Commute path bandwidth traces from 3G networks: analysis and applications

By Håkon Riiser, Paul Vigmostad, C. Griwodz, Pål Halvorsen. ACM SIGMM Conference on Multimedia Systems, 2013

384 citations recorded by the source corpus.

In this dataset paper, we present and make available real-world measurements of the throughput that was achieved at the application layer when adaptive HTTP streaming was performed over 3G networks using mobile devices. For the streaming sessions, we used popular commute routes in and around Oslo (Norway) traveling with different types of public transportation (metro, tram, train, bus and ferry). We also have a few logs using a car. Each log provides a times-tamp, GPS coordinates and the measured number of bytes downloaded for approximately every second of the route. The dataset can be used in several ways, but the most obvious application is to emulate the same network bandwidth behavior (on specific geographical positions) for repeated experiments.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/091578afc211f9c871c1af90b06cdcb8dadd37fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Sociology and Political Science

Citations in the corpus

Neural Adaptive Video Streaming with Pensieve (2017, methodology): “Network traces: To evaluate Pensieve and state-of-the-art ABR algorithms on realistic network conditions, we created a corpus of network traces by combining several public datasets: a broadband dataset provided by the FCC [10] and a 3G/HSDPA mobile dataset collected in Norway [37].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “The simulation results based on the mobile dataset from [68] show that the proposed DQL can improve the average QoE up to 25% compared with the bitrate control scheme [69].”
BOLA: Near-optimal bitrate adaptation for online videos (2016, background): “One trace was excluded because it had an average bandwidth for m in [1 ,M ] do 6: x ← download time ( n,t (cid:48) ,m ) 7: x δ ← (cid:98) x/δ (cid:99) · δ 8:”
Oboe: auto-tuning video ABR algorithms to network conditions (2018, methodology): “To understand how Oboe works on throughput datasets beyond those discussed in §4.2, we evaluated Oboe on two other datasets, FCC [8] and HSDPA [46] that have been used in recent work [39, 59].”
HTTP/2-Based Adaptive Streaming of HEVC Video Over 4G/LTE Networks (2016, background): “[12], several properties are logged, among which the GPS coordi-”
Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks (2018, methodology): “Moreover, we used real bandwidth traces [11], [12], [13] to train our RL agent, and we set the packet drop probability to be 10%.”