Passive monitoring and geo-based prediction of mobile network vehicle-to-server communication

Schmid, Josef; Hes, Philipp; Hos, Alfred; Schuller, Björn

doi:10.1109/iwcmc.2018.8450395

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Other authors, e.g., Martínez et al [62] and Pögel et al [79] showed approaches capable of measuring features from different input sources like the network interface and the low-level model parameters. The paper of Schmid et al [94] presented a tool, which is able to store a dataset on disk as well as to process the recorded samples of LTE, TCP, and GPS 194:24 values immediately. Of course, there are a number of tools like Netradar [71] or SamKnows [92] that provide performance measurements at the application level, but since these do not provide TCP data, they have not been studied in this work.…”

Section: Available Datasets and Measurement Toolsmentioning

confidence: 99%

A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks

2021

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Network communication has become a part of everyday life, and the interconnection among devices and people will increase even more in the future. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is quite a challenging task. In this survey, we provide an extensive insight into the literature on Transmission Control Protocol throughput prediction. The goal is to provide an overview of the used techniques and to elaborate on open aspects and white spots in this area. We assessed more than 35 approaches spanning from equation-based over various time smoothing to modern learning and location smoothing methods. In addition, different error functions for the evaluation of the approaches as well as publicly available recording tools and datasets are discussed. To conclude, we point out open challenges especially looking in the area of moving mobile network clients. The use of throughput prediction not only enables a more efficient use of the available bandwidth, the techniques shown in this work also result in more robust and stable communication.

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Section: Available Datasets and Measurement Toolsmentioning

confidence: 99%

A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks

2021

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“…For the evaluation of the deep learning models' location, independent datasets at two locations are leveraged. The first dataset comprises a new measurement campaign at a test area used in [2], [11]. It is located in the vicinity of the city of Amberg, Bavaria, in Germany.…”

Section: Datasetsmentioning

confidence: 99%

“…As described in the previous work, this test round is selected, because of its variety of traffic scenarios, such as urban, interurban, and motorway roads. In comparison with the former Amberg dataset [2], [11], the measurement setup is changed to enable the Multiple-Input and Multiple-Output (MIMO) capability of the used Qualcomm modems. Besides this modification, the setup remained the same, including the TCPAnalyzer tool chain for conducting the measurements with a sampling frequency of 3.33 Hz.…”

Section: Datasetsmentioning

confidence: 99%

A Deep Learning Approach for Location Independent Throughput Prediction

Schmid

Schneider

Hob

et al. 2019

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)

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Mobile communication has become a part of everyday life and is considered to support reliability and safety in traffic use cases such as conditionally automated driving. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is still a challenging task while on the move. Whereas most approaches in this research field rely on historical data measurements, mapped to the corresponding coordinates in the area of interest, this paper proposes a throughput prediction method that focuses on a location independent approach. In order to compensate the missing positioning information, mainly used for spatial clustering, our model uses low-level mobile network parameters, improved by additional feature engineering to retrieve abstracted location information, e. g., surrounding building size and street type. Thus, the major advantage of our method is the applicability to new regions without the prerequisite of conducting an extensive measurement campaign in advance. Therefore, we embed analysis results for underlying temporal relations in the design of different deep neuronal network types. Finally, model performances are evaluated and compared to traditional models, such as the support vector or random forest regression, which were harnessed in previous investigations.

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