International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Machine Learning
Machine Learning
Measurements of In-Motion 802.11 Networking
WMCSA '06 Proceedings of the Seventh IEEE Workshop on Mobile Computing Systems & Applications
A measurement study of vehicular internet access using in situ Wi-Fi networks
Proceedings of the 12th annual international conference on Mobile computing and networking
Understanding wifi-based connectivity from moving vehicles
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Feasibility of content dissemination between devices in moving vehicles
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Study of subjective and objective quality assessment of video
IEEE Transactions on Image Processing
Metrics for evaluating video streaming quality in lossy IEEE 802.11 wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Performance comparison of 3G and metro-scale WiFi for vehicular network access
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Temporal quality assessment for mobile videos
Proceedings of the 18th annual international conference on Mobile computing and networking
Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flows
NDM '13 Proceedings of the Third International Workshop on Network-Aware Data Management
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In this paper, we characterize the performance of HD video streaming in 802.11n WLANs under user mobility. We conducted experiments in QuRiNet, a large-scale outdoor wireless testbed that experiences little electromagnetic interference. We observe the variation in video quality with the variance of both speed of a mobile user and his distance from access point (AP). Using subjective scores and objective video quality assessment metrics, we build a non-linear regression model to estimate video quality based on user speed and distance. An ensemble machine learning kernel, bagging, is used in conjunction with Reduced Error Pruning Decision Trees to build a non-linear prediction model that scores 69% correlation with video quality. Overall, we find that distance has larger impact on video quality than speed. However, the physical factors such as speed and distance cannot be used in isolation to estimate video quality accurately.