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SENSEABLE CITY LAB

Towards Large-scale Drive-by Sensing withMulti-purpose City Scanner Nodes

Simone Mora, Amin Anjomshoaa, Tom Benson, Fabio Duarte, and Carlo RattiSenseable City Laboratory

Massachusetts Institute of TechnologyCambridge, MA, USA

{moras, amina, bensont, fduarte, ratti}@mit.edu

Abstract—Drive-by sensing has the potential to provide hyper-

local data to study a number of urban phenomena and deliver

actionable insights for public good. Yet drive-by deployments

are often characterized by small fleets, huge costs and lack of

flexibility to adapt to a city’s multiple sensing needs. The City

Scanner project aims to accelerate the development of drive-

by sensing by turning everyday vehicles into sensing nodes. In

this paper we present Greta II, a solar-powered sensing platform

that allows for low-cost continuous data collection and streaming

of multiple phenomena. Thanks to a modular design it can be

customized with off-the-shelf sensors and re-used across different

deployments. Greta II can be easily built and deployed without

disrupting a vehicle’s normal operation. The platform has been

validated during test deployments in two large American cities

to collect air quality data. Drawing on our experience building

and deploying Greta II we discuss challenges to be solved for

enabling large-scale drive-by sensing.

Index Terms—Smart City, Drive-By, Sensing, Platform, Inter-

net of Things, IoT.

I. INTRODUCTION

V

ehicular-based sensing, drive-by for short, emerged adecade ago as a technological paradigm to enable assem-

bling highly-granular space/time datasets of urban phenomena.Although it has been popularized by applications for streetview imaging, e.g. [1]; drive-by approaches are nowadays usedto sense a wide range of phenomena including air pollution,natural gas leaks, road lighting, street surface quality, energyefficiency of building envelopes, traffic congestion and crowdflows. Data collected can be used in a variety of applicationsand has implications for environmental monitoring, policymaking and governance. By relaying on sensors mounted onvehicles such as cars, buses and bikes, drive-by sensing canprovide a more cost-efficient solution compared to stationarysensors or remote sensing via planes and satellites.

Most drive-by data collection experiments require a constantflow of data sampled at high spatial and time frequency. Anumber of urban phenomena have hyper-local nature and arecharacterized by high volatility. For example, air pollution canvary by a factor of 8x within small neighbourhoods such ascity blocks [2]. To add, due to technical limitations, sensorsthat are mounted on vehicles might be less accurate than theones that can be deployed on the ground stations. Multiple andredundant readings can help with the reduction of data biases.

Despite the need for collecting large datasets, most of drive-by sensing experiments rely on a small fleet of vehicles andsuffer from the following two challenges.

First, most experiments rely on especially-purposed vehi-cles, retrofitted with sensors, and driven on specific routes bydedicated drivers with the primary aim of collecting data. Suchapproaches can be hardly scaled up to the required sensingneeds due to human-time and cost factors. To add, a sensingfleet big enough to sufficiently describe certain phenomenawould significantly contribute to city traffic.

Second, although advances in low-cost sensors and IoTplatforms can accelerate the devices development; most ofdrive-by sensing platforms are still difficult to deploy, requiremodification on the hosting vehicle, are hard to customize tomultiple sensing needs and to manufacture on a large scale.

The City Scanner project1 aims at developing a sensingplatform to enable large scale drive-by deployments usingeveryday urban vehicles as sensing nodes. The platform hasat its core an autonomous, solar-powered and modular device,named Greta II, which latest design iteration and early evalu-ation is described in this paper.

Greta II does not pose any requirement on the hostingvehicle, it can be easily attached and removed to everydayvehicles, allowing to re-configure the sensing fleet easily andon-demand. Further, because of its modular design, it canbe configured with a number of sensors including particulatematter counters, accelerometers, thermal cameras and noisemeters. Captured data is streamed in real-time to cloud andavailable for analysis and visualization via APIs.

We validated our platform during three data collectionexperiments in two large American cities. We have collecteda total of 21,000 data-points including temperature, humidityand air quality. By reflecting on our experience designing,developing and deploying Greta II, and the challenges wecountered during deployments, we draw lessons learned andimplications for design of future drive-by platforms.

II. RELATED WORKS

Over the last decade, several research works have adoptedthe concept of vehicle-based sensing in urban environments.

1http://senseable.mit.edu/cityscanner

This is a field also known as “urban sensing” [3]; and has thegoal of delivering actionable insights for public good.

Although drive-by sensing can be performed without de-ploying additional sensors, for example leveraging data fromsensors embedded in drivers’ smartphones [4] (e.g. GPS,accelerometers, microphone) or available via the vehicle OBDinterface; to exploit a vehicle for environment sensing, supple-mentary sensing components are needed.

These solutions often make use of IoT architectures andtechnologies, and have a lot in common with the broaderresearch field of Wireless Sensor Networks (WSNs) that tackletechnical challenges such as reducing energy consumption andcommunication overheads [5], multi-agent cooperation andtask distribution [6] and delay bounds for sensitive tasks [7].

Research works have shown feasibility of using drive-bysensors e.g. for monitoring street lighting infrastructure withphoto-transistors on top of vans [8], monitor air quality usingparticulate matter and gaseous sensor on buses [9], trash trucks[10], [11], cleaning vehicles [12] and cars [2], [13], surveyingroad (surface) quality [14], analyzing thermal efficiency forbuilding envelopes [10]; and overall cyclist experience [15].

Yet, those works have developed sensing devices that arecustom-built and make use ad-hoc configurations that canbe hardly replicated by other researchers, both because oftechnical complexity and use of proprietary hardware andsoftware solutions. They either connect to the power networkof hosting vehicles or use batteries that needs to replaced.For some experiments, the collected data is downloaded at theend of experiment and no network connectivity or real-timedata processing takes place. As a consequence, the prohibitivecost and technical challenges of real field experiments withhundreds or thousands of instrumented vehicles, often causesresearchers to fall back on simulation modes and tools [16].

III. LARGE-SCALE DRIVE-BY SENSING

According with our literature review and our experiencedesigning, building and deploying drive-by systems [10], [17];we identified three limitations in present drive-by sensingworks that hinder large-scale deployments:

• Adaptation of the hosting vehicle is required, e.g. tofasten sensors on the roof or wire them to the vehicleelectrical system. Depending on the type of devices,specific vehicles and dedicated drivers might be required.Vehicles’ alterations might be permanent.

• Drive-by systems are often designed for single-purposedata collection. Furthermore, once the sensing device isfinalized, no alteration or customization’s are permitted orreplacement of sensors with different ones are allowed.This doesn’t allow reuse of a sensing fleet for differentdata collection purposes or for multi-purpose sensing.

• Drive-by systems are often ad-hoc solutions that cannotbe replicated and built on a large scale. This is due both tothe prototyping process stopping at an early stage and theuse of proprietary or expensive technologies and tools.

For these reasons, most drive-by deployments are centeredon very few, non-reproducible data collection experiments that

Fig. 1. Greta II sensing node

make use of small fleets of vehicles. The data collected isusually enough to validate the systems from a technical pointof view and study phenomena such as street surface qualitythat have a rather non-volatile nature, but it can hardly be usedfor broader applications such as environmental studies or forpolicy making.

The aim of our work is to enable large scale drive-bysensing. The City Scanner project [10] is developing a multi-purpose platform that allows for capturing a wide range ofphenomena in order to identify multiple city features. Theplatform has at its core a customizable sensing device, GretaII, that allows for low-cost, autonomous and continuous datacollection of multiple phenomena. Our work builds on populartechnologies and tools and will be eventually released tothe public as open source. We expect that Greta II willenable large-scale data collection experiments using fleets ofeveryday vehicles (e.g. buses, taxis, trains) as sensing nodes.The following section describes Greta II’s latest developmentiteration.

IV. GRETA II SENSING NODES

Greta II prototype is a self-contained cylindrical devicesized roughly 20cm in diameter and 2Kg in weight (Figure 1).It is composed by a multi-layer hardware architecture enclosedin 3D-printed shell made of black nylon. The printed partsare 10x the strength of plastic by reinforcing chopped-carbonnylon with continuous strands of fiberglass. The shell com-bines industrial quality with affordability. The base (visible inwhite in Figure 1) was constructed in 0.6cm PLA to provide a

structural resilience in connecting between the shell, magnetsand potentially, multiple solar panels.

The device can be extended using off-the-shelf sensors andsupports multiple communication interfaces.

This prototype aims at showing feasibility of supportingdevelopment of scalable, on-demand, sensing fleets; the designgoals that have guided the development of our prototype aredescribed in the following:

A. Simple deployment

Greta II poses little deployment requirements on the hostingvehicle. The sensing node can be easily affixed to or removedfrom the host vehicle. This can be done without requiring theuse of specialist tools, and without modification or interferenceof the vehicles normal operation. This has been made possibleby addressing the main factors that limited deployment inprevious works: the way the sensors are secured to the vehicleand the way in which they are powered.

First, Greta II is powered by solar energy by using9W, monocrystalline photo-voltaic panels (PVs). Because theamount of solar energy harvested largely vary according withthe location of a city and the route of the vehicle, Greta IIsupports for tiling multiple PVs (each of them sized 21x16cm)in order to satisfy the energy requirements of the device(Figure 1). The support is provided by a physical mountingstructure that allows for secure tiling of PVs panels, a powermanagement circuitry and a rechargeable battery working asa energy buffer. According to our early tests, two PVs aresufficient to power the device all year long in cities withsimilar solar irradiance values to New York City. Furtherdetails are described later in the discussion section.

Second, Greta II makes use of high-quality rubber-coatedmagnets as a momentary anchoring system. Because theweight of each node can vary according with the type ofsensors used and the number of solar panels tiled together;multiple magnets can be used in arrays, with each of themdeveloping a force of circa 200N. The rubber coating increasesfriction and protect the mounting surface from being scratched.Due to the type of magnets used, the strength of the bindingis not influenced by temperature.

B. Multi-purpose, customizable architecture

Greta II is a modular extensible architecture designed tosupport a wide range of sensors, e.g. particle counters, gassensors, noise meters and thermal cameras. Greta II has atits core an energy-efficient architecture that provides a set ofservices to enable easy customization of the sensing capabilityusing off-the-shelf sensors.

Core services include:• Data processing - a low-energy 32bit CPU manages

both background services (described below) and multiplesimultaneous data collection processes.

• Adaptive data streaming - a 3G modem allows for real-time data streaming. Older 2G networks are also sup-ported. A software routine adapts the frequency of data

broadcasts according with user settings or battery state-of-charge; either providing real-time data or aggregatingdata in larger chunks transmitted at lower rate, for energysaving. An on-device 16GB flash memory is used asdata buffer and for backup purposes. Data is broadcastedto a cloud back-end and made available to third-partyapplications via dedicated APIs.

• Location awareness service - a 66 channels GPS chipprovides capability for geo-tagging of the data collected.A 9-DoF IMU sensor provides additional precision, de-tecting vehicle’s start & stop and orientation, allowing toput the GPS in standby mode whenever not moving.

• Power management service - provide reliable energysupply to the core components and plugged sensorsdevices, harvesting energy from the connected photo-voltaic panel(s). Due to the high variance of solar energyproduction, a Li-Po battery is used as energy buffer. Thesystem is complemented by a circuit for battery chargemanagement, state of charge estimation and energy con-sumption monitoring.

• Remote administration service - each sensing node can beremotely administered. Firmware updates can be pushedto the device over the air. Telemetry data includingbattery health and charge, location and device logs canbe reviewed via a web dashboard.

Sensors can be connected to the core module via hardwareinterfaces supporting the popular protocols SPI, I2C and TTL.Although the three interfaces can be used simultaneously, thenumber of sensors that can be plugged to the device might belimited by their physical size (need to fit in the node enclosure)and energy requirement.

The firmware running on the device is written in C andmakes use of FreeRTOS2 and the Particle Device OS3. Furtherimplementation details together with instructions for customiz-ing the platform will be addressed in a future publication.

C. Low-cost, simple manufacturing

Greta II makes use of low-cost, off-the-shelf components.Hardware schematics, bill of components, firmware and shellconstruction files will be made available under an open-source license in the near future. Greta II motherboard canbe produced with desktop milling machines or ordered viaonline PCB services. Manual assembly can be done withbasic soldering skills. Electronic parts adopted are availablevia several e-tailers. The shell can be produced with desktop3D printers. The overall cost for producing one Greta II iscirca USD500, plus the cost for the sensors.

V. TEST DEPLOYMENTS

Greta II has been evaluated during three test deployments.The first experiment lasted for about three hours and tookplace in Boston, MA during November 2018. The second andthird experiments took place in New York City in December

2Free RTOS - https://aws.amazon.com/freertos/3Device OS - https://www.particle.io/device-os/

Fig. 2. Greta II prototypes during a test deployment in New York City

2018 and lasted for about six hours each. The primary goal ofthese tests was to evaluate the prototype’s deployment processfor reliability by collecting telemetry data and observationsfrom the researchers on-site. As secondary goal, we aimed atevaluating feasibility of collecting air quality data, specificallyparticulate matter concentrations (PM2.5), temperature andhumidity. Hyperlocal air quality mappings is one of the mostcommon drive-by sensing use case, as demonstrated by [9]–[13]

A. ProcedureDuring the first experiment one Greta II node has been

equipped with a set of sensors to monitor air quality, includingan Alphasense OPC-N2 particulate counter wired on the SPIinterface and a Bosch BME280 temperature and humiditysensor on the I2C link. An external active GPS antennawas also adopted to ensure high reliability of the locationawareness service. During the second and third experimentan additional device had been built and deployed togetherwith the first one (the two prototypes are visible in Figure 2).Although the additional prototype included the same set ofsensors, and looked identical to the first one, it was equippedwith an additional PMS5003 particulate counter wired on theTTL interface. The addition of a third sensor was meant tostress-test the device with the 3 sensor interfaces collectingdata simultaneously. On the contrary, no external GPS antennahad been provided, in order to evaluate difference in locationprecision between the two devices. Both prototypes wereequipped with one solar panel.

The prototypes were configured to sample and log datato the flash memory with a 1Hz frequency. In addition,during the second and third experiment data was broadcastedevery 60 seconds to a cloud back-end. Besides capturingtime, location, air quality (PM2.5) and weather (temperatureand humidity) data, the device was also configured to log anumber of telemetry features including PV voltage and currentproduction, system’s voltage and current draw, GPS accuracyand data streaming status.

The Greta II prototypes were deployed on two rental carsdriven in turns by three researchers. Predefined test routeswere prepared to touch different urban districts including

residential neighborhoods, downtown areas and highways. Theroutes were also designed to cross fixed air quality monitoringstations in order to provide reference values to be used asground truth for the evaluation of air quality data.

B. Results

During each test, deployment of the device on the roof of thecar and successive removal had been effortless. The magneticbinding performed well and kept the device securely in place.The bending properties of the PLA base of Greta II allowedfor the bottom of the device to adapt to the car roof curvature,ensuring a correct alignment of the rubber-coated magnets (asvisible in Figure 2).

The two prototype’s test performed as expected and col-lected a total of circa 21,000 data-points.

Telemetry data reveal insights on the device operation.Regarding the accuracy of the location awareness service

(Section IV-B), during the last experiment, data shows amedian error of 2.1 meters (SD 0.6 meters) for the prototypewith external GPS antenna. This allowed for geo-tagging dataat a street-level granularity. Instead, the prototype equippedwith internal antenna was affected by a median error of 2.9meter (SD 26.3 meters). The other two experiments showsimilar results. The high standard deviation in the prototypeequipped with internal antenna can be motivated by the deviceloosing track of a reliable GPS fix while driving in the streetcanyons; making therefore not possible to geo-tag datapointswith a sufficient precision for the purpose of this study.

Both prototypes never lost connection to the cellular net-work. This is not a surprising result due to the highly urbanizedareas chosen for the experiments.

Figure 3 compares the energy (current) produced by thesolar panel and consumed by the system selecting 3-hourswindows from two experiments. The first section relates tothe Boston experiment, which took place with cloudy weather,average temperature of 10 degree Celsius and 40% humidity.Boston is characterized by Global Horizonal Irradiance (GHI)4

of 3.84kWh/m2/day. The other selection refers to the second

4GHI represent both direct and diffused solar radiation received on a surfacethat is always on a horizontal plane.

Fig. 3. Measured current produced (green) and consumed (red) during the first and third data collection experiment. Overcasts with periods of sun on theleft, snow on the right.

NYC experiment wich took place in snowy weather, averagetemperature of 6.5 degree Celsius and 60% humidity. NYC’sGHI is 3.93kWh/m2/day. Although the two tests took placein cities with very similar solar irradiance and both in thewinter season, they represent in turn the best and worst weathercondition under which our experiments took place.

Average current consumed was 260mA in both tests. This isin line with technical specification of the electronic componentused, which show a 200mA consumption for the OPC-N2particulate counter, 80mA for the CPU, 40mA for the GPSand 20-2000mA (peak current) for the 3G modem.

Average current produced by the PV was circa 170mA forthe first experiment and 40mA for the second experiment.Because in both experiments the current produced was lowerthan the nominal requirement, the node’s battery supplied extracurrent to match energy requirements for correct functioning.As a consequence, in order to work continuously under theweather condition under which the experiments took place,the sensing nodes should have been equipped with extra solarpanels.

Air quality data from the two cities are currently analyzed toidentify location of potential pollution hotspots and understandthe feasibility of using particulate counter sensors for drive-by. We are in the process of acquiring data from stationarysensors along the route to be used for validation. Findingswill be reported in a upcoming publication.

VI. DISCUSSION

A number of challenges emerged from our test deployments.Using air quality sensors required to change the outer shell

of Greta II in order to allow a constant flow of air throughthe device and required a new approach for waterproofingthe system. Other sensors, e.g. thermal cameras have strictrestriction on the orientation in space of camera lenses; othersensors might have strict energy requirements. The physicaland hardware characteristics of sensors deeply impacts on thedesign of the sensing platform. To make a truly customizable,multi-purpose platform, modularity has to be considered fromhardware, software and physical design perspectives.

Our analysis of the energy balance of the system shows thataverage current used by the system was between two timesand seven times the energy produced by the system’s PV. As aconsequence, to work autonomously, the sensing nodes shouldbe equipped with at least two PVs in the first case and seven inthe second case. On the other side, under direct sunlight, thePV delivered a peak current between two times and four timesthe demand of the system. This is visible in Figure 3-left withthe spikes in green corresponding with periods of clear sky.This is an interesting result considering that the experiment hasbeen performed in the months with the lowest solar irradianceof the year and under poor -but not severe- weather. We couldspeculate that during summer, with clear sky and long dayhours, one PV could supply more than four times the energyrequired of the system. In this case, the sensing node shouldbe equipped with a large capacity battery in order to store theextra electrical charge to be used during overcast days or evenup to several winter weeks; comporting a trade-off betweensystem autonomy versus cost, weight and size. Weather is notthe only parameter affecting energy production. Location ofdeployment and time of the year, also are important factors; yetthey can be easily estimated. Instead, the route of the vehiclecould heavily affect the amount of solar irradiance receivedby the PV. Other effective factors include specific propertiesof street canopy, urban canyons or travel time under bridgesor in tunnels. This factor is hard to estimate, especially forunscheduled vehicles such as taxis. Finally temperature belowfreezing point and over 40 degree Celsius deplete efficiencyof PVs as well as batteries.

In short, although the system’s power consumption is influ-enced by the type of sensor plugged into the sensing node,its energy requirement is very constant and can be reasonablyestimated from components specifications. On the other hand,energy produced by solar is affected by a number of externalparameters which are challenging to estimate. Further work isrequired in order to develop a model to estimate the amountof energy that can be produced by one sensing node, give thefactors mentioned above.

VII. CONCLUSION AND FUTURE WORK

In this paper we have identified factors that limit currentdrive-by sensing efforts. We presented Greta II sensing nodeplatform, part of the City Scanner project, that thanks to itshigh customizability and easy deployment procedure has thepotential of turning everyday vehicles into sensors; enablingfor large-scale deployments. We have shown feasibility ofusing Greta II for collecting air quality data and highlightedchallenges that need to be tackled to ensure scalability andcontinuous, autonomous operations of our platform.

Future work points at multiple directions. We aim at testingGreta II with different sensors, e.g. thermal cameras, noisemeters and to arrange test deployments in multiple cities char-acterized by different environmental and spatial conditions. Weare also working on a new hardware that allows for automatedmanufacturing of the sensing nodes, to lower production costsand time. We are working towards developing a model topredict the solar energy that can be produced by the sensingnode. Finally, we plan to provide APIs with more supportfor building data analytic apps and an interactive visualizationdashboard fed by City Scanner data. We plan to releaseGreta II’s hardware schematics, firmware and construction filesunder a open source license in the coming weeks.

ACKNOWLEDGMENT

The authors thank Allianz, Amsterdam Institute for Ad-vanced Metropolitan Solutions, Brose, Cisco, Ericsson, Fraun-hofer Institute, Liberty Mutual Institute, Kuwait-MIT Cen-ter for Natural Resources and the Environment, Shen-zhen, Singapore-MIT Alliance for Research and Technology(SMART), UBER, Victoria State Government, VolkswagenGroup America, and all the members of the MIT SenseableCity Lab Consortium for supporting this research. Also, thefinancial support of the Austrian Science Foundation (FWF)through the Erwin-Schrdinger Grant No. J3693-N30.

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