1

Smarter Cities with Parked Cars as Roadside UnitsAndre B. Reis, Susana Sargento, and Ozan K. Tonguz

Abstract—Real-time monitoring of traffic density, road con-gestion, public transportation, and parking availability are keyto realizing the vision of a smarter city and, with the adventof vehicular networking technologies such as IEEE 802.11p andWAVE, this information can now be gathered directly from thevehicles in an urban area. To act as a backbone to the network ofmoving vehicles, collecting, aggregating, and disseminating theirinformation, the use of parked cars has been proposed as analternative to costly deployments of fixed Roadside Units.

In this paper, we introduce novel mechanisms for parkingvehicles to self-organize and form efficient vehicular supportnetworks that provide widespread coverage to a city. Thesemechanisms are innovative in their ability to keep the network ofparked cars under continuous optimization, in their multi-criteriadecision process that can be focused on key network performancemetrics, and in their ability to manage the battery usage of eachcar, rotating roadside unit roles between vehicles as required. Wealso present the first comprehensive study of the performance ofsuch an approach, via realistic modeling of mobility, parking,and communication, thorough simulations, and an experimentalverification of concepts that are key to self-organization. Ouranalysis brings strong evidence that parked cars can serve as analternative to fixed roadside units, and organize to form networksthat can support smarter transportation and mobility.

I. INTRODUCTION

AS the world population continues to shift from rural tourban areas, smarter transportation becomes an increas-

ingly necessary part of urban development. With urbanizationcomes a greater need for mobility, both in the form of personalvehicles and of public transportation, and consequently ademand for better transit and parking information. A greaterawareness of available transportation resources is therefore akey element of a Smart City.

The advent of vehicular networking, through the IEEE802.11p and WAVE standards, allows individual vehicles tocommunicate and report their activity, which enables the directmonitoring of transportation resources. Traffic density androad congestion can be measured in real-time [1], [2], [3],parking space availability estimated from cars that arrive anddepart [4], [5], and the location of buses and other public trans-port can be tracked directly and efficiently [6]. The envisionedtraffic efficiency applications assume one such infrastructuredeployment in the form of Roadside Units (RSUs), but theprohibitive costs associated with RSU hardware, installation,and maintenance have severely limited their adoption [7], [8].

A. B. Reis is with the Department of Electrical and Computer Engineering,Carnegie Mellon University, Pittsburgh, PA 15213-3890 USA, and also withthe Universidade de Aveiro, Instituto de Telecomunicacoes, Aveiro 3810-193,Portugal (e-mail: abreis@cmu.edu).

S. Sargento is with the Universidade de Aveiro, Instituto de Telecomunica-coes, Aveiro 3810-193, Portugal (e-mail: susana@ua.pt).

O. K. Tonguz is with the Department of Electrical and Computer Engineer-ing, Carnegie Mellon University, Pittsburgh, PA 15213-3890 USA (e-mail:tonguz@ece.cmu.edu).

Vehicles that park in urban areas can be leveraged to takeon the roles of fixed roadside units, positioning themselvesas an effective alternative to costly deployments of networkinfrastructure. Research shows that parked cars equipped with802.11p technology are able to self-organize and create avehicular support network in an urban area, replacing oraugmenting existing roadside units [9], [10]. Such a vehicularsupport network, created from parked cars, is able to serve animportant role in achieving the vision of a Smarter City: theubiquity of parked cars in the urban area allows them to formwidespread mesh networks, while their On-Board Unit (OBU)hardware lets them be aware of nearby vehicle activity andmonitor the city’s transportation resources.

This paper introduces a new approach for parked car self-organization. This approach advances existing techniques inseveral ways: the proposed mechanisms optimize networks ofparked cars beyond their initial grouping, which leads to amore efficient selection of cars to act as RSUs; a multi-criteriadecision making process acts directly on key metrics of signalstrength, RSU saturation, and network coverage, allowing fora precise control of the resulting support network; and carbattery usage is factored into the decision process, with RSUroles being rotated among parked cars, ensuring a controlleduse of each vehicle’s battery resources.

The work presented in this paper also includes the firstcomprehensive evaluation of the concept of leveraging parkedcars as effective roadside unit replacements. Thorough sim-ulation studies analyze how parked cars initially organize toform new networks, and how those networks behave in theirsteady-state. We shed light on the number of parked cars thatneed to be recruited to provide urban coverage in variousscenarios, and analyze the quality and strength of the resultingnetworks in detail. Upper and lower bounds are establishedfor the balance between better coverage strength and fewerroadside units, providing a unique frame of reference to judgethe performance of self-organizing approaches, and variousiterations on the design of the newly introduced decisionprocesses are all seen to operate near the most optimal bounds.We bring in detailed, realistic models of the distribution ofparking events and their respective duration [11], and validatethe proposed mechanisms against them. Finally, this workpresents the first empirical study of the process through whichvehicles learn their own coverage map by overhearing beaconsfrom other moving cars, an essential element to the self-organization process, and prove its validity. With this, weprovide strong confirmation that self-organizing approachesare suitable for the creation of vehicular support networksfrom parked cars, and provide both an efficient set of newdecision algorithms to do so and a comprehensive simulationplatform to evaluate their performance on.

arX

iv:1

706.

0224

7v1

[cs

.NI]

7 J

un 2

017

2

This work’s main contributions can be summarized asfollows:• A new approach for better parked car self-organization

is introduced. Instead of relying on individual decisions,we give newly parked cars the role of decision makers,allowing them to reconfigure RSUs in their vicinity. Thisensures a continued optimization of the resulting network.

• A multi-criteria decision making solution is formulatedfor the problem of assigning RSU roles to available cars.

• A system to limit the battery power draw on each caris developed, allowing RSU roles to be rotated betweenavailable vehicles as a part of each decision step.

• An extensive study is provided for the performance ofa self-organized approach for parked cars. We show, forthe first time:

– how networks of parked cars come together;– the quality of the support network that is created;– what the best balance between the number of re-

cruited cars and the quality of the network is;– the sensitivity to the frequency of parking events, and

to the radio range of the vehicles’ hardware;– the performance of the network under realistic park-

ing behaviors drawn from empirical data.• An experimental study is shown for the process that

cars employ to learn their own map of coverage, andits resilience is demonstrated.

The remainder of this paper is organized as follows. Sec-tion II gives an overview of the main concepts behind vehicularsupport networks that consist of parked cars, and describesour new approach for self-organization based on local de-cision makers. Section III presents a thorough evaluation ofthe proposed decision mechanisms, their efficiency, and theirperformance in realistic scenarios. An empirical study on thelearning process that newly parked cars use to determine theircoverage is shown in Section IV. Related work is presentedin Section V, and finally, concluding remarks are given inSection VI.

II. IMPROVING DECISIONS FOR SELF-ORGANIZEDNETWORKS OF PARKED CARS

Groups of parked cars that self-organize can become road-side units in a standalone mode, forming a mesh network withpoint-to-point links between them, or as relays to existingfixed RSUs, extending their coverage range. These modes canalso coexist, as is shown in Figure 1. The masses of parkedcars must then work towards self-organization while meeting anumber of goals: providing widespread coverage to the urbanarea; selecting, from large pools of parked vehicles, whichshould take on RSU roles; and managing the battery powerdrain on those cars where the On-Board Unit is kept active.

The work in [10] introduced a number of novel conceptsthat facilitate this self-organization between cars. Specifically,it proposed a cellular division of the urban area, aligning cellboundaries to GPS coordinates (with a 1 GPS second interval).With this cell division as a common reference, parked cars canthen be instructed to listen to Cooperative Awareness Messages(CAMs) from neighboring vehicles, and to build their own

Figure 1. Parked cars can act as relays for existing roadside units, and alsoform mesh networks to extend the area covered by the vehicular supportnetwork.

coverage maps from these beaconing messages, marking cellswhere beacons were received from with the signal strength ofthose messages. The exchange of coverage maps between carsallows decision processes to know the utility of each car tothe network, and optimize using that information.

The mechanisms presented in the following section makeuse of these core concepts. We advance existing work by in-troducing a completely new approach for the decision process,one where newly parked cars are given the authority to makedecisions for their neighbors, assigning and revoking RSUroles to other neighbor RSUs to optimize the local network.Additionally, we formulate the decision problem as a Multiple-Criteria Decision-Making (MCDM) problem that explicitlyevaluates key metrics of the resulting network of parked cars,and factors battery usage data from active RSUs into eachdecision.

A. Local Decision Maker

As a new vehicle parks, it first constructs its own coveragemap by listening to beacons being sent from moving cars,and gathers the coverage maps of its neighboring RSUs [9].Then, it creates a list of candidate roadside unit entities inits 1-hop neighborhood, consisting of itself and other activeparked cars. From this pool, the decision maker lists thepossible combinations obtained from assigning or revokingRSU roles to the entities in the pool, and assigns a score toeach combination. We name a combination of active/inactiveentities a Coverage Solution, and designate it by Sk (where kis the index of the coverage solution).

Once all coverage solutions are ranked, the one with thehighest score is applied to the network. To do this, thenewly parked car, acting as a authoritative decision maker,sends specially crafted messages reassigning RSU roles in itsvicinity. Through this approach, the network of RSUs sees alocal optimization every time a new vehicle parks.

3

B. Ranking Coverage Solutions

A self-organizing approach for parked cars has a numberof core goals: the network of cars should aim to providewidespread coverage to the urban area in all of its locations,whenever possible, and to supply that coverage with strongsignal, so that moving nodes can connect to the RSU networkreliably and with high data rates. It must also aim to minimizethe number of parked cars that take on RSU roles, and the timethose vehicles spend active as RSUs, drawing power from theirrespective batteries.

Because the network is intended to be self-organized, thereis an implicit assumption that a central controller is notavailable, and so decisions must be taken by the parkedvehicles themselves, communicating only between them asneeded. Poor decisions may lead to either (a), more parkedcars being assigned RSU roles than necessary, creating a morecrowded network and wasting battery energy, or (b), an RSUnetwork that is suboptimal in signal strength, area of coverage,or both.

To score a coverage solution, we resort to dimensionlessanalysis: a Weighted Product Model (WPM), where the utilityof each solution is determined by multiplying a series ofattributes, each of which is raised to the power of a coeffi-cient that represents its weight to the solution’s score. Thisapproach eliminates any units of measure, which is ideal toour multi-dimensional decision-making problem. The score ofa coverage solution Sk is, then, a product of attributes aj,k,each weighted by a coefficient wj :

score (Sk) =n∏j=1

(aj,k)wj (1)

Our scoring function integrates the following four attributes:

• Signal Strength (asig, wsig): An average of the best signalstrength available at each cell. It is the main attribute thatpushes towards a city-wide network with strong coverage.

• RSU Saturation (asat, wsat): The counterweight to signalstrength, roadside unit saturation represents the numberof RSUs that provide coverage to a cell. Higher averagesignal strength in the city demands higher RSU saturationand, therefore, a larger number of RSUs.

• Area of Coverage (acov, wcov): The span of the coveragebeing provided by the RSU network. A strong signalstrength does not imply widespread coverage, as it onlyconsiders cells where coverage is provided. This attributecan favor solutions with wider coverage.

• Energy Usage (abat, wbat): A measure of how muchenergy active RSUs in a coverage solution have expended,and how close they are to a maximum threshold. Thisattribute pushes towards coverage solutions that removeaging RSUs from the network, keeping the battery uti-lization of active parked cars under control.

Each of these attributes is calculated on the expectedoutcome of a coverage solution, i.e., on the RSU networkthat would theoretically result if the coverage solution wereapplied, assigning and revoking RSU roles.

C. Constraining the Search Space

For a decision maker to score all coverage solutions thatmay be possible in its neighborhood, it needs to search a spaceof 2N possible combinations, with N being the number ofneighbors plus the decision maker itself. For denser networksof RSUs, the number of coverage solutions in the search spacecan become too unwieldy and computationally expensive.

We adopt a more restrained approach, by restricting thesearch space to coverage solutions that do not revoke morethan two RSU roles in the decision maker’s neighborhood.Because the self-organized RSU network is grown incremen-tally as each new car parks, RSUs seen by a decision maker arethe result of earlier, equally-optimized decisions, so coveragesolutions that revoke higher numbers of roles are unlikely tolead to high decision scores, and it is therefore safe to excludethem from the search space.

Under these constrains, valid coverage solutions will belongto one of three possible categories:

1) No entities are disabled: the candidate parked carbecomes an RSU, and all neighboring RSUs remainactive. A single such solution exists (nCn = 1), whichwe denote as S0. It increases the number of RSUs inthe city by one.

2) One entity is disabled: either the newly parked car ora neighboring RSU. nCn−1 such solutions exist, andthey do not change the number of RSUs in the city.The solution that only disables the newly parked car isa ‘no-action’ solution, leaving the network unchanged.We denote this specific case as SNA.

3) Two entities are disabled: the parked car takes on anRSU role and replaces two existing RSUs; or, both theparked car and a neighbor RSU are disabled. nCn−2such solutions exist, and they decrease the number ofRSUs in the city by one.

With this approach, a decision maker with, e.g., 8 neighborshas to evaluate only 37 solutions of the 256 that were possible.

D. Attribute Models

We now show how to quantify each of the four attributesthat are part of the scoring function. Our aim here has beento keep these models as straightforward as possible, so thatour subsequent analysis can show, with clarity, what eachattribute contributes towards the resulting network of parkedcars. An algorithm to calculate all attributes and produce afinal decision score is shown afterwards, in Section II-E.

1) Signal Strength: Our signal strength attribute is theaverage signal strength that is expected of a given coveragesolution. From the received coverage maps and the vehicle’scoverage map, the signal strength of each cell in the resultingsolution is taken and averaged.

In this work we use a 1-5 classification that correspondslinearly to a Received Signal Strength Indicator (RSSI). Be-cause the decision algorithm is dimensionless, RSSI may alsobe used directly. Other metrics such as signal power (e.g., indBm) or network bandwidth (e.g., in Mbps), should be equallypracticable, but are not evaluated here. For accuracy, only thecells that the decision maker can cover should be considered,

4

since the vehicle acting as decision maker does not have acomplete view of the network beyond these cells.

2) RSU Saturation: The RSU saturation attribute reflectsthe overlap in coverage between active roadside units. For agiven cell in a coverage solution, its saturation value equalsthe number of RSUs that can cover the cell (i.e., the numberof RSUs that a vehicle located on the cell will see). Thismetric reflects the number of RSUs in the area, and also theirarrangement: a poor RSU role assignment where RSUs arestacked too close sees more cells with higher saturation (moreoverlap between the RSUs’ coverages), while a more efficientdistribution gives a more widespread coverage and more cellswith lower saturation.

Saturation is also a measure of redundancy in the RSU net-work, which may be desirable from an availability standpoint:when cells are covered by more than a single RSU (saturationgreater than 2), if a car with the RSU role is removed fromthe network, continued access to the network can be ensuredby other RSUs.

As with the signal strength attribute, our saturation attributeis the average of the roadside unit saturation seen at eachcell of a coverage solution. Average saturation can rangefrom [1,∞[, but in practice, an efficient algorithm will keepmean saturation in a range of [1.44,≈ 5] (see Sections III-Aand III-D).

3) Area of Coverage: The signal strength and RSU sat-uration attributes, on their own, do not ensure a widespreadcoverage by the network of parked cars, which is also a desiredtrait for this network. Consider that, once a base networkis established, new coverage will be available primarily atthe fringes of the covered area, where the signal is weaker,therefore pushing average signal strength down and penalizingcoverage solutions that expand the network’s reach. To makesure that local decisions push towards widespread coverage,we include an attribute that quantifies the area of serviceprovided by each coverage solution.

To compute this attribute, the algorithm begins by countingthe number of covered cells in the S0 coverage solution, whichwe defined earlier as the solution that disables no RSUs and,therefore, has the widest possible coverage area (since all othercoverage solutions involve disabling at least one entity). Thecoverage attribute acov is then defined as the ratio betweena solution’s number of covered cells and the widest possiblecoverage in that decision step (belonging to S0):

cvg(Sn) := ‘number of cells with service in Sn’

acov(Sn) =cvg(Sn)cvg(S0)

(2)

Unlike asig and asat, the area of coverage attribute mustinclude all cells covered by the decision maker and its neigh-boring RSUs. This ensures that new coverage at the edges ofthe existing network is correctly scored.

4) Battery Usage: To compute the battery usage attributeabat, the car acting as the decision maker must first query itsneighbor RSUs for an indicator of their energy expenditure.This request can be sent together with the request for coveragemaps that the decision process requires. Vehicles can havedifferent battery capacities, which are in turn affected by

the age of the battery, so the indicator is best calculatedindividually by each vehicle, and not by the decision maker.

In this work, we use a straightforward model where eachvehicle has pre-specified standard (τm) and maximum (τM )times of activity. The indicator decreases linearly once τmis exceeded, and the parked car relinquishes its RSU roleforcefully at τM :

τn,i := ‘active time of entity ξn,i’

bat(ξn,i) =

{1 τn,i < τm1− τn,i−τm

τM−τm otherwise (3)

This design keeps battery concerns invisible to the decisionprocess, at first, and then allows for RSU roles to be reassignedby decision makers before a hard limit on a vehicle’s batteryresources is reached. The resulting battery attribute of acoverage solution is the averaging of all the neighboringroadside units’ battery indicators:

ξn := ‘set of entities in solution Sn’|ξn| := cardinality of ξnξn,i := ‘active entity i in solution Sn’

abat(Sn) =∑i∈ξn bat(ξn,i)

|ξn|(4)

Coverage solutions that are able to revoke longer-standingRSU roles without affecting the network (e.g., by handing overan older RSU role to a new vehicle in a similar location) willsee higher decision scores. We analyze this attribute in detailin Section III-C.

E. Scoring Algorithm

Pseudocode to score a coverage solution via the WeightedProduct Model shown in Section II-B is given here. Algo-rithm 1, ScoreCoverageSolution, computes the attributes aj,kand weighs them according to their respective weights wj , pro-ducing the final scoring metric, score (Sk). These algorithmsmake use of the decision maker’s coverage map (SCM0), andthe coverage maps from its 1-hop and 2-hop neighborhood(N1,N2). All notation is summarized in Table I.

Table INOTATION REFERENCE

Definition Notation

Matrix indices (row, col.) i, jGeographic indices (lat., lon.) x, ySelf-observed coverage map [SCM] = (scmij)Decision maker’s own SCM SCM0

2-hop neighbor SCMs N2 = {SCM1, SCM2, . . .}Local map of coverage [LMC] = (lmcij)Local map of saturation [LMS] = (lmsij)Coverage solution Sk

Number of covered cells in Sk covSk

SCMs of RSUs in Sk [SCMSk ] = {SCMk1 , SCMk

2 , . . .}Battery indicators of RSUs in Sk [BATSk ] = (batkn)

5

Algorithm 1: ScoreCoverageSolutionData: SCM0, [SCMSk ], [BATSk ], N2, covS0 , wsig, wsat,

wcov, wbat

Result: score (Sk)1 . lmcxy (in an LMC) and lmsxy (in an LMS) are

initialized to 02 asig ← asat ← acov ← abat ← 03 // create signal and saturation maps4 foreach SCMn{SCMSk ,N2} do5 foreach scmn[xy] ∈ SCMn do6 if scmn[xy] > lmcxy then lmcxy ← scmn[xy]

7 if scmn[xy] > 0 then lmsxy ← lmsxy + 18 end9 end

10 // average signal strength to get asig11 foreach lmcxy ∈ LMC do12 if lmcxy > 0 and scmxy ∈ SCM0 > 0 then13 asig ← asig + lmcxy14 end15 end16 asig ← asig / covSCM0

17 // average RSU saturation to get asat18 foreach lmsxy ∈ LMS do19 if lmsxy > 0 and scmxy ∈ SCM0 > 0 then20 asat ← asat + lmsxy21 end22 end23 asat ← asat / covSCM0

24 // compute acov25 acov ← covSk / covS026 // compute abat27 foreach batkn ∈ BATSk do28 abat ← abat + batkn29 end30 abat ← abat / |Sk|31 // produce a decision score32 score (Sk)← asig

wsig · asatwsat · acovwcov · abatwbat

III. EVALUATION OF SELF-ORGANIZED NETWORKS OFPARKED CARS

We now analyze the performance of the decision processshown in Section II. To do so, we run simulations on a1 km2 area in the city of Porto, on a custom-designed platformthat simulates realistic vehicle mobility [12], and features realobstruction masks [13], road topologies [14], and a communi-cation model that follows empirical signal measurements takenin Porto [15]. The custom platform and all of its associateddata have been made available in [16], as part of this research.

The study we present in this section begins by evaluatingseparate attributes of the decision process in controlled sce-narios, to see how each attribute contributes to the resultingRSU network. Then, we create randomized RSU networksand determine best and worst bounds for the self-organizedprocesses, and see how our decision algorithms fare againstthese theoretical limits. We then look into the decisions’sensitivity to the number of cars on the road and the OBU radio

30

40

50

60

70

80

90

0 1000 2000 3000 4000 5000 6000 7000

Active R

oadsid

e U

nits

Simulation Time [s]

wsat

0.400.300.200.100.05

0.6

0.7

0.8

0.9

1

0 1000 2000 3000 4000 5000 6000 7000

% C

ity C

overe

d

Simulation Time [s]

wsat

0.400.300.200.100.05

Figure 2. Parked cars with active RSU roles (top) and percentage of theurban area where RSU service is made available (bottom), through the courseof 2-hour simulations, as the weight controlling the RSU saturation attributeis varied. Data sampled in 1-second intervals.

range, and conclude with day-long simulations that integraterealistic models of parking activity and duration.

A. Balancing Roadside Unit Saturation

Signal strength and roadside unit saturation are the two coreattributes of our decision process, as they balance one another:stronger mean signal can be reached by assigning new RSUroles in underserved areas, which in turn increases the RSUsaturation in the network.

We begin our study by looking at a decision process with theasig and asat attributes alone. We ran sets of 2-hour simulationswhere the 1 km2 urban area saw an average of 55 movingvehicles in circulation, entering the city at a rate of 0.5 vehiclesper second. The weight of the signal strength, wsig, remainedconstant at 1.0, while the weight of the saturation attribute,wsat, was adjusted.

The evolution of the number of active RSUs and the percent-age of the city covered by those RSUs is plotted in Figure 2,with samples taken in 1-second intervals. Beginning with anetwork devoid of RSUs, the transient state of the networklasted for approximately 1060 seconds (≈17 min.) from themoment the first vehicle parked until the city’s coverage areastabilized. Both figures show that the network of parked carsenters a steady state, and these decision mechanisms keep thenetwork stable in terms of RSUs and coverage. The samebehavior was seen in the mean signal strength and mean RSUsaturation (not shown).

We present a steady state analysis of the network in Table II,as the weight wsat that controls the RSU saturation attribute isincreased. Both attributes behave as expected: higher weightson wsat push towards fewer active RSUs and, consequently,a lower mean signal strength (as the signal strength attribute,

6

asig, is de-prioritized in favor of asat). The metrics’ standarddeviations become tighter depending on which attribute isleading the decision process, which is desirable.

We continued to push wsat beyond 0.4, but no meaningfulchanges were observed in the network. The best (lowest)RSU saturation that was observed was of 1.4 RSUs percell. This self-organized process is unable to push for lowersaturation levels (ideally, towards the minimum of 1.0, a singleRSU per cell), for the understandable reason that it requirescommunication between RSUs in order to function. For a pairof RSUs to communicate with one another, their coveragesmust necessarily overlap – therefore, RSU saturation in thatoverlap must exceed 1.0.

Increasing wsat pushes the saturation of the network down,decreasing the number of RSUs, while also increasing theefficiency in terms of urban area covered per RSU. The1 km2 urban area in Porto where we run our simulationshas 650 usable cells (i.e., where vehicles can move through),which corresponds to a 468 000 m2 (0.47 km2) area that theparked cars can provide coverage to. At the lowest wsat, theaverage area covered per RSU was of 5 600 m2, while at thehighest wsat, each RSU was able to cover 7 200 m2, a 28%increase. We observed a strong inverse correlation betweenRSU saturation and urban area covered per RSU (at a linearcorrelation coefficient of −0.995).

B. Expanding Network Coverage

The previous analysis showed that restricting the number ofRSUs in the city causes the undesired side effect of reducingthe citywide coverage that the RSU network is able to provide.While this is expected (coverage and RSU count are inherentlylinked), a scoring algorithm must be able to optimize thenetwork towards providing a more widespread coverage, aswell. We now see how the inclusion of a coverage attributeinto the decision process helps achieve this goal.

We repeated the 2-hour simulation sets, fixing wsat = 0.2 toserve as a baseline from the previous analysis, while increasingthe weight wcov of the coverage attribute. Figure 3 shows howthe percentage of the city that is covered evolves throughoutthe simulation, for the different attribute weights. The data

Table IINETWORK STEADY STATE VARYING A SATURATION ATTRIBUTE

wsatSignal Distribution

2 3 4 5CityCov.

SignalStr.

RSUSat.

ActiveRSUs

0.05 94% 4.51±0.86

2.62±1.11

79

0.10 92% 4.38±0.93

2.21±0.95

69

0.20 83% 4.05±1.07

1.64±0.72

55

0.30 77% 3.90±1.12

1.48±0.65

50

0.40 75% 3.86±1.13

1.45±0.63

49

wsig = 1.0

0.6

0.7

0.8

0.9

1

0 1000 2000 3000 4000 5000 6000 7000

% C

ity C

overe

d

Simulation Time [s]

wcov

1.000.500.300.100.00

Figure 3. Percentage of the urban area covered by the self-organized RSUnetwork, through the course of 2-hour simulations, as the weight controllingthe coverage attribute is varied. Data sampled in 1-second intervals.

show that the coverage attribute pushes the RSU network’stotal coverage to high levels, and improves the stability of thatcoverage. The duration of the transient state does not appearto change through different attribute sets, as well.

We provide an analysis of the network’s steady state inTable III. The data show that higher wcov values lead to amore widespread coverage of the network with fewer RSUsthan in our initial analysis. To push towards better coverage,the decision process does recruit additional RSUs, increasingboth signal strength and RSU saturation as a result. However,it does so at a more optimal distribution of RSUs (more areacovered per RSU) than before. To illustrate this fact, Table IVcompares two parameter sets that recruit similar numbers ofRSUs and achieve similar mean signal strength in the city,the first set with asig and asat attributes only, and the secondwith the acov attribute included in the scoring algorithm. Thiscomparison shows that the inclusion of acov increased citywidecoverage by 5% and reduced RSU saturation by 8%, at noincrease to the number of active RSUs or cost to the meansignal strength that is being provided.

With this analysis, we showed that a coverage attributeimproves the self-organization of the parked cars, ensuring notonly a higher citywide coverage but also a better distribution ofRSU roles, with lower RSU saturation and higher area coveredper RSU.

C. Managing Battery Utilization

The fourth and final attribute of our decision scoring algo-rithm concerns the time a parked car spends with its DSRCelectronics activated, performing RSU roles, and drawingpower from the car’s battery. A vehicle’s battery, when itsengine is off, is a finite resource that must be managedcorrectly. Not only must the car’s energy budget be sharedwith electrical systems that are active when the car parks (e.g.,alarm systems, and keyless entry systems), but failing to doso has the serious consequence of leaving the driver stranded,should the battery be too drained for engine re-ignition.

By including the battery utilization of active RSUs in thescoring algorithm, we aim to drive the decision makers towardscoverage solutions that replace aging RSUs with newly parkedcars, while at the same time attempting to preserve the existingstructure of the RSU network.

7

To study the efficacy of this attribute, we begin by settinga hard threshold of 1 hour on the maximum time a parkedvehicle can hold RSU roles for. Active parked cars thatexceed this threshold are forcefully removed from the RSUnetwork. We then set the τ parameters in Equation (3) to beginpenalizing RSUs once their active time exceeds 30 minutes(τm = 1800, τM = 3600). With this configuration, RSUbattery life does not factor into the decision process until30 minutes of activity have elapsed, allowing for decisions tofocus exclusively on the quality of the network as long whilethe parked cars’ activity times are within expected values.Empirical data provided in [10] showed that an OBU operatingfor ≈6.5 hours consumes approximately 4.2% of a typical carbattery. We opt for a more conservative limit in this evaluation:30 minutes to 1 hour with an active OBU would result in abattery usage of 0.32%− 0.64%.

The figures in Table V show the distribution of the lifetimeof RSU roles in the network during 8-hour simulations,together with the network’s metrics after the transient state,as we increase the weight wbat of the battery attribute.

When abat is not in play (wbat = 0), we see most RSUsbeing disabled at our designated 1-hour threshold. A noticeablenumber of RSU roles are also revoked shortly after theyare assigned, which can be attributed to the initial set ofroles being rotated out as newly parked cars in more optimallocations become available. As wbat is increased, the RSUsthat were hitting the 1-hour threshold begin to be replacedinstead by decisions from decision makers in the city, withhigher weights leading to earlier role revocation, pushing the

Table IIINETWORK STEADY STATE VARYING A COVERAGE ATTRIBUTE

wcovSignal Distribution

2 3 4 5CityCov.

SignalStr.

RSUSat.

ActiveRSUs

0.00 83% 4.05±1.07

1.64±0.73

55

0.10 92% 4.18±1.00

1.77±0.77

60

0.30 96% 4.30±0.94

1.94±0.82

65

0.50 97% 4.36±0.90

2.03±0.84

68

1.00 98% 4.43±0.87

2.19±0.85

72

wsig = 1.0, wsat = 0.2

Table IVPARAMETER SET COMPARISON IN STEADY STATE

wsatwcov

Signal Distribution2 3 4 5

CityCov.

SignalStr.

RSUSat.

ActiveRSUs

0.100.00

92% 4.38±0.93

2.21±0.95

69

0.200.50

97% 4.36±0.90

2.03±0.84

68

wsig = 1.0

lifetime of RSUs closer towards τm.The steady-state analysis of various city metrics in Table V

indicates that a well-designed attribute can rotate RSU roles ef-ficiently, with a negligible impact to the existing self-organizedRSU network. Mean signal strength, RSU saturation, citycoverage, and number of active RSUs all deviated less than3% as wbat was increased, relative to the baseline where thebattery attribute was not included. This important result showsthe efficacy of our proposed algorithm in the management ofa parked car’s battery utilization as it takes on RSU tasks.

Finally, we note that a small percentage of RSU roles stillexceed the 1-hour threshold and have to be forcefully revoked,even at the highest wbat weights we evaluated. We hypothesizethat these are RSUs in critical locations where not enough newparking events occur for a decision maker to be able to find anadequate replacement. Increasing the time allotted for an RSUrole to be replaced (τM − τm) may mitigate this behavior.

D. Decision Performance

An essential measure of the quality of a self-organizednetwork of parked cars is its ratio of signal strength to RSUsaturation. For a certain mean signal quality that is achievedin a given area, there is an optimal and a sub-optimal numberand placement of RSUs that may lead to it. We study thisratio in our simulation setup by filling the network withparked vehicles, and assigning RSU roles to those vehiclesin a random manner. For each random attribution of roles, wethen measure the resulting signal strength and RSU saturationin the network. The data in Figure 4 show 500.000 randomassignments, on top of which we overlay data of the citywidemetrics that were collected during the simulations presentedin the previous three sections.

Relatively clean upper and lower bounds can be observedfrom the sets of random attributions. These bounds are animportant finding: for self-organized networks of cars, theyreveal that the ratio between the number of recruited cars andthe strength of the coverage being provided will be bounded.Here, the lower bound represents the most optimal placementof RSUs for each observation of signal strength, while theopposite is true for the upper bound. This gives us an important

Table VROADSIDE UNIT LIFETIME ANALYSIS

wbatRSU Lifetime

densityCityCov.

SignalStr.

RSUSat.

Act.RSUs

0.00 95% 4.24±0.98

2.09±0.85

68

0.30 96% 4.29±0.97

2.13±0.84

70

0.50 96% 4.28±0.95

2.12±0.86

70

1.00 95% 4.27±0.96

2.10±0.85

69

1.50 95% 4.26±0.97

2.10±0.84

69

Lifetime histograms span 3600 seconds. wsig = 1.0, wsat = 0.2, wcov = 0.3

8

Figure 4. Signal strength and RSU saturation metrics sampled in 1-secondintervals from the simulations in Sections III-A, III-B and III-C.

frame of reference to analyze the performance of any decisionmaking process. The data show that an increasing effort isrequired to push the network to higher signal strengths. Forexample, improving mean signal from 2.0 to 4.0 is possiblewith, on average, one extra RSU, however the same effortis required to move the mean signal from 4.0 to 4.5, andmore than double that effort will be needed to go from 4.5 to4.9. This assumes an optimal assignment of RSU roles, whichmight not always be possible.

The overlaid data from the decision mechanisms demon-strate that these operate very close to the lower signal-to-saturation bound revealed by the random role assignmentdata. The scoring algorithm that included a coverage attribute,in Section III-B, operates closer to this lower bound thanthe initial 2-attribute algorithm, which matches our earlierobservation that this attribute may lead to a more optimaldistribution of RSU roles. The introduction of an RSU life-time threshold and the inclusion of a battery attribute, inSection III-C, sees the data samples pushed away from thisideal lower bound to a small degree, which is an expectedconsequence from the forceful revocation of RSU roles fromparked cars that have been active for some time. In all threecases, regardless, the decision algorithms place the resultingRSU network in a very optimal region.

These important results show that our decision mechanismsare operating near the best observed efficiency for a networkcomprised of parked cars, achieving various levels of signalstrength at the smallest possible RSU saturation and, conse-quently, the lowest number of RSUs at their most optimalarrangement.

E. Sensitivity to Vehicle Density

The simulation studies presented so far saw an averagenumber of 55 moving vehicles per km2 on the road, whichmatches a medium-low density scenario. We now analyzeidentical scenarios where we reduce the number of movingvehicles, to find out how the decision mechanisms respond tothe lower vehicle density. This density affects primarily the

30

50

70

Active R

oadsid

e U

nits

vehicles55352520

3.8

4.0

4.2

4.4

Mean S

ignal S

trength

vehicles55352520

1.2

1.6

2.0

2.4

Mean R

SU

Satu

ration

vehicles55352520

0.7

0.8

0.9

0 1000 2000 3000 4000 5000 6000 7000

% C

ity C

overe

d

Simulation Time [s]

vehicles55352520

Figure 5. Evolution of various network metrics throughout 2-hour simulations,as the density of vehicles per km2 is altered. Scoring algorithm weights setto wsig = 1, wsat = 0.2, wcov = 0.3, and wbat = 0. Data sampled in1-second intervals.

time parked cars take to build their coverage maps, and thefrequency of parking events.

The evolution of the number of active RSUs, mean signalstrength, mean RSU saturation, and total city coverage can beseen in Figure 5. Four densities of vehicles were considered:55, 35, 25, and 20 vehicles per km2, which range frommedium- through low-density scenarios.

The data in these figures show that only the duration ofthe network’s transient state is affected by the decrease inthe number of road vehicles, with all four metrics eventuallyconverging to a similar steady state. This indicates that thedecision mechanisms are robust and should operate equallywell in the face of varying vehicle densities, as is expectedfrom real life scenarios. A small 2% decrease can be seenin the steady state citywide coverage, which could indicatethat the lowest-density scenarios have insufficient new parkingevents for the algorithm to completely optimize the network.

F. Sensitivity to Wireless Radio Range

We now provide an analysis of the decision mechanisms’sensitivity to the radio range of the OBUs used in the vehicles.Figure 6 shows various network metrics in the network’ssteady state as a multiplier is applied to our propagation mod-els, scaling the effective range of the vehicles’ DSRC radios.

9

30

35

40

45

50

55

60

65

70

75

1.00 1.25

1.50 1.75

2.00

Active R

oadsid

e U

nits

Range Multiplier

CI 99%

4.04.14.24.3

Sign

al S

tr.

1.92.02.12.2

Satu

ratio

n.98

.99

1.0

1.00 1.25

1.50 1.75

2.00

Cov

erag

e

Range Multiplier

CI 99%

Figure 6. Steady state averages of various network metrics of 2-hoursimulations, as a multiplier is applied to the radio range of vehicles and parkedcars. Scoring algorithm weights set to wsig = 1, wsat = 0.3, wcov = 0.5,and wbat = 0. Confidence intervals stated in the 99% level.

The data indicate that the number of RSU roles assigned bythe self-organizing mechanisms decreases almost linearly withthe corresponding increase in radio range: doubling the radiorange halves the number of RSUs, from≈ 69.1 to≈ 33.6. Thisis an interesting result due to the fact that the total area thata vehicle’s radio can reach should increase quadratically withthe radio range (area = πr2), assuming perfect conditionsfor propagation. In an urban environment, however, vehicles’ssignals are constrained by the road layout, in effect beingtunneled by surrounding buildings. We hypothesize this tobe the reason behind the near-linear relation that is observed,instead of the quadratic one that one might expect.

Besides the number of active RSUs, both signal strength andRSU saturation remain relatively constant, despite the changein radio range, indicating that the algorithms are resilient tothis network aspect. In terms of total coverage being provided,the range increase also grew this metric by 2%, to a near-complete coverage level.

G. Realistic Parking

We conclude our simulation work with a look at theself-organizing mechanisms operating under realistic parkingconditions. In order to do so, we integrated the realistic parkingmodels presented in [11], which include a distribution ofvehicle arrivals (and subsequent parking) throughout the day,and a comprehensive hour-by-hour stochastic model of thetime these same vehicles remain parked. The data from whole-day simulations performed with accurate parking models ispresented in Figure 7. Parking events at two densities (4000and 2000 vehicles parked over 24 hours) are distributedthroughout the day following the models from [11].

In realistic scenarios, decision mechanisms face two newissues. The first is a possible shortage of newly parked cars,which constrains the assignment of RSU roles to vehicles thatmay not always be at locations optimal for an RSU network.The second concerns the vehicles’s parking durations: evi-dently, cars parking for only a short time make for poor RSUs,

Park

ing

Pro

b. D

ensity

Parking DurationOver 3 hours

Under 3 hours

10

20

30

40

50

Active R

SU

s

4000 2000

3.7

3.9

4.1

4.3

4.5

Sig

nal S

trength

4000 2000

1.3

1.6

1.9

2.2

2.5

RS

U S

atu

ration

4000 2000

0.2

0.4

0.6

0.8

3 A.M. 6 A.M. 9 A.M. 12 P.M. 3 P.M. 6 P.M. 9 P.M.

% C

ity C

overe

d

Day Time

4000 2000

Figure 7. Evolution of various network metrics throughout whole-daysimulations with realistic parking behavior. The first plot shows a simplifiedview of how parking events are distributed, with long-term and short-termparking behaviors split up. The remaining plots compare two densities ofparking events: 4000 and 2000 vehicles parked (over 24 hours). Scoringalgorithm weights set to wsig = 1, wsat = 0.2, wcov = 0.3, and wbat = 0.

and this parking duration is not known beforehand; but a morepernicious effect occurs when a car parking for a short timemakes the decision to disable a nearby car that is parked for alonger time. Without knowledge of the time each vehicle willspend parked, or a means to recruit previously-disabled RSUs,this side effect is difficult to mitigate.

Despite these issues, the data in Figure 7 show that, underrealistic parking conditions, a self-organized network of parkedcars is able to provide and maintain widespread coverage of theurban area throughout the day, at adequate levels of coveragestrength, while assigning roles to only a fraction of the vehiclesthat park throughout the day.

Practical applications for parked cars may benefit fromattempting to predict the parking duration for each vehicle,prioritizing those more likely to remain parked in RSU roleassignment. As an example, the models in [11] indicate thatlong-term parking occurs primarily in the early morning hours,so vehicles that park in these hours may be more suitablecandidates for RSU roles.

10

146 325

146 326

146 327

146 328

146 329

146 330

146 324

146 323

146 322

146 331

146 332

146 333

-31132

-31122

-31123

-31121

-31120

-31119

-31124

-31125

-31126

-31127

-31128

-31129

-31130

-31131

-31111

-31118

-31117

-31116

-31115

-31114

-31113

-31112

Higher RSSI

Parked Car

Figure 8. Histograms of signal strength measurements made by a parked car (in blue) of beacons received from other vehicles that were traveling throughneighboring cells. Each histogram shows the distribution of the RSSI of all beacons overheard from the cell it is plotted in. The parked car was positionednear an intersection on cell (-31 121, 146 326).

IV. INFERRING COVERAGE MAPS FROM RECEIVEDBEACONS

In this section we present an empirical study that replicatesthe learning process a newly parked car goes through in orderto determine its own coverage map. A primary concern of anyself-organizing approach that relies on a parked car’s abilityto determine its own coverage map so that informed decisionscan be made, is the reliability of the process that creates thecar’s coverage map. Radio signals can vary greatly in an urbanenvironment, due to the significant number of obstructionsthat cause the signal to fade. Wave reflections and cornerdiffraction also contribute to the received signal in an urbanenvironment.

We performed an experimental study of the process ofcreating a coverage map by a parked vehicle. For this study,we parked a DSRC-enabled vehicle and instructed it to collectbeacons being broadcast from neighboring vehicles. Thesebeacons contain the GPS coordinates of the transmittingvehicles, and the parked car can determine the signal strength(RSSI) of each received beacon. We then drove other vehiclesequipped with OBUs in the neighboring roads of the parkedcar. This experiment was located in the city of Aveiro, Por-tugal, in an urban area with a variety of small residences,low-rise apartments, and commercial buildings.

From the beacon data that was recorded by the parkedcar, the car then grouped measurements by their geographiccell, and computed the average signal strength and standarddeviation of the measurements belonging to each cell. We then

plotted histograms of signal strength to better understand itsdistribution. These can be seen in Figure 8, overlaid on top ofa city map.

These histograms show that vehicle-to-vehicle signalstrength in an urban vehicular network is well-behaved overshort distances. This suggests that a self-organizing approachcan depend on coverage maps that were learned by parkedcars (by observing beacons), and that the signal strength valuesestimated for each cell will be representative of the strengthof coverage that can be provided to that cell by the listeningvehicle. On most cells, the spread of the signal strength aroundthe mean is relatively small, particularly if one considers howdynamic the environment can be. Our OBUs report RSSI ina range of 1 to 50, and of the 75 cells from which beaconswere received, 88% of the cells showed a standard deviationunder 5.0.

The nature of a cell-based coverage maps does implythat a level of abstraction is applied to the city map and,consequently, there may be situations where a deep fade occurswithin a cell, and the average received power from beaconsfails to reflect the coverage quality on that cell. In thesesituations, a bimodal distribution can often be seen.

V. RELATED WORK

Monitoring and collecting data on vehicular traffic, roadcongestion, and trip patterns are natural uses for DSRCtechnology, where information can be pulled directly fromDSRC-equipped vehicles, obviating the need for more com-plex approaches. Research in this field has suggested the

11

use of vehicles [1], buses [2], and infrastructure-supportedVANETS [3] as viable candidates for gathering such data.The location and tracking of parking space availability isanother possible use of DSRC technology, and proposals fordetecting and distributing this type of data can be foundin [4], [5]. For public transportation, the work in [6] proposesthe use of DSRC to capture real-time information on thelocation of city buses, as well as the traffic conditions thatsurround the buses’ routes. Vehicular networks have also beenshown to be effective collectors and disseminators of sensordata [17], which are crucial for realizing the vision for aSmart City. In certain conditions, the vehicles themselvescan also become sensors [18], performing street imaging,temperature and weather monitoring, or license plate recog-nition. A common theme to this body of work is that eitherinfrastructure is required to gather and distribute the requireddata, or opportunistic, delay-tolerant mechanisms are resortedto in order to improve the probability of data collection.

The concept of leveraging parked cars as an alternative toexpensive deployments of roadside units was first introducedin [19], as a way to boost node density in a sparse vehicularnetwork. Interesting works such as [20] and [21] furthercontributed to this idea, suggesting the use of parked cars toovercome communication issues due to corner obstructions,and as RSU assistants to content caching and downloading.Later works, in [9], [10], proposed a broader use for these en-tities, allowing them to self-organize to form a more expansiveand versatile vehicular support network.

Our work brings new mechanisms for self-organizingparked cars that improve on existing approaches by being ableto further optimize the resulting support network, acting bothduring the initial formation of the network and its steady state,and incorporating battery usage information to limit the powerdrawn from each active car. This work also provides the firstcomprehensive evaluation of such networks in scenarios withrealistic models of parking frequency and duration, and anexperimental verification of key concepts.

VI. CONCLUSION

A novel approach has been provided to enable parked cars toself-organize and form widespread vehicular support networksin the urban area, which can then be used to monitor vehiculartraffic, public transportation, and available parking. Throughnewly introduced mechanisms and a multi-criteria decisionprocess, the vehicular support network that is created canbe continuously optimized through time, designed to targetspecific metrics of signal strength, roadside unit saturation,and area covered, while rotating RSU roles between vehiclesto manage battery utilization. An extensive simulation study,coupled with an experimental verification of key concepts,provides the first strong evaluation of a self-organizing ap-proach for parked cars. It reveals, for the first time, how sucha network forms and evolves, what quality of coverage ispossible and how it is distributed, what balance is achievablebetween recruited vehicles and network strength, and validatesthe proposed mechanisms against simulations that integraterealistic models of parking behavior.

REFERENCES

[1] N. Alam, A.T. Balaie, and A.G. Dempster, “A DSRC-Based TrafficFlow Monitoring and Lane Detection System,” IEEE 73rd VehicularTechnology Conference (VTC Spring), 2011.

[2] Kun-chan Lan and Ze Ming Wu, “On the feasibility of using publictransport as data mules for traffic monitoring,” IEEE Intelligent VehiclesSymposium, pp. 979-984, 2008.

[3] M. De Felice et al., “Traffic monitoring and incident detection throughVANETs,” 11th Annual Conference on Wireless On-demand NetworkSystems and Services (WONS), pp. 122-129, 2014.

[4] M. Caliskan, D. Graupner, and M. Mauve, “Decentralized Discovery ofFree Parking Spaces,” Proceedings of the 3rd International Workshopon Vehicular Ad Hoc Networks (VANET ’06), pp. 30-39, 2006.

[5] R. Panayappan et al., “VANET-based approach for parking spaceavailability,” Proceedings of the 4th ACM international Workshop onVehicular Ad Hoc Networks (VANET ’07), pp. 75-76, 2007.

[6] Ling-yan Wang et al., “A research on bus information service systemusing DSRC,” IEEE 5th International Conference on Intelligent Trans-portation Systems, pp. 365-369, 2002.

[7] Center for Automotive Research (CAR), “Connected Vehicle Technol-ogy Industry Delphi Study,” [Tech. Rep.], Sep 2012.

[8] American Association of State Highway and Transportation Officials(AASHTO), “National Connected Vehicle Field Infrastructure FootprintAnalysis,” [Tech. Rep.], No. FHWA-JPO-14-125, Jun. 2014.

[9] A.B. Reis, S. Sargento, and O.K. Tonguz, “Leveraging Parked Carsas Urban Self-Organizing Road-Side Units,” IEEE 82nd VehicularTechnology Conference (VTC2015-Fall), Sep. 2015.

[10] A.B. Reis, S. Sargento, and O.K. Tonguz, “Parked Cars are ExcellentRoadside Units,” IEEE Transactions on Intelligent Transportation Sys-tems, 2017.

[11] A.B. Reis and S. Sargento, “Statistics of Parked Cars for Urban VehicularNetworks,” 2016 IEEE 17th International Symposium on A World ofWireless, Mobile and Multimedia Networks (WoWMoM), Jun. 2016.

[12] D. Krajzewicz et al., “Recent Development and Applications of SUMO– Simulation of Urban MObility,” International Journal on Advances inSystems and Measurements, vol. 5, no. 3&4, pp. 128-138, 2012.

[13] PostGIS: Spatial and Geographic objects for PostgreSQL, [Online].Available: http://postgis.net/

[14] OpenStreetMap Foundation and OpenStreetMap Contributors, Open-StreetMap. [Online]. Available: http://www.openstreetmap.org/

[15] A. Cardote et al., “A statistical channel model for realistic simulationin VANET,” 2012 IEEE VNC, pp. 48-55, Nov. 2012.

[16] A.B. Reis, “Swift-GISSUMO: A Swift implementation of a middlewareto integrate mobility from SUMO and geography from GIS,” [Online].Available: https://github.com/abreis/swift-gissumo/tree/its-t

[17] U. Lee et al., “Mobeyes: smart mobs for urban monitoring with avehicular sensor network,” IEEE Wireless Communications, vol. 13,no. 5, pp. 52-57, Oct. 2006.

[18] A. Bazzi et al., “IEEE 802.11p for cellular offloading in vehicular sensornetworks,” Computer Communications, vol. 60, pp. 97-108, 2015.

[19] Nianbo Liu, Ming Liu, Wei Lou, Guihai Chen, and Jiannong Cao,“PVA in VANETs: Stopped cars are not silent,” 2011 Proceedings IEEEINFOCOM, pp. 431-435, Apr. 2011.

[20] C. Sommer, D. Eckhoff, and F. Dressler, “IVC in Cities: Signal Atten-uation by Buildings and How Parked Cars Can Improve the Situation,”IEEE Transactions on Mobile Computing, vol. 13, no. 8, Aug. 2014.

[21] F. Malandrino, C. Casetti, C.F. Chiasserini, C. Sommer, and F. Dressler,“Content downloading in vehicular networks: Bringing parked cars intothe picture,” IEEE PIMRC 2012, pp. 1534-1539, Sep. 2012.

Andre B. Reis is currently working toward thePh.D. degree in electrical and computer engineeringwith Carnegie Mellon University (CMU), Pittsburgh,PA, USA, and the University of Aveiro under theCMU-Portugal Program. He received the B.S. andM.Sc. degrees in electronics and telecommunicationsengineering from the University of Aveiro, Portu-gal, in 2009, in collaboration with the EindhovenUniversity of Technology, Netherlands. His currentresearch focuses on infrastructure support systemsfor vehicular networks in challenging scenarios. He

has also published on multimedia Quality of Experience over ad hoc networks.

12

Susana Sargento is an Associate Professor with“Habilitation” in the University of Aveiro and theInstitute of Telecommunications, where she is lead-ing the Network Architectures and Protocols (NAP)group. She has more than 15 years of experience intechnical leadership in many national and interna-tional projects, and worked closely with telecom op-erators and OEMs. She has been involved in severalFP7 projects, EU Coordinated Support Action 2012-316296 “FUTURE-CITIES”, national projects, andCMU-Portugal projects. She has been TPC-Chair

and organized several international conferences and workshops. She has alsobeen a reviewer of numerous international conferences and journals, such asIEEE Wireless Communications, IEEE Networks, and IEEE Communications.Her main research interests are in the areas of self-organized networks, inad-hoc and vehicular network mechanisms and protocols, such as routing,mobility, security and delay-tolerant mechanisms, resource management, andcontent distribution networks. In March 2012, Susana co-founded a vehicularnetworking company, Veniam, a spin-off of the Universities of Aveiro andPorto, which builds a seamless low-cost vehicle-based internet infrastructure.Susana is the winner of the 2016 EU Prize for Women Innovators.

Ozan K. Tonguz is a tenured full professor in theElectrical and Computer Engineering Department ofCarnegie Mellon University (CMU). He currentlyleads substantial research efforts at CMU in thebroad areas of telecommunications and networking.He has published about 300 research papers in IEEEjournals and conference proceedings in the areas ofwireless networking, optical communications, andcomputer networks. He is the author (with G. Fer-rari) of the book Ad Hoc Wireless Networks: ACommunication-Theoretic Perspective (Wiley, 2006).

He is the inventor of 15 issued or pending patents (12 US patents and 3international patents). In December 2010, he founded the CMU startup knownas Virtual Traffic Lights, LLC, which specializes in providing solutions toacute transportation problems using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications paradigms. His current research interestsinclude vehicular networks, wireless ad hoc networks, sensor networks, self-organizing networks, artificial intelligence (AI), statistical machine learning,smart grid, bioinformatics, and security. He currently serves or has served as aconsultant or expert for several companies, major law firms, and governmentagencies in the United States, Europe, and Asia.