Exploiting Map Topology Knowledge forContext-predictive Multi-interface Car-to-cloud

CommunicationBenjamin Sliwa, Johannes Pillmann, Maximilian Klaß and Christian Wietfeld

Communication Networks InstituteTU Dortmund University, 44227 Dortmund, Germany

e-mail: {Benjamin.Sliwa, Johannes.Pillmann, Maximilian.Klass, Christian.Wietfeld}@tu-dortmund.de

Abstract—While the automotive industry is currently fac-ing a contest among different communication technologies andparadigms about predominance in the connected vehicles sector,the diversity of the various application requirements makesit unlikely that a single technology will be able to fulfill allgiven demands. Instead, the joint usage of multiple commu-nication technologies seems to be a promising candidate thatallows benefiting from characteristical strengths (e.g., usinglow latency direct communication for safety-related messaging).Consequently, dynamic network interface selection has become afield of scientific interest. In this paper, we present a cross-layerapproach for context-aware transmission of vehicular sensordata that exploits mobility control knowledge for schedulingthe transmission time with respect to the anticipated channelconditions for the corresponding communication technology. Theproposed multi-interface transmission scheme is evaluated ina comprehensive simulation study, where it is able to achievesignificant improvements in data rate and reliability.

I. INTRODUCTION

Within the ongoing struggle between IEEE 802.11p and5G about which technology will win the race for becomingthe enabler for autonomous driving and a key factor forsmart city-based Intelligent Transportation Systems (ITSs) [1],more and more research works address the joint-usage ofboth technologies in order to exploit specific strengths andcompensate corresponding shortcomings [2]. While the mainuse-case for WiFi-based approaches is near-field safety-relatedcommunication such as the distribution of Cooperative Aware-ness Messages (CAMs), an additional cellular connection willserve for uplink car-to-cloud communication using the vehicleas a mobile sensor, e.g. for predictive maintenance, trafficmonitoring and intelligent traffic control (cf. Fig. 1).

In earlier work [3], we presented the Channel-aware Trans-mission (CAT) scheme that takes into account the channelquality based on the Signal-to-noise ratio (SNR) of the LongTerm Evolution (LTE) downlink signal for determining ef-ficient transmission times in order to avoid resource-intensetransmissions during unfavorable channel situations. Anothersignificant boost in the transmission efficiency was achieved bymachine learning based throughput prediction in [4] using allavailable LTE downlink indicators as well as knowledge aboutthe payload size. While the previous work only addressedpure cellular data transmission via a single LTE link, theapplicability of the context-aware transmission approach for

multi-interface applications with heterogeneous communica-tion technologies is evaluated in this work. Furthermore,the availability of mobility information obtained from thenavigation system is exploited to integrate knowledge aboutthe surrounding buildings and the anticipated trajectory of thevehicle into the transmission decision process and take theshadowing effects into account for the channel assessment.

The paper is structured as follows. After giving an overviewabout relevant state-of-the-art approaches, the system modelof the solution approach is presented and the analytical modelof the transmission scheme is discussed. Afterwards, the setupfor the simulative evaluation is introduced and finally, detailedevaluations focusing on specific indicators and performancemeasures are discussed.

II. RELATED WORK

The research fields related to this work are classified bythe topics context-aware communication and multi-interfacedata transmission. The anticipatory communication paradigm[5] has been proposed for paying attention to the channeldynamics mainly related to the mobility behavior of thevehicles. Mobility knowledge and context information is ex-

Exploitation by Data-driven Cloud Services

IEEE 802.11p-enabledroad infrastructure

Cellular 4G/5Gradio link

Cloud Access

IntelligentTraffic Control

PredictiveMaintenance

RoadRoughness

WeatherForecast

Sensing of vehicle andenvironment data

Fig. 1. Example application scenario: Vehicles collect sensor data whichis then transmitted to the cloud via the radio link with the highest availablenetwork quality.

Accepted for presentation in: 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall)

c© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses,including reprinting/republishing this material for advertising or promotional purposes, collecting new collected worksfor resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ploited to optimize decision processes within the communi-cation systems themselves. In [6], the problem of building-related shadowing in vehicular communication scenarios isdiscussed and addressed by dynamic beaconing strategies. Acomprehensive survey about interworking of IEEE 802.11pand cellular network technologies for Vehicle-to-everything(V2X) applications is provided in [7] and a simulation-basedstudy of various application requirements for both technolo-gies is presented in [8]. The authors of [9] bring togetherthe aforementioned research fields for infrastructure-assistedselection of the currently best radio access technology. Withina simulative performance evaluation, they achieve significantbenefits for downloading data from a server before a defineddeadline has expired.

A hierarchical method with a centralized server for inter-face selection is proposed in [10]. The network interface foropportunistic data transmissions is selected with respect tothe communication region based on previous measurementsthat are maintained in a database. In order to pay attention tothe time-variance of the transmission channel, vehicles haveto periodically download the most recent decision strategies.Similarly, [11] proposes interface selection based on Qualityof Service (QoS) requirements for enabling handovers betweendifferent technologies. Another approach for exploiting thepresence of multiple network interfaces, that is currentlyreceiving great attention within the vehicular communicationcommunity, is the application of network coding methods. In[12], the authors propose a network-coding-assisted schedul-ing scheme for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. While this specific topicis not further discussed in the context of this work, it will beconsidered for future extensions of the proposal. In contrast toexisting solutions that integrate the infrastructure side into theinterface selection process and which are therefore unlikely tobe realized in real-world scenarios due to the required changeson the network provider side, the proposed approach worksdecentralized and only uses locally accessible information.

III. SIMULATION-BASED SOLUTION APPROACH

In this section, the overall system model as well as theanalytical model for the transmission scheme are presented.Vehicles perform uplink transmissions of sensor data to acloud-server using the proposed context-aware transmissionschemes in order to exploit favorable channel conditions forincreased data rate and reliability.

The processing steps for context-aware multi-interface datatransmission are illustrated in Fig. 2. The generated sensor datais stored in a local buffer until a positive sending decisionis made. Mobility information as well as current channelquality measurements provide the transmission context andinfluence the sending decision using mobility prediction andchannel quality estimation. Each host evaluates the obstaclechannel model of [13] for the current and predicted position.Thus a sending probability is calculated and evaluated beforetransmitting the whole data of the buffer. Several system

VelocityPositionDirectionTrajectory

Context-aware Transmission Scheme

MobilityPrediction

Dynamic Interface Selection

Transmission Probabilitiesper Network Interface

Future ChannelContext

Future Position

IEEE 802.11p LTE

Transmission ofthe whole buffer

ChannelEstimation

RSRPRSSI

SystemParameters

ChannelSensing

Mobility

Context-awareness Data Transmission Process

DataSource

TransmissionBuffer

Virtual Sensor Application

Transmission Payload

Context

SensorPacket

Fig. 2. System model of the context-predictive multi-interface transmissionapproach.

parameters can be changed to influence the sending decisionand the interface selection characteristics.

A. Mobility Prediction

In the course of this work, three different mobility predictionmethods are implemented and evaluated that differ in theutilized mobility information. Fig. 3 gives a brief overviewof the concept of mobility prediction and the implementedchannel model considering obstacles in the line of sight(LOS). A major goal of the transmission process is to avoidtransmissions while buildings are in the LOS, as they have ahigh impact on the channel quality. Thus, mobility predictionis applied to find a future location with an expected enhancedchannel quality. The first algorithm uses simple linear Extrap-olation to predict the vehicle’s future position based on itscurrent speed v(t) and direction for a given prediction horizonτ . The traveled distance smax is calculated as:

smax = v(t) · τ (1)

Free space

Shadowing-relatedattenuation

Current vehicleposition

=5s

=10s

Fig. 3. Mobility prediction with different values for the prediction horizonτ . Here, the data transmission is most likely to be triggered at τ = 10s, afterthe vehicle has passed the building-related attenuation region.

The second method Trajectoryvel uses the calculated valueof smax but considers the vehicle’s route by accessing thenavigation system.

smax =

{0.5 · a(t)2 · τ + v(t) · τ v(t) < vmax

vmax · τ v(t) = vmax(2)

For the third method Trajectoryacc, the calculation ofsmax takes the current acceleration a(t) into account usingEq. 2. The acceleration-based term is used until the maximumspeed allowed on the road segment is reached. This methodaims to provide a better model for the vehicle’s behavior attraffic signals and is only applied for positive accelerationvalues.

Based on the calculated maximum traveling distance thefuture position of the vehicle is obtained, following the route,if available. Afterwards, for the channel quality estimation, thechannel model is evaluated for the predicted position.

B. Single-interface Transmission SchemeFig. 4 shows an example trace of the SNR and compares

periodic transmissions (which is performed regardless of thecurrent channel situation) with the original probabilistic CATas described in [3], which calculates a transmission probabilitywith respect to the measured SNR value. Thus, good channelconditions are chosen more likely for transmitting data andresult in an enhanced mean data rate.

Periodic

CATHigh sending probability for

good channel conditions

Low sending probability for

bad channel conditions

Fig. 4. Example temporal behavior of the CAT [3] transmission scheme incomparison to naive periodic data transmissions that do not take the channelquality into account.

In order to enable multi-interface transmissions with het-erogeneous communication technologies, the implicitly SNR-normed metric of the legacy CAT needs to be abstracted toallow the comparison of metrics with different value ranges.Therefore, the abstract metric Φ is defined by its minimumvalue Φmin and maximum value Φmax. With Eq. 3, the currentmetric value Φ(t) is normed to its respective value range. Thetransmission probability p(t), which is calculated according toEq. 4, is computed in the time interval [tmin, tmax] in orderto avoid too long buffering times and too short data packets.The exponent α defines how much the transmission probabilityshould rely on very high metric values within the respectivevalue range.

Θ(t) =Φ(t)− ΦminΦmax − Φmin

(3)

p(t) =

0 t− t′ ≤ tmin

Θ(t)α tmin ≤ t− t′ ≤ tmax

1 t− t′ ≥ tmax

(4)

For considering the predicted future network state, theextended predictive CAT (pCAT) transmission scheme is intro-duced with Eq. 5. Depending on the anticipated developmentof the channel quality, the transmission probability is changedto transmit data early if the channel quality decreases or delaythe transmission if it improves. γ1 is applied if the channelquality is expected to improve and γ2 if it is anticipated todecrease.

p(t) =

0 t− t′ ≤ tmin

Θ(t)α·z1 tmin ≤ t− t′ ≤ tmax,∆Φ(t) ≥ 0

Θ(t)αz2 tmin ≤ t− t′ ≤ tmax,∆Φ(t) ≤ 0

1 t− t′ ≥ tmax

(5)

z1 = max (∆Φ(t) · (1−Θ(t)) · γ1, 1) (6)

z2 = max (|∆Φ(t) ·Θ(t) · γ2| , 1) (7)

∆Φ(t) = Φ (t, t+ τ)− Φ(t) (8)

C. Multi-interface Transmission Scheme

The proposed multi-interface approach computes the trans-mission probabilities for every network interface of the vehicleaccording to Eq. 9. The overall transmission probability isthe maximum value of the different individual interfaces.Thereupon, the overall probability is evaluated to send alldata in the buffer. As the proposed algorithm is workingprobabilistically, the selection of the network interface itself isindependent of the current transmission scheme, which mightbe CAT or pCAT.

pMI = max {pi(Φ(t))} ∀ Φ(t) (9)

For the following evaluations, two different metrics aredefined: ΦRSRP is based on the Reference Signal ReceivedPower (RSRP) of the LTE link and ΦRSSI characterizesthe Received Signal Strength Indicator (RSSI) of the IEEE802.11p signal.

IV. METHODOLOGICAL SETUP

The methodological simulation setup is illustrated in Fig. 5and consists of the third party extension frameworks SimuLTE[14] and Vehicles in Network Simulation (Veins) [15] that usethe OMNeT++ [16] simulation environment. In addition, thetraffic simulator Simulation of Urban Mobility (SUMO) [17]is used to model the vehicular mobility and to provide thebuilding locations.

Network Simulation withINET/OMNeT++

SimuLTELTE

VeinsIEEE 802.11p

Transmission ChannelObstacle Shadowing

Sensor ApplicationData Generation and

Transmission Decision

PHY

APP

MACVehicularMobility

MapTopology

Traffic Simulationwith SUMO

Fig. 5. Coupling of mobility and communication simulator. Frameworks forLTE and IEEE 802.11p are coupled within OMNeT++ for communicationsimulation.

Although OMNeT++ itself is built upon a modular ap-proach, various changes on the different layers were necessaryas the extension frameworks are not designed for being used ina multi-interface use-case and Veins has a strong dependencyamong the different layers. Similarly, as SimuLTE relies onits own dedicated channel models, the physical layer wasbrought together with the Veins channel model in order tointegrate shadowing effects and establish consistency betweenthe different frameworks.

eNodeB PGW Router

Server

RSUStart

End

Fig. 6. Generic Manhattan Grid scenario with three LTE cells and eightRSUs and the route of an example vehicle.

Fig. 6 shows the map of the simulation scenario. Fromall present vehicles, a percentage of r are randomly chosento be equipped with communication capabilities, the othersact as interference traffic. Each vehicle performs and grid-based Random Walk and sends sensor data to the cloud-serveraccording to the transmission scheme. There are three LTEcells and eight RSUs in the simulation playground. Fig. 6shows the route of an exemplary vehicle. In the followingsection, the temporal behavior of this particular vehicle is usedto state out characteristical effects occurring in mobility andchannel prediction.

The relevant parameters for the reference scenario are

TABLE ISIMULATION PARAMETERS

Parameter Value

LTE carrier frequency 1800 MhzeNode B transmission power 33 dBmeNode B antenna OmnidirectionalIEEE 802.11p carrier frequency 5.89 GHzSensor frequency 1 HzSensor payload 10 kByteTransmission interval ∆t 15 sMinimum buffering duration tmin 10 sMaximum buffering duration tmax 60 sPlayground size 1000 m × 1000 mNumber of vehicles 150Penetration rate r 10 %Attenuation per cut β 2 dBΦRSRP [min,max] [-140 dBm, -50 dBm]ΦRSSI [min,max] [-89 dBm, -50 dBm]α 8γ1 3γ2 0.5

defined in Tab. I.

V. RESULTS OF THE SIMULATIVE PERFORMANCEEVALUATION

This section evaluates the three different mobility predictionalgorithms as well as the resulting channel quality prediction.The results of the simulation runs for an LTE, 802.11p and amulti-interface scenario are presented and discussed.

Peak error values due to directionchanges (e.g. turns) of the vehicle

Extrapolation

Trajectoryvel

Trajectoryacc

Fig. 7. Temporal behavior of the absolute location prediction error withprediction horizon τ = 10 s for a single vehicle.

Fig. 7 shows the absolute location prediction error forthe aforementioned vehicle for short-term predictions withτ = 10s. The extrapolation-based algorithm performs worst,as it does not consider turns, resulting in the peaks withhigh position errors. The trajectory-aware approaches performsignificantly better. Additionally, considering the accelerationfurther reduces the resulting error after the car has stopped orslowed down at intersections and traffic signals.

Fig. 8 shows the statistical properties for all consideredmobility prediction schemes with τ = 10 s, τ = 30 s andτ = 60 s. The achievable error range is mainly influencedby the vehicle’s current speed value and its respective time-stability. Interactions with other traffic participants and trafficsignals have a major impact on the resulting accuracy as theyare not considered by the mobility prediction.

0 20 40 60 80 100

Position Error [m]

0.0

0.2

0.4

0.6

0.8

1.0

CD

FΦ

=10s

=30s

=60s

Extrapolation Trajectoryvel

Trajectoryacc

Fig. 8. Accuracy of three different mobility prediction methods evaluatedfor for all vehicles with prediction horizons of 10 s, 30 s and 60 s.

0 50 100 150 200 250 300 350

Time [s]

140

130

120

110

100

90

80

70

60

50

RSRP

[ dB

m]

LTE Signal

Predicted value

Measurement

IEEE 802.11p Signal

Fig. 9. Temporal behavior of the RSRP and RSSI estimation based on themobility prediction with acceleration of one example car. The black graphrepresents the actual measurements, whereas the red graph represents theestimated values.

Fig. 9 shows the measured values for RSRP and RSSI forthe given route in Fig. 6. Due to the limited range of the RSUs,connection losses occur between the connectivity peaks. Ashandovers are not considered by the prediction process, theRSRP estimation differs from the measured values resultingin small peaks.

The accuracy of the channel metric prediction mainly relieson the quality of the mobility prediction, as it can be seen inFig. 10. A smaller prediction horizon reduces the dependencyon an accurate speed value and results in a lower predictionerror.

Fig. 11 illustrates the temporal behavior of the consideredradio links as well as the resulting transmission scheme. Thesending algorithm chooses the interface with the best channelquality and thus avoids connectivity valleys of each technologyby choosing the other.

The resulting overall values for the considered key perfor-mance indicators goodput, sensor data age (which is mainly

0 5 10 15 20

RSRP Error [dB]

0.0

0.2

0.4

0.6

0.8

1.0

CD

FΦ

Extrapolation

Trajectoryvel

Trajectoryacc

=10s

=30s

=60s

0 5 10 15 20

RSSI Error [dB]

0.0

0.2

0.4

0.6

0.8

1.0

CD

FΦ

Fig. 10. Accuracy of the RSRP and RSSI estimation for the different mobilityprediction methods and prediction horizons.

0 50 100 150 200 250 300 350

Time [s]

0.0

0.2

0.4

0.6

0.8

1.0N

orm

ed

Metr

ic

Exploitation of RSU links duringlow quality cellular connection periods

Fig. 11. Temporal behavior for a single vehicle using multiple interfaces forsensor data transmission. The dashed green line indicates the mean value ofthe metric for a sending decision.

determined by the duration a sensor packet is stored in thetransmission buffer) and packet delivery ratio are shown inFig. 12, which compares the application-layer performance ofthe solely application of a single communication technologywith the multi-interface approach. The results show that theachievable data rate is significantly increased by both, multi-interface communication (+25% data rate increase) as well astopology-awareness using pCAT (+17% throughput gain). Forpure IEEE 802.11p-based transmission, the mean sensor dataage is very high due to the relatively low coverage of theRSUs, resulting in high buffering durations. Here, the multi-interface approach is able to achieve significant benefits byusing the cellular LTE link when no IEEE 802.11p connectionis available.

VI. CONCLUSION

In this paper, we presented an anticipatory multi-interfacecommunication scheme that uses mobility prediction and chan-nel quality estimation in order to optimize the transmission

LTE 802.11p MI LTE 802.11p MI LTE 802.11p MI0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Goodput

[MB

it/s]

Periodic CAT pCAT

+17 % through topology-awareness

+26 % through multi-interface approach

LTE 802.11p MI LTE 802.11p MI LTE 802.11p MI020406080

100120140160

Sens

or A

ge [s

]

Periodic CAT pCAT

LTE 802.11p MI LTE 802.11p MI LTE 802.11p MI0.0

0.2

0.4

0.6

0.8

1.0

Pack

et D

eliv

ery

Ratio

Periodic CAT pCAT

Fig. 12. Goodput and sensor data age for three different scenarios andtransmission schemes. The tradeoff between goodput and data delay is reducedfor the multi interface approach.

time of vehicular sensor data. Moreover, topology informationabout the surrounding buildings is taken into account forintegrating the shadowing-behavior of the radio link into thetransmission decision process.

Using comprehensive simulations in OMNeT++, it wasshown that the mean data rate and reliability of the car-to-cloud data transmissions can be significantly improvedby mobility-prediction and multi-interface communication. Infuture work, the transmission scheme will be integrated intoreal-world devices for comprehensive field evaluation in thepublic cellular network. In addition, we will evaluate the useof network coding techniques to increase the robustness of thedata transmissions.

ACKNOWLEDGMENT

Part of the work on this paper has been supported by Deutsche Forschungs-gemeinschaft (DFG) within the Collaborative Research Center SFB 876“Providing Information by Resource-Constrained Analysis”, project B4 andhas been conducted within the AutoMat (Automotive Big Data Marketplacefor Innovative Cross-sectorial Vehicle Data Services) project, which receivedfunding from the European Union’s Horizon 2020 (H2020) research andinnovation programme under the Grant Agreement No 644657.

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