting | Applied Sciences (Switzerland), 12, 21 (2022) |
| 29635 | 0.852 | Lin C.; Flanigan K.A. | Human Trajectory Estimation Using Analog Privacy-Preserving Urban Sensing Technologies | Proceedings of SPIE - The International Society for Optical Engineering, 12486 (2023) |
| 16714 | 0.851 | Bellavista P.; Cardone G.; Corradi A.; Foschini L.; Ianniello R. | Crowdsensing In Smart Cities: Technical Challenges, Open Issues, And Emerging Solution Guidelines | Handbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities (2015) |
| 48369 | 0.85 | Bessho M.; Sakamura K. | Sensing Street-Level Crowd Density By Observing Public Bluetooth Low Energy Advertisements From Contact Tracing Applications | 2021 IEEE International Smart Cities Conference, ISC2 2021 (2021) |