City Scanner: Building and Scheduling a Mobile Sensing Platform for Smart City Services

• Published in: IEEE internet of things journal 2018-12, Vol.5 (6), p.4567-4579
• Main Authors: Anjomshoaa, Amin, Duarte, Fabio, Rennings, Daniel, Matarazzo, Thomas J., deSouza, Priyanka, Ratti, Carlo
• Format: Article
• Language: English
• Subjects: Acoustic properties; Buses (vehicles); Data acquisition; Detection; Environmental monitoring; Garbage; Global Positioning System; Intelligent sensors; Intelligent vehicles; mobile sensing; mobility patterns; Photonics; Physical properties; Road traffic; road vehicles; Scanners; Smart cities; smart city; Spatiotemporal phenomena; Temperature sensors; Trucks; Urban areas; Urban environments; Vehicles; wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2018.2839058

A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing approaches. It is shown that the physical properties of the urban environment that can be captured using drive-by sensing include ambient fluid, electromagnetic, urban envelope, photonic, and acoustic properties, which comprise the FEELS classification. In addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, e.g., buses, and those with less predictable mobility patterns, e.g., taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near real-time visualization of sensed data. The sensing platform was mounted on top of garbage trucks and collected drive-by data for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, we discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of nondedicated land vehicles to optimize data collection in terms of spatiotemporal coverage.