Efficient Machine-type Communication usingMulti-metric Context-awareness for Cars used as

Mobile Sensors in Upcoming 5G NetworksBenjamin Sliwa1, Thomas Liebig2, Robert Falkenberg1, Johannes Pillmann1 and Christian Wietfeld1

1Communication Networks Institute, 2Department of Computer Science VIIITU Dortmund University, 44227 Dortmund, Germany

e-mail: {Benjamin.Sliwa, Thomas.Liebig, Robert.Falkenberg, Johannes.Pillmann, Christian.Wietfeld}@tu-dortmund.de

Published in: 2018 IEEE 87th IEEE Vehicular Technology Conference (VTC-Spring)DOI: 10.1109/VTCSpring.2018.8417753

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.

Abstract—Upcoming 5G-based communication networks willbe confronted with huge increases in the amount of transmittedsensor data related to massive deployments of static and mobileInternet of Things (IoT) systems. Cars acting as mobile sensorswill become important data sources for cloud-based applicationslike predictive maintenance and dynamic traffic forecast. Due tothe limitation of available communication resources, it is expectedthat the grows in Machine-Type Communication (MTC) willcause severe interference with Human-to-human (H2H) commu-nication. Consequently, more efficient transmission methods arehighly required. In this paper, we present a probabilistic schemefor efficient transmission of vehicular sensor data which leveragesfavorable channel conditions and avoids transmissions when theyare expected to be highly resource-consuming. Multiple variantsof the proposed scheme are evaluated in comprehensive real-world experiments. Through machine learning based combina-tion of multiple context metrics, the proposed scheme is able toachieve up to 164% higher average data rate values for sensorapplications with soft deadline requirements compared to regularperiodic transmission.

I. INTRODUCTION

The deployment of autonomous cars will act as a catalyst fordata-driven sensor systems. Crowdsensing-based applicationssuch as road-roughness detection and distributed weather-forecasting will achieve significant gains in the availabilityand up-to-dateness of sensor data with the help of cars actingas mobile sensor nodes. Those kinds of applications do nothave real-time requirements for the data, but soft deadlines,where too old data is discarded. Consequently, upcoming 5Gnetworks will require mechanisms for dealing with a highincrease in the amount of transmitted MTC-data. In additionto reliable data transfer, highly efficient data transmissionsmechanisms are required to keep the interference with H2Hcommunication as low as possible. In earlier work, we haveproposed the opportunistic transmission scheme Channel-aware Transmission (CAT) [1] for Long Term Evolution(LTE) cellular networks, which has been extended with apredictive component to consider future channel conditionswith predictive CAT (pCAT) in [2]. Its general idea is to storesensor data locally as long as the channel quality is low inorder to avoid packet loss and retransmissions. The buffereddata is sent to a cloud-server when the device is experiencing aconnectivity hotspot with very high channel quality. The actualtransmissions are triggered depending on a passive downlinkchannel quality indicator. In this paper, we generalize theprinciples and extend CAT with a generalized multi-metricmodel for context-aware MTC in vehicular environments.The paper is structured as follows. After presenting relevant

state-of-the-art work in this area, we present the analyticalmodel of the proposed probabilistic transmission scheme anddiscuss methods for combining multiple transmission metricsbased on generic approaches and means of machine learning.Afterwards, the setup of the experimental evaluation studyis introduced and finally the results are presented. In orderto guarantee a high level of reproducibility, we provide thedeveloped measurement software in an Open Source way aswell as the raw data obtained from our measurements.

II. RELATED WORK

Due to a constantly increasing amount of mobile traffic, an-ticipatory and opportunistic data transmission approaches gaingreater attraction, especially for MTC. Therefore, researchershave started to excessively analyze the performance of real-life mobile networks to derive metrics and methods for theoptimization of versatile mobile applications or the networkitself at different system levels [3].

While many works address the optimization of the infras-tructure side, e.g., optimizing the radio-resource schedulerby forecasting the User Equipment (UE) traffic [4], [5] andmobility [6], [7], numerous approaches focus on the UE sideas the main trigger of transmissions in a cellular network: Withknowledge of the UE trajectory, centralized approaches like [8]incrementally generate maps of the link-quality at correspond-ing locations. Such measurements, in conjunction with online-learning algorithms, may be used to derive coverage mapsof a cellular network [9] and finally schedule transmissionsaccording to the current connectivity. Other researches proposedecentralized approaches with the focus on QoS and QoEfor continuous or buffered transmissions, e.g., video stream-ing [10], [11]. Here, the throughput-measurements of recenttransmissions feed an adaption logic in the client to requesta dynamically-scaled quality of the content according to thecurrent link capacity.

For short transmissions, e.g. MTC, passive metrics becomemore important since active probing is impractical in thesecases [12]. An empirical comparison of passive downlinkindicators like Received Signal-Strength Indicator (RSSI) andSignal-to-Noise-Ratio (SNR) on the throughput in differentenvironments is addressed in [13]. In [14] the authors analyzeand identify the suitability of indicators like Reference Sig-nal Received Power (RSRP) and Reference Signal ReceivedQuality (RSRQ) for estimations of the uplink connectivity atdifferent cell loads.

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Fig. 1. Example temporal behavior for periodic data transfer and the proposedCAT-based scheme. CAT avoids transmissions during connectivity valleys andtransmits more frequently when experiencing connectivity hotspots

III. SOLUTION APPROACH

Regular transmission schemes for sensor data are mostlyperiodic and do not consider the current channel qualitysituation for timing the data transfer. Consequently, manytransmissions are performed with a low transmission effi-ciency, require retransmissions and consume a lot of energy.Moreover, channel capacity is occupied for a long time periodand is not available for other cell participants. It can beconcluded, that the determination of favorable transmissiontimes can have a significant impact on the overall system effi-ciency. Consequently, connection hotspots should be exploitedwhereas transmissions during connectivity valleys should beavoided. In the following, we first present the analytical modelof the proposed probabilistic transmission scheme. Afterwardsdifferent methods for combining the available indicators inorder to achieve a better measurement for the actual channelquality are discussed. An example trace is shown in Fig. 1.While the periodic transmission approach sends data regardlessof the current channel conditions, the proposed CAT schemeavoids transmissions at bad channel conditions and sends datawith a higher frequency if the channel quality is high.

A. Analytical Model of the Transmission Scheme

The generalized model for CAT is shown in Eq. 1. Eachtdecision seconds, it computes the transmission probabilitypΦ(t) for an abstract metric Φ which is specified by aminimum value Φmin and a maximum value Φmax. Φ iscalled conducive if the channel quality gets better with highervalues of Φ and harmful if it gets worse. Transmissions are notperformed if the time difference ∆t since the last transmissionis below a minimum value tmin and performed regardless ofthe current metric value if ∆t exceeds a defined maximumvalue tmax. The timeouts are used to specify the application re-quirements in terms of packet size and deadline requirements.If ∆t is in between those timeouts, a transmission probabilityis computed depending on the current metric value Φ(t) withthe helper function Θ and Eq. 2.

pΦ(t) =

{0 : ∆t ≤ tminΘ(Φ(t))α : tmin < ∆t < tmax1 : ∆t > tmax

(1)

Θ (Φ(t)) =

{Φ(t)−Φmin

Φmax−Φmin: Φ is conducive

1−(

Φmax−Φmin

Φ(t)−Φmin

): Φ is harmful

(2)

The metric is further characterized using a weighting exponentα that controls the convergence speed of Φ towards Φmaxand specifies the impact of metric differences for the channelquality. Fig. 2 shows the analytical transmission probabilitywith respect to the current metric value for different exponentvalues.

0 5 10 15 20 25 30 35 40

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Fig. 2. Analytical transmission probability for CAT for different values ofthe weighting exponent α.

B. Multi-metric Transmission

Cellular communication networks provide a wide range ofdifferent network quality indicators that can be used on theclient side in order to evaluate the actual channel quality. In thefollowing, different generic and machine-learning based ap-proaches for combining the different indicators are described.

1) Optimistic: The transmission probability is computed forall considered metrics. As the resulting transmission probabil-ity, the maximum value is chosen (Eq. 3) in order to leverageconnectivity hotspots.

popt(t) = max {pΦ1(t), pΦ2(t), ..., pΦn(t)} (3)

2) Pessimistic: The minimum value of all considered metricis used as the transmission probability. This approach focuseson avoiding transmissions during connectivity valleys.

ppes(t) = min {pΦ1(t), pΦ2(t), ..., pΦn(t)} (4)

3) Weighted Mean: If knowledge about the impact of theindividual metrics on the overall performance is available, itcan be used to strengthen or weaken their weight.

pmean(t) =1∑gi

n∑i=1

pΦi(t) · gi (5)

4) Machine Learning based Datarate Prediction: With thisapproach, the anticipated data rate is computed and used as thedecision metric for the transmission scheme. The processingpipeline for the machine learning process is illustrated inFig. 3. The available context parameters are used as the fea-tures and different learning models (Artificial Neural Network(ANN), M5 Decision Tree (M5T), Support Vector Machine(SVM), Linear Regression (LR)) are applied (cf. Tab. IV forthe performance comparison). During the training phase, themeasured effective data rate forms the label for the learningprocess. In contrast to the other multi-metric approaches, alsothe payload size of the current data packet is integrated intothe transmission decision.

IV. METHODOLOGY

In this section, we present the setup for our empirical study.The default parameters are denoted in Tab. I.

TABLE IPARAMETERS OF THE REFERENCE SCENARIO

Parameter ValueSensor frequency 1 HzSensor payload size 50 kByteTransmission interval (periodic) 30 stdecision 1 stmin {10 s, 30 s}tmax 120 s

A virtual sensor application is generating data packetsaccording to specified sensor frequency and packet size values.The packets are buffered until a transmission decision is madefor the whole buffer and the data has been transmitted suc-cessfully. For the performance evaluation, a periodic schemethat uses tmin is used as the transmission interval is usedas a reference for the probabilistic model. Fig. 4 shows amap of the evaluation scenario and consists of two connectedtracks with different characteristics. Track 1 is set up in atypical suburban area with speed limitations ranging from 30km/h to 70 km/h. Track 2 is mostly highway with a speedlimitation of 130 km/h. For most of the following evaluations,data is aggregated from both tracks and separate discussionsof the tracks are performed when considered beneficial. Foreach parametrization of the transmission scheme, 5 runs wereperformed on each of the tracks. Overall, more than 4000transmissions were performed on a total distance of morethan 1000 km. The measurements were performed in a publicLTE network. For the collection of network quality data aswell as mobility information, we developed an Android-basedmeasuring app featuring live visualization that is available asOpen Source 1 . The measurements were performed with a

1Available at https://github.com/BenSliwa/MTCApp

Passive ProbingLTE Downlink

Active ProbingHTTP POST

SensorApplication

Mobility

RSRPRSRQSNRCQI

Data rate Payload size Velocity

Model

Training Phase Application Phase

Offline Training andCross Validation

Machine Learning

Error Estimation

Online Machine Learning

Predicted Datarate

ArtificialNeural Network

M5 DecisionTree

LinearRegression

Support VectorMachine

Label Feature/

Fig. 3. Processing pipeline for the machine learning based datarate predictionprocess. The dashed components are only required for the training phase.

A 45

Track 1

9 km

Track 2

14 km

Fig. 4. Overview of the 23km long test route containing different street typesand speed limitations. (Map: c©OpenStreetMap contributors, CC BY-SA)

Samsung Galaxy S5 Neo (Model SM-G903F). The raw dataof the measurements is provided at [15] as Open Access data.

TABLE IIPARAMETERS FOR THE CONSIDERED METRICS

ΦRSRP ΦRSRQ ΦSNR ΦCQI ΦM5T

min -120dBm

-11 dB 0 dB 2 0 MBit/s

max -80 dBm -4 dB 30 dB 16 {15, 18}MBit/s

α 8 6 8 6 8conducive true true true true true

Tab. II contains the parametrization for the consideredmetrics. For ΦRSRQ and ΦCQI , the exponent α is chosenlower as the value range is smaller.

V. RESULTS

In this work, the evaluation is focused on the applicationlayer throughput (referred to as the goodput) as a measurementfor the transmission efficiency and the age of the sensor data.As discussed in [1], higher throughput values significantlyreduce the interference with other cell user as well as theoverall energy consumption of the mobile device.

A. Single Metric Transmission SchemesThe end-to-end goodput for the considered metrics is shown

in Fig. 5. All CAT-based metrics achieve significantly highergoodput values than the periodic scheme. While the highestgain is achieved for the SNR-based metric, it also shows thehighest variance. In many cases the reported SNR changessignificantly while the transmission is still being performed.Since the selection of a transmission scheme is bound to the

requirements of the actual application, the resulting DeadlineMiss Ratio (DMR) values for three different generic applica-tions are compared in Tab. III. The deadlines were chosen with

Transmission Scheme

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2.9 MBit/s+ 71.5 %

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Fig. 5. End-to-end goodput and sensor data age for the considered base metrics

TABLE IIIRESULTING DEADLINE MISS RATIO FOR MULTIPLE APPLICATION

REQUIREMENTS

Deadline tmin Periodic ΦRSRP ΦRSRQ ΦSNR ΦCQI

δ1 = 30 s10 s 0.012 0.119 0.099 0.281 0.27930 s 0.034 0.285 0.263 0.464 0.48

δ2 = 60 s10 s 0.001 0.046 0.029 0.139 0.18630 s 0.015 0.109 0.082 0.257 0.44

δ3 = 120 s10 s 0 0.01 0 0.017 030 s 0 0.021 0.012 0.014 0

δ4 = 180 s10 s 0 0 0 0.004 030 s 0 0 0 0 0

respect to realistic requirements of traffic management systemsas discussed in [16]. If the application does not have strictdeadline requirements, a higher tmin value should be selectedin order to increase the minimal packet size and achieve a bet-ter ratio of payload size and protocol overhead. The correlationbetween the considered metrics and the achieved goodput isshown in Fig. 6 for the acquired data from Sec. V-A. TheRSRP is strongly correlated with the resulting data rate ifthe device is in the cell edge region. A breakpoint of thecurve can be identified at -90 dBm that separates cell edgebehavior from cell center behavior. After the breakpoint, itsmeaningfulness is reduced to the increased interference withother communicating devices. Since the plots only considerthe value of the Key Performance Indicator (KPI) for thebeginning of the transmissions, its meaningfulness is reducedfor longer transmission durations. This is even more dramaticif the KPI itself has a high variance, as it is the case for theSNR. The packet size has a high impact on the resulting datarate as it controls the relation of payload and protocol over-head. Two areas with different characteristics can be identified.Up to approximately 2.25 MB, the protocol overhead has adominant influence and the goodput improves with higherpacket sizes. For bigger packets, the variance of the data rate ishighly increased, as the longer transmission duration increasesdependency to the channel variance. In the plot, both periodictransmission schemes (tmin = 10, tmin = 30) can be clearly

identified, as they do not alter the payload size of the packets.As a result of the correlation considerations, no single metricis able to provide a robust measurement for the channel qualityin all considered situations.

B. Machine Learning Based Datarate Prediction

Prediction of the datarate is a regression task. Thus, we trainmultiple regression methods and validate their performancein 10-fold cross validation. During the training phase, thetransmission time and the payload size of the HypertextTransfer Protocol (HTTP) POST transmissions is monitoredand the passive quality indicators are recorded. The modelswe compare are Artificial Neural Networks, Support VectorMachine (using Polynomial Kernel), M5 decision tree and asimple linear regression. The performance of the models ismeasured using the test data fold in terms of correlation, meanabsolute error and root mean squared error. Results can befound in Table IV. The highest prediction accuracy is achievedfor M5T which is the only machine learning model whichis considered for further evaluations. The performance of theprediction model is visualized in Fig. 7 where measured datarate and its predicted value are compared. For the proposedtransmission scheme, only the overestimation of the channelquality is considered harmful as only a worst-case estimationis required and underestimation will most likely even increasethe resulting data rate.

TABLE IVPERFORMANCE OF REGRESSION MODELS: ARTIFICIAL NEURAL

NETWORK (ANN), SUPPORT VECTOR MACHINE (SVM), M5 DECISIONTREE (M5T), LINEAR REGRESSION (LR) COMPARED IN TERMS OFCORRELATION, MEAN ABSOLUTE ERROR (MAE) AND ROOT MEAN

SQUARED ERROR (RMSE). BEST VALUES ARE HIGHLIGHTED.

Model Correlation MAE RMSEANN 0.83 1.62 2.07SVM 0.80 1.61 2.13M5T 0.86 1.33 1.81LR 0.57 2.19 2.92

-130 -120 -110 -100 -90 -80 -70 -60 -50

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better relation of payloadsize vs overhead

Periodic data transfer

Fig. 6. Correlation of different indicators with the resulting end-to-end goodput. The plots were generated from the data obtained from the single-metricCAT-based transmissions as well as from the periodic transmissions. The black curve shows the 0.95 confidence interval of the mean value.

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Fig. 7. Measured data rate versus predicted data rate using the M5 decisiontree. Diagonal is highlighted in blue. Underestimations of the channel qualityare not considered harmful for the proposed transmission scheme as it onlyrequires a worst-case estimation.

C. Multi-metric Transmission Schemes

Fig. 8 shows the performance evaluation for the consid-ered multi-metric transmission approaches. For this evalua-tion, the tracks are treated separately, as different behaviorswere detected during the measurements. The weighted meanapproach uses the same weights for all considered metricsas they achieved similar data rate values in the single-metricevaluations. It can be observed that the mean goodput for

the optimistic and weighted mean approach is below thebest single metric result which was achieved for ΦSNR. Itcan be concluded that low values of a single metric canbe considered meaningful indicators for unfavorable channelconnections. Contrastingly, high values of a single indicator donot provide enough information for detecting the presence of aconnectivity hotspot. For the pessimistic approach, the resultsdiffer significantly depending on the considered evaluationtrack. While very high values are achieved for the highwaytrack (mean: 14.58 MBit/s), the scheme shows a similarperformance as the single-metric for the suburban track (mean:11.16 MBit/s). The high rate of low Channel Quality Indicator(CQI) values as shown in Fig 6 combined with the lowtime variance due to relatively low velocity values resultsin a transmission scheme, which is mostly controlled by thetransmission timeout tmax and not the actual channel condi-tions. Therefore, another pessimistic transmission scheme isintroduced, which does not integrate the CQI information intothe transmission decision. As shown in the figure, the resultingperformance is similar for the two tracks (suburban mean:12.63 MBit/s, highway mean: 12.65 MBit/s). In conclusion,it is much more important to avoid transmissions duringconnectivity valleys than to leverage connectivity hotspots.The best performance is achieved with the machine learningbased approach which does not only achieve the highest meandata rate (18.5 MBit/s for the highway scenario with Φmax=18MBit/s) but also reduces the number of occurrences of lowdata rate transmissions significantly. It is the only scheme thatis not only considering the network quality, but also the sizeof the payload which has a decent influence on the achievableefficiency as shown in Fig. 6. The tradeoff between goodputand data delay can be controlled by the parameter Φmax.

Transmission Scheme

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8.64 MBit/s+ 123 %

11.5 MBit/s+ 164 %

Opt Mean Pes Pes\CQI M5T15 M5T18 Opt Mean Pes Pes\CQI M5T15 M5T18

Machine learning based approachachieves highest goodput in both

evaluation scenarios

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Age [s]

Suburban Highway

Opt Mean Pes Pes\CQI M5T15 M5T18 Opt Mean Pes Pes\CQI M5T15 M5T18

Data delay can be controlled via

Periodic

(b)

Fig. 8. End-to-end goodput and sensor data age for the considered multi-metric transmission schemes (tmin = 30).

VI. CONCLUSION

In this paper, we presented a probabilistic model for theefficient transmission of sensor data in a vehicular context.Its general goal is to find the optimal transmission time forvehicular sensor data with respect to the channel conditionsin order to save communication resources and avoid retrans-missions. In comprehensive field evaluations, the impact ofdifferent network quality indicators serving as metrics for thetransmissions decision was evaluated. Although all consideredmetrics are able to increase the resulting data rate significantly,they differ in terms of variance and DMR. Through applicationof a machine learning based combination of the availablenetwork quality indicators, the average goodput can be fur-ther improved significantly while the delay is only slightlyincreased. Other variants of the proposed transmission schemecan be considered depending on the DMR requirements ofthe application. In future work, we want to further improvethe transmission scheme by taking interdependencies with thetransport layer protocols into account. Furthermore, we wantto enhance the accuracy of network quality estimation byintegrating knowledge about the cell capacity obtained frompassive control channel analysis as described in [17].

ACKNOWLEDGMENT

Part of the work on this paper has been supported byDeutsche Forschungsgemeinschaft (DFG) within the Collabo-rative Research Center SFB 876 “Providing Information byResource-Constrained Analysis”, projects A4 and B4 andhas been conducted within the AutoMat (Automotive BigData Marketplace for Innovative Cross-sectorial Vehicle DataServices) project, which received funding from the Euro-pean Union’s Horizon 2020 (H2020) research and innova-tion programme under the Grant Agreement No 644657.Thomas Liebig received funding from the European UnionHorizon 2020 Programme (Horizon2020/2014-2020), undergrant agreement number 688380 “VaVeL: Variety, Veracity,VaLue: Handling the Multiplicity of Urban Sensors”.

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