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City Scanner Senseable City Lab :.:: Massachusetts Institute of Technology This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the definitive publisher-authenticated version, please refer directly to publishing house’s archive system SENSEABLE CITY LAB This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2018.2839058, IEEE Internet of Things Journal IEEE INTERNET OF THINGS JOURNAL, 2018 1 City Scanner: Building and Scheduling a Mobile Sensing Platform for Smart City Services Amin Anjomshoaa, Fabio´ Duarte, Daniel¨ Rennings, Thomas Matarazzo, Priyanka deSouza, and Carlo Ratti Abstract—A large number of vehicles routinely navigate such sensors with the aim of capturing a specific feature of the through city streets; with on-board sensors, they can be trans- urban environment, e.g., air quality [4], [21] or road conditions formed into a dynamic network that monitors the urban envi- [37]. One of the most commonly used terms in the vehicular- ronment comprehensively and efficiently. In this paper, drive- by approaches are discussed as a form of mobile sensing, that based sensing paradigm is Vehicular Sensor Networks (VSNs), offer a number of advantages over more traditional sensing in which vehicles have a certain role in a Wireless Sensor approaches. It is shown that the physical properties of the urban Network (WSN). In this paper, we adopt the term drive-by environment that can be captured using drive-by sensing include sensing to refer to urban sensing using road vehicles. Ambient Fluid, Electromagnetic, Urban Envelope, Photonic, and Drive-by sensing offers a number of advantages over more Acoustic properties, which comprise the FEELS classification. In traditional approaches, such as remote and stationary sens- addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. ing. Natural phenomena and physical properties are typically The mobility patterns of sensor-hosting vehicles play a major continuous signals in both temporal and spatial dimensions. role in drive-by sensing. Vehicles with scheduled trajectories, To represent these signals as digital sensor data, each sensing e.g., buses, and those with less predictable mobility patterns, e.g., channel must capture sufficiently dense spatiotemporal data taxis, are investigated for sensing efficacy in terms of spatial and for its application. Yet, in many environmental use-cases, temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass the collected data have been constrained in a spatial and/or data acquisition on a multitude of city features. The City Scanner temporal dimension, which limits the information that can framework follows a centralized IoT regime to generate a near be extracted. For instance, stationary air pollution sensors real-time visualization of sensed data. The sensing platform was measure the ambient pollutants in precise locations, but may mounted on top of garbage trucks and collected drive-by data miss potential differences in nearby streets and neighborhoods for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. [35]. On the other hand, satellite-based measurements can be Based on a real-world application, we discuss and show the used to infer air quality levels over large swaths of land, potential of using drive-by approaches to collect environmental but only provide temporal snapshots of pollutant concentra- data in urban areas using a variety of non-dedicated land vehicles tions. Moreover, robust mathematical models are required to to optimize data collection in terms of spatiotemporal coverage. predict more detailed changes in surface temperature over Index Terms—Environmental monitoring, Mobile sensing, Mo- time [31]. These methods have however been shown to be bility patterns, Road vehicles, Smart city, Spatiotemporal phe- accurate enough for certain applications that do not require nomena, Urban areas, Wireless Sensor Networks. a high temporal resolution, such as measuring chlorophyll concentration in coastal zones [10]. I. INTRODUCTION This paper introduces City Scanner, a mobile sensing plat- ITIES are data factories; enormous amounts of data are form for smart city services. Related works in drive-by sensing generated from various sources, every day. Increasing are discussed in Section II. Subsequently, a general categoriza- effortsC to collect such data from the urban environment are tion of spatiotemporal phenomena that can be captured in a driven by promises of improved services or products for the drive-by approach is introduced in Section III. In Section IV, public, ranging from self-driving cars, to smart buildings, the sampling characteristics of drive-by sensing methods are and data-driven traffic lights. Collections of spatiotemporal discussed and compared with airborne and stationary sensing. datasets of urban phenomena can thus empower advanced Since City Scanner is specifically created to be deployed on analytics and technical solutions for local governments and a fleet of existing vehicles, the suitability of various vehicles urban planners. in terms of spatiotemporal coverage is addressed in Section Recently, portable sensors, with high accuracy and embed- V. The ideology of the paradigm is furthermore elaborated ded communication technologies, have become available and upon in Section VI. As a proof of concept, City Scanner has affordable. A number of studies have utilized vehicles to carry been deployed on municipal garbage trucks in Cambridge, MA for eight months. The outcomes and implications of this The authors are with the Senseable City Laboratory, Massachusetts Institute application are discussed in Section VII. Finally, we conclude of Technology, Cambridge, MA, USA e-mail: amina, fduarte, rennings, tomjmat, desouzap, ratti @mit.edu. { our work in Section VIII. Fabio´ Duarte is also} with Pontif´ıcia Universidade Catolica´ do Parana,´ Brazil. II. RELATED WORKS Daniel¨ Rennings is also a MSc student at Delft University of Technology, the Netherlands. In the domain of VSNs, a number of studies have fo- Manuscript received November 11, 2017. cussed on the network architecture and communication as- 2327-4662 (c) 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2018.2839058, IEEE Internet of Things Journal IEEE INTERNET OF THINGS JOURNAL, 2018 2 TABLE I OVERVIEW OF DRIVE-BY SENSING STUDIES Title Urban Phenomena Utilized Sensors (* is from smartphone) Type of Vehicle CarTel [12] Traffic congestion, WiFi access points, driv- GPS, WiFi, OBD, camera Car ing behavior BikeNet [5] Passing vehicles, cycling behavior, topogra- GPS, 2-axis accelerometer, CO2 meter, reed Bike phy, air quality, noise pollution, visual map relay, camera*, microphone* Nericell [25] Road quality, traffic conditions Accelerometer*, microphone*, GPS* Car ParkNet [20] Parking statistics GPS, ultrasonic rangefinder Car VOTERS [37] Road conditions GPS, camera, microphone, accelerometer, Van mm-wave radar, GPR, tire pressure sensor N.A. [15] Street lighting infrastructure GPS, light sensor, CCD Camera, odometer Van interface, IMU, OBD N.A. [30] Thermal signature GPS, long wave infrared radiometric cam- Van eras, near-infrared camera, optical camera N.A. [1] Air pollution GPS, NO, NO2, black carbon Google Street View vehicles pects, leading to many publications on vehicle interactions purpose case, the focus is on a single urban phenomenon, in terms of communication [34]. The prohibitive costs and although multiple sensors may be utilized. privacy implications of real field experiments with hundreds Drive-by sensing has been employed to measure city fea- of instrumented vehicles envisioned in these scenarios, has tures ranging from natural phenomena such as temperature, forced researchers and developers to fall back to simulations humidity, and air quality, to those more closely related to [9]. On the other hand, much less attention has been given to the urban environment, e.g., parking spot occupancy, street research that has utilized a fleet of non-interconnected ground light infrastructure, road conditions, traffic congestion, and vehicles as a resource for monitoring the environment, which WiFi access points. However, the majority of efforts are was previously termed as drive-by sensing. An overview of focused on quantifying air quality and road conditions in drive-by sensing studies, with their respective configurations urban environments2. The works on air quality often include and sensing purposes is presented in Table I. meteorological measurements, predominantly being temper- A little over a decade ago, drive-by sensing emerged as a ature and humidity, to correct the raw measurements for new network paradigm for sensing urban environments [16]. the effect of environmental parameters using a calibration One of the first works in this domain, [12], already envisioned mechanism (e.g. [14], [22]). Studies on road quality have the
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