Estimating Rainfall Intensity by Using Vehicles asSensors

Carlos T. Calafate, Karin Cicenia, Oscar Alvear, Juan Carlos Cano, Pietro ManzoniDepartment of Computer Engineering (DISCA)

Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022, Spaincalafate@disca.upv.es, ciceniak@gmail.com, oalvear@gmail.com, {jucano, pmanzoni}@disca.upv.es

Abstract—Vehicles are key elements in the envisioned SmartCities, not only providing more efficient mobility, but also becom-ing mobile network elements able to perform many useful tasks.Environment sensing is a good example where the combinationof data coming from vehicles allows achieving insight onlycomparable to the deployment of hundreds or thousands ofsensors in a city. Obtaining rainfall estimations with a highspatial granularity is an example of a task where relying ontraditional methods would become too expensive due to the highnumber of data sources required. Vehicular networking has agreat potential to address such challenge by converting everyvehicle in a rain sensor. In this paper we carry out a simulationstudy to estimate the rainfall intensity in a specific area using avehicular network as data source. To this purpose, we model arainfall pattern taking real values as reference, and we devisea simulation scenario where the rainfall pattern is deployed.Experimental results using the OMNeT++ simulator show that,even with a low density of vehicles contributing to the proposedmonitoring system, rainfall intensity can still be predicted with ahigh accuracy and granularity, thereby validating the proposedapproach.

Index Terms—Vehicular networks; rainfall prediction; rainsensors; simulation.

I. INTRODUCTION

The Smart City paradigm assumes that all the elementsintegrating such cities are smart. Vehicles are not an exception,and so in the future all vehicles are expected to be endowedwith different sensors, wireless communication devices, andto have embedded mobile devices in the form of on-boardcomputers and GPS navigators. Such devices shall enablemaking intelligent decisions and facilitate data sharing, wheredriver assistance, car safety, fuel prices, or road conditionsare just some examples about the information that could beexchanged. Based on the cooperation among vehicles [1], andthe information they are able to provide, many interestingapplications can be built.

Recently, the crowdsensing paradigm [2] has emerged asan approach that, relying on the individual contributions ofeach user, creates a larger view of the phenomenon underobservation. Obviously, this paradigm can be easily adapted tovehicular environments, where each vehicle, through the dif-ferent sensors it has attached, is able to contribute with usefulinformation about any conditions of interest at its particularlocation (e.g. traffic status). The delivery of such informationto a centralized database, followed by data analysis using

appropriate tools, is then able to provide valuable insight froma global perspective.

In this paper we study how vehicles can be used to create acooperative rainfall intensity measurement system. Comparedto traditional systems, this solution has several advantages suchas greater coverage, greater granularity, and minimal cost. Themain drawbacks are the dependency on user willingness toparticipate, and the lower accuracy that these distributed, low-cost sensors have compared to professional ones.

An essential requirement to make such solution feasibleis that participating vehicles should be equipped with a rainsensor, which could be the one that regulates their windshieldwiper. In fact, nowadays, most vehicles have integrated driver-programmable intelligent windscreen wipers that detect thepresence and amount of rain using a rain sensor, adjusting thewiper speed accordingly [3]. This value can be used to estimatethe rain intensity to a certain extent. In the current work,exploiting the features and capabilities of existing vehicles,we perform a simulation study in order to estimate in near-real-time the intensity of rainfall based on the informationsensed by vehicles in a specific area. We assume that eachparticipating vehicle will be considered as a rain sensor, andthat sensed data is uploaded wirelessly to a central cloud serverfor data fusion and processing.

The rest of the paper is structured as follows. In SectionII we present some related works on the use of vehicularnetworks for participatory environmental monitoring. Sec-tion III presents details about rain intensity modeling. Then,Section IV describes the methodology we adopted in thispaper. Section V details the simulation framework used forour experiments, while experimental results are presented inSection VI. Finally, the main conclusions are presented inSection VII, along with references to future work.

II. RELATED WORK

Rain intensity measurements are of great interest to differentsectors of our society, since they allow the administration tobetter determine the required actions to take. It also allowsimproving weather predictions, and insurance companies canbetter detect fraud attempts, among other advantages.

In the past, rain intensity measurements relied mostly onfew weather stations deployed by the administration, havinglimited representativeness for most parts of the territory. Inthe past decade, alternative measurement techniques using

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Table I: Classification of precipitation according to intensity.

Intensity Amount per hourWeak ≤ 2 l/m2

Moderate ]2,15] l/m2

Strong ]15,30] l/m2

Very strong ]30,60] l/m2

Torrential > 60 l/m2

satellites, microwave links, and acoustic rain gauges have beenproposed by different authors [4], [5].

More recently, the idea of using moving cars as rainfallmeasurement devices was proposed [6]. In particular, theseauthors propose using the wiper speed (W) as an indication ofrainfall rate (R) by applying a hypothetical W-R relationshipwith an assumption about the rainfall rate estimation error.Alternatively, authors such as Kurihata et al. [7] proposeusing in-vehicle camera images to judge rainy weather bydetecting raindrops on the windshield; similarly, Gormer etal. [8] propose a novel rain sensing concept based on anautomotive in-vehicle camera where the bottom part of theimage sensor is used to detect raindrops, while the upper partis still usable for other vision-based applications.

In [3] the authors develop and analyze the relationshipbetween sensor readings and rainfall intensity based on lab-oratory experiments. They use cars as moving rain gauges,relying on windshield wipers or optical sensors as measure-ment devices. An interesting approach that extends the weathermonitoring concept is proposed in [1], where the authorsuse cars equipped with different sensors for environmentmonitoring instead.

In this paper we take on these ideas and study, usinga realistic simulation model, the actual prediction accuracyachieved when relying on vehicles as rain gauges to determinethe rain intensity throughout a target geographical area.

III. RAINFALL CHARACTERIZATION AND MODELING

Assessing rainfall intensity in real time is of interest to bothdrivers and authorities, as well as the public in general, sincehigh rain intensities can endanger lives and cause significantdamage.

To determine if our proposed approach is able to detect highrain intensities with good accuracy, our goal in this section is tocreate a realistic rain pattern model that recreates worst-caseconditions, that is, torrential rain concentrated in a specificgeographical area during a short time period.

Available precipitation measurements typically rely on de-vices known as gauges or pluviographs, the latter being usedmainly when intense rainfall occurs during a short period.The Spanish National Agency for Meteorology (AEMET [9])defines five categories (see Table I) to characterize the differentrainfall intensities, along with the corresponding amount ofprecipitation per hour.

In addition, for rain patterns to be realistic, we must alsotake into account the following AEMET criteria:

Figure 1: Target region of the city of Valencia.

Table II: Rain intensity vs distance from peak area undertorrential rain conditions (10-minute period).

Distance (m) Intensity (l/m2)0.0 10.0292 7.5

491.5 5.0611.5 4.0631.5 3.0691.0 2.5770.5 2.0920.5 1.5

3430.5 0.53890.5 0.1

• The rain is neither perfectly constant nor infinitely in-tense, presenting progressive maximum and minimumvalues, and following a regular distribution.

• The most interesting phenomena are those having acentered distribution with a single maximum, similar toa Gaussian distribution.

• Officially, rain intensity is classified according to theamount recorded in one hour; nevertheless, records canbe variable, and usually the total rainfall registered isdistributed in less than one hour.

Taking this information as reference, in the next section wedetail the proposed rain pattern model.

A. Rain pattern modeling

Our analysis will focus on a target area that includes themain part of the city of Valencia, specifically an area sized4×3 Km, in a 10-minute period of short but highly intenserainfalls. For this endeavor it is first necessary to specify rainthresholds that follow a realistic pattern in terms of time ofprecipitation, distance, and intensity.

Considering the restrictions defined previously, Table IIshows real data taken by AEMET for rainfall samples lasting10 minutes, and during which the distance in meters and theintensity measured in liters per square meter (l/m2) were

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0

2

4

6

8

10

-4000 -3000 -2000 -1000 0 1000 2000 3000 4000

Rain

Inte

nsity (

l/m

^2)

Distance (m)

Figure 2: Gaussian fit for rain intensity vs. distance.

Table III: Scenario limits and hotspot coordinates.

Position Latitude LongitudeLower-left limit 39.464914 -0.406639Upper-right limit 39.491919 -0.361717

Hotspot 1 39.486500 -0.396678Hotspot 2 39.488363 -0.389219Hotspot 3 39.475907 -0.388786Hotspot 4 39.488981 -0.380444Hotspot 5 39.477729 -0.380776Hotspot 6 39.483625 -0.374916

recorded, having 10 l/m2 as the maximum intensity value.Through regression, we then obtained the gaussian model forrain intensity, as shown in figure 2.

We can see that the obtained regression function showsvalues falling sharply as we move away from the centralposition, being rain intensity nearly negligible for distancesto the center greater than 1 km.

B. Rain pattern generation

Taking the gaussian-shaped model of rain intensity derivedin the previous section, we extended it to the 2D gaussian casein order to make it feasible to deploy in our target area:

f(x, y) =a√

2πs2· e

(x−x0)2

2s2+

(y−y0)2

2s2 (1)

where s represents the standard deviation, and (x0, y0)correspond to the source coordinates of each target location.

We then proceeded to define a set of hotspots (see table III)in order to create a horseshoe-shaped rain pattern of significantdimensions within the target area defined previously (see figure3), thereby avoiding simpler patterns that would be too easyto predict.

As a result of combining the different gaussian curves,each of them centered on one of the hotspots defined, weobtained the pattern shown in figure 4, which we will useas reference for our experiments. Notice that the light-yellowregion at the core of the horseshoe shape represents a veryhigh rain intensity, while near the scenario edges (in red) rain

Mapa Valencia

Borders and hotspots

StudyArea

Punto Inicial

Punto Final

Hotspot 1

Hotspot 2

Hotspot 3

Hotspot 4

Hotspot 5

Hotspot 6

(4 x 3) Km

Figure 3: Hotspots experiencing peak rain intensity values.

0 800 1600 2400 3200 4000

060

012

0018

0024

0030

00

X coord (m)

Y c

oord

(m)

Figure 4: Generated rain pattern.

intensity is nearly zero. Such significant differences wherechosen on purpose in order to create a challenging scenarioto test our solution. In particular, the challenge is for theadministrator to recreate this pattern based on information ofthe rain sensors, which are deployed in the different vehiclesactively participating in the system.

IV. METHODOLOGY FOR RAIN SENSING USING VEHICLES

In this section we describe the methodology followed inorder to monitor rain intensity using vehicles as sensors.To this aim we followed the strategy proposed by Urra etal. [1], who provide a five-step process for general-purposemonitoring using vehicular networks. These steps are thefollowing:

Step 1: Determining the goal of the monitoring task.Step 2: Allocating vehicles for the monitoring.Step 3: Collecting the data of interest.Step 4: Routing the collected data.Step 5: Processing the data retrieved.

Following these steps, we first determined that the goal ofthe monitoring task is rainfall intensity sensing. Regarding

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Reply

Request

Reply

Request

Figure 5: Simulation Framework.

the second step, the data sources will be sensors installed onselected vehicles, which will measure the rainfall intensity.Such vehicles, when traveling around the city, will be usedin the third step to collect the rainfall data. The acquired datawill be sent to a predefined storage server either directly usingcellular technology, or indirectly by relying on on-board short-range wireless devices to transfer the data to other nearby carsuntil a Roadside Unit is reached. The combination of data fromdifferent sources can improve the estimation of real rainfall [3].Finally, the collected information will be gathered and storedfor analysis and processing.

In the section that follows we adopt this methodology forperforming a simulation-based analysis.

V. OVERVIEW OF THE SIMULATION FRAMEWORK

The simulation study was carried out in the OMNeT++simulator [10], which is an modular discrete-event simulationframework. To meet the requirements of our study, the simu-lator should be able to model both vehicle mobility and rainsensing in an independent, although integrated manner.

To simulate vehicular mobility we will use a micro-scopic traffic simulator called ”Simulation of Urban Mobility”(SUMO) [11]. This tool is well known and widely used byexperts in the transportation field, especially in the vehicularnetworking field.

Initially, a configuration file including details about the roadnetwork and vehicle mobility is provided to SUMO. Once asimulation is launched, both OMNeT++ and SUMO simulatorswill run in parallel, being connected via a TCP socket; theprotocol used for this communication has been standardizedas the Traffic Control Interface (TraCI) [12]. It will provide usaccess to the running road traffic simulation using OMNeT++,and it allows retrieving values concerning simulated objects(e.g. get current position) and control their behavior (e.g. stopvehicle).

Regarding the integration of rain sensing within OMNeT++,such possibility is not available by default. Thus, we extendedthe simulator in order to achieve full integration in a seamlessand transparent manner. As illustrated in figure 5, the generalapproach was similar to the one taken by SUMO, followingthe request/reply paradigm between independent entities. So,each vehicle is endowed with a RainSensor module, whichperiodically updates the rain intensity perceived at the currentposition. The procedure is the following:

1) A timer is set for each vehicle to periodically retrievethe rain intensity value.

2) When the timer is triggered, the RainSensor modulerequests the current vehicle position to SUMO using theTraCI component.

3) Once the vehicle position is known, the RainSensormodule requests the RainManager module for the rainintensity status at the current position.

4) The rain intensity value is returned, and then relayed toa central node which stores it in a trace file, along withthe vehicle position and timestamp for later analysis.

Once the simulation is completed, we rely on the R tool [13]to perform a statistical analysis. In particular, we use it tovisualize the obtained data in a map, as well as to performgeospatial prediction using a statistical procedure known askriging [14], thereby obtaining a heat map for rain intensityfor the whole target area [15], [16]. This way we are able tocompare the reference rain map that was generated (see figure4) against the one reconstructed via the kriging process.

VI. EXPERIMENTAL RESULTS

The purpose of this section is to determine the validity andeffectiveness of the proposed approach. In particular, we wantto find out whether a reduced number of vehicles equippedwith appropriated rain sensors are capable of estimating therainfall intensity distribution throughout the target area with alow prediction error.

To accomplish our goal, we will rely on simulation usingthe framework defined in the previous section, and measurethe accuracy of the rainfall intensity estimation in a specificarea. The results presented below are obtained with 50 or 250vehicles participating in the system, which is equivalent to adensity of rain sensors of merely 4.17 and 20.8 devices persquared kilometer, respectively.

In figure 6 we show the combination of all vehicle traces,highlighting in color the sensed value at each position. Wecan clearly see that the differences between having 50 or 250sensing vehicles are quite noticeable, being the data providedby the former already capable of providing a preliminaryinsight of the rain intensity map throughout the target area.This occurs since route dispersion promotes that vehiclesgather samples from more geographical locations, therebyenriching the overall map.

Based on the data gathered by the different vehicles in eachscenario, we then proceeded to perform geospatial predictionusing kriging. The resulting color map for the predicted rainintensity in both scenarios is presented in figure 7, alongwith the standard error associated to this procedure. It quicklybecomes evident that, in both cases, the horseshoe-shapedpattern used as reference (see figure 4) can clearly be devised.However, for the 50 vehicles scenario, the accuracy of thecontour, and also the quality of the estimations, are poorer.In particular, in the bottom part of the figure, a high rainintensity area can be devised, when in fact it is inexistent.This means that the prediction accuracy for the 50 vehiclesscenario is worse compared to having 250 vehicles, especiallyfor predictions near the edges of the scenario.

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(a) 50 source vehicles.

(b) 250 source vehicles.

Figure 6: Obtained rain intensity samples with different num-ber of sources. Dark red represents very high rain intensity,and dark blue very low rain intensity.

To complete our study, we calculated the actual differencesbetween the original rain intensity map (reference) and thepredicted rain intensity map through a metric we calledaverage prediction error (P̄e), which is defined as follows:

P̄e =1

n×m·

n∑i=0

m∑j=0

| referencei,j − predictedi,j |

For this calculation we assume that our target area (bothreference and predicted maps) is split into small elements,creating a matrix sized n × m; notice that the dimensionsof such matrix will determine the desired granularity for theprediction output since a rain intensity value is associated toeach matrix element.

Table IV presents the P̄e values when varying the numberof sensing vehicles from 50 to 250, and the relative sensingerror ε introduced by the rain gauge (assumed negligible in

Table IV: Average prediction error when varying the numberof vehicles and the sensor accuracy (50 samples per minute).

# vehicles, sampling error P̄e (%)50 vehicles, ε = 0% 1.05

50 vehicles, ε = 10% 1.0750 vehicles, ε = 20% 1.15150 vehicles, ε = 0% 0.70

150 vehicles, ε = 10% 0.71150 vehicles, ε = 20% 0.80250 vehicles, ε = 0% 0.62

250 vehicles, ε = 10% 0.63250 vehicles, ε = 20% 0.72

the previous results), that now takes values 0%, 10% and 20%.We find that the impact of the sensing error ε is rather low, asdifferences in the overall estimation error (P̄e) are minimal.Also, as the number of vehicles increases, the impact of ε onthe prediction quality becomes less relevant. This is expectablesince the data fusion procedure associated to the kriging-basedgeospatial prediction process inherently acts as a filter as well.

Overall, we find that, with 250 vehicles (about 20.8 vehiclesper km2), we can already consider that the resulting predictionis acceptable even in the presence of low quality sensors,being the estimation error rather low (<1%). In addition,since we chose a worst-case scenario, introducing a highersensor density in typical scenarios is not expected to introducesubstantial improvements in terms of prediction accuracy.On the contrary, reducing the number of participants below4 devices per squared kilometer will introduce substantialprediction errors, unless the rain pattern is quite simple.

VII. CONCLUSIONS AND FUTURE WORK

The equipment, facilities, and technologies available invehicles have experimented significant improvements in recentyears, with several more in the horizon. Such improvementsallow vehicles to share information among them, which canlead to the development of novel and interesting solutions. Inthis sense, this paper studies the prediction accuracy achievedwhen converting vehicles into rain gauges to determine therain intensity in a specific geographical area. To achieve thisgoal, we first introduced a model for realistic rain patterngeneration, and then, following a standard methodology, weperformed a feasibility analysis of our proposal which, amongothers, required extending the vehicular simulation frameworkprovided by OMNeT++ to provide support for rain sensing.

Based on simulation experiments with different vehicledensities, we found that the proposed solution is effectiveeven when the vehicle density is rather low (about 4 devicesper squared kilometer), achieving a very good accuracy whenincreasing this value to about 20 devices per squared kilometer.Also, we find that the geospatial prediction process is quiterobust to the inaccuracies introduced by sensing elements.

As future work we plan to actually implement the proposedsolution by integrating a rain gauge unit into vehicles, alongwith a smartphone application able to relay sensed data to acloud server.

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Kriging Prediction

−15

−10

−5

0

5

10

Kriging Standard Error

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

(a) 50 source vehicles

Kriging Prediction

−15

−10

−5

0

5

10

Kriging Standard Error

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

(b) 250 source vehicles

Figure 7: Output of the spatial prediction process and associated error. Dark areas represent very high rain intensity/high error,and light areas represents very low rain intensity/low error.

ACKNOWLEDGMENTS

This work was partially supported by the “Ministerio deEconomía y Competitividad, Programa Estatal de Investi-gación, Desarrollo e Innovación Orientada a los Retos dela Sociedad, Proyectos I+D+I 2014”, Spain, under GrantTEC2014-52690-R.

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