Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

Sliwa, Benjamin; Liebig, Thomas; Falkenberg, Robert; Pillmann, Johannes; Wietfeld, Christian

doi:10.1109/vtcspring.2018.8417753

Cited by 27 publications

(27 citation statements)

References 17 publications

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“…A possible explanation for this behavior is that in many situations, one of the considered network quality indicators has a dominant impact on the behavior of the data rate under defined conditions. The analysis in [3] points out that at the cell edge -which can be identified by the RSRP -the interference level, which is partly identifiable by the RSRQ, has a strong impact on the data rate. In contrast to that, the SINR is of higher importance within the cell center.…”

Section: A Comparison Of Different Data Aggregation Approachesmentioning

confidence: 99%

Empirical Analysis of Client-Based Network Quality Prediction in Vehicular Multi-MNO Networks

Sliwa

Wietfeld

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applying anticipatory communication principles, data transmissions can dynamically utilize the mobile network with the highest estimated network performance in order to achieve improvements in data rate, resource efficiency, and reliability.In this paper, we present the results of a comprehensive real-world measurement campaign in public cellular networks in different scenarios and analyze the performance of online data rate prediction based on multiple machine learning models and data aggregation strategies. It is shown that multi-MNO approaches are able to achieve significant benefits for all considered network quality and end-to-end indicators even in the presence of a single dominant MNO. However, the analysis points out that anticipatory multi-MNO communication requires the consideration of MNO-specific machine learning models since the impact of the different features is highly depending on the configuration of the network infrastructure.• MNO uses the whole data per MNO (3 sets). • Scenario divides the data for the different scenarios campus, urban, suburban and highway (12 sets).

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Section: A Comparison Of Different Data Aggregation Approachesmentioning

confidence: 99%

Empirical Analysis of Client-Based Network Quality Prediction in Vehicular Multi-MNO Networks

Sliwa

Wietfeld

2019

2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)

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“…Data transmissions are performed from a moving vehicle to a cloud-based server, whereas the overall driven distance is more than 2000 km. Further details about the setup and the parametrization are provided in [32] and [11]. The proposed ML-CAT scheme is compared to naive periodic data transfer (transmission interval 30 s) and the SINR-based CAT and Periodic CAT ML-CAT Transmission Scheme Creation of a simulation network based on a real world scenario and definition of behavior rules for the mobile agents.…”

Section: B Data Transfer: Machine Learning-based Opportunistic Transmentioning

confidence: 99%

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Sliwa¹,

Liebig

Vranken

et al. 2019

2019 IEEE International Systems Conference (SysCon)

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While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period -the hybrid vehicular traffic -is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.

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“…If the connectivity map does not contain an entry for the estimated position P (t + τ ), the non-predictive CAT scheme as described in [12] is applied as a fallback mechanism. Fig.…”

Section: B Probabilistic Context-predictive Transmission Of Vehiculamentioning

confidence: 99%

“…The channel quality data to build the connectivity map and the mobility data for the trajectory-based prediction is based on measurements from 90 previous drive tests that were performed in the same scenario in [12]. The values for the weighting factor γ have to be chosen with respect to the metric's value range and its granularity.…”

Section: A Evaluation Scenariomentioning

confidence: 99%

Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks

Sliwa

Falkenberg

Liebig

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly upto-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resourceefficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.

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Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks

Cited by 27 publications

References 17 publications

Empirical Analysis of Client-Based Network Quality Prediction in Vehicular Multi-MNO Networks

Empirical Analysis of Client-Based Network Quality Prediction in Vehicular Multi-MNO Networks

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks

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