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Networking research has witnessed a renaissance from exploring the seemingly unlimited predictive power of machine learning (ML) models. One such promising direction is throughput prediction – accurately predicting the network bandwidth or achievable throughput of a client in real time using ML models can enable a wide variety of network applications to proactively adapt their behavior to the changing network dynamics to potentially achieve significantly improved QoE. Motivated by the key role of newer generations of cellular networks in supporting the new generation of latency-critical applications such as AR/MR, in this work, we focus on accurate throughput prediction in cellular networks at fine time-scales, e.g., in the order of 100 ms. Through a 4-day, 1000+ km driving trip, we collect a dataset of fine-grained throughput measurements under driving across all three major US operators. Using the collected dataset, we conduct the first feasibility study of predicting fine-grained application throughput in real-world cellular networks with mixed LTE/5G technologies. Our analysis shows that popular ML models previously claimed to predict well for various wireless networks scenarios (e.g., WiFi or singletechnology network such as LTE only) do not predict well under app-centric metrics such as ARE95 and PARE10. Further, we uncover the root cause for the poor prediction accuracy of ML models as the inherent conflicting sample sequences in the fine-grained cellular network throughput data.
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A Lightweight Scheme for Rapid and Accurate WiFi Path Characterization
WiFi serves as one of the key mechanisms for wireless access for mobile devices whether at home, on travel, or during normal day-to- day activities. Unfortunately, the perceived high bandwidth and low cost of WiFi is often tempered with varying degrees of quality. Compounding this further, existing techniques for assessing network performance are often expensive in terms of time, bandwidth, and energy making them ill-suited for widespread, longitudinal deployment. In this paper, we propose Fast Mobile Network Characterization (FMNC) to address this shortcoming. FMNC uses sliced, structured, and reordered packet sequences along with an awareness of frame aggregation to rapidly characterize available bandwidth. FMNC does this within the context of a single HTTP GET, consuming less than 100 KB on the downlink with resolution of the path characteristics typically occurring in under 250 ms. We demonstrate the performance of FMNC through extensive lab experiments under a variety of configuration scenarios.
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- Award ID(s):
- 1718405
- PAR ID:
- 10095323
- Date Published:
- Journal Name:
- Proc. of ICCCN
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Networking research has witnessed a renaissance from exploring the seemingly unlimited predictive power of machine learning (ML) models. One such promising direction is throughput prediction – accurately predicting the network bandwidth or achievable throughput of a client in real time using ML models can enable a wide variety of network applications to proactively adapt their behavior to the changing network dynamics to potentially achieve significantly improved QoE. Motivated by the key role of newer generations of cellular networks in supporting the new generation of latency-critical applications such as AR/MR, in this work, we focus on accurate throughput prediction in cellular networks at fine time-scales, e.g., in the order of 100 ms. Through a 4-day, 1000+ km driving trip, we collect a dataset of fine-grained throughput measurements under driving across all three major US operators. Using the collected dataset, we conduct the first feasibility study of predicting fine-grained application throughput in real-world cellular networks with mixed LTE/5G technologies. Our analysis shows that popular ML models previously claimed to predict well for various wireless networks scenarios (e.g., WiFi or singletechnology network such as LTE only) do not predict well under app-centric metrics such as ARE95 and PARE10. Further, we uncover the root cause for the poor prediction accuracy of ML models as the inherent conflicting sample sequences in the finegrained cellular network throughput data.more » « less
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null (Ed.)Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios.more » « less
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When people connect to the Internet with their mobile devices, they do not often think about the security of their data; however, the prevalence of rogue access points has taken advantage of a false sense of safety in unsuspecting victims. This paper analyzes the methods an attacker would use to create rogue WiFi access points using software-defined radio (SDR). To construct a rogue access point, a few essential layers of WiFi need simulation: the physical layer, link layer, network layer, and transport layer. Radio waves carrying WiFi packets, transmitted between two Universal Software Radio Peripherals (USRPs), emulate the physical layer. The link layer consists of the connection between those same USRPs communicating directly to each other, and the network layer expands on this communication by using the network tunneling/network tapping (TUN/TAP) interfaces to tunnel IP packets between the host and the access point. Finally, the establishment of the transport layer constitutes transceiving the packets that pass through the USRPs. In the end, we found that creating a rogue access point and capturing the stream of data from a fabricated "victim" on the Internet was effective and cheap with SDRs as inexpensive as $20 USD. Our work aims to expose how a cybercriminal could carry out an attack like this in order to prevent and defend against them in the future.more » « less
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In this paper, we study how to support high-quality immer- sive multiplayer VR on commodity mobile devices. First, we perform a scaling experiment that shows simply replicating the prior-art 2-layer distributed VR rendering architecture to multiple players cannot support more than one player due to the linear increase in network bandwidth requirement. Second, we propose to exploit the similarity of background environment (BE) frames to reduce the bandwidth needed for prefetching BE frames from the server, by caching and reusing similar frames. We nd that there is often little sim- ilarly between the BE frames of even adjacent locations in the virtual world due to a “near-object” e ect. We propose a novel technique that splits the rendering of BE frames between the mobile device and the server that drastically enhances the similarity of the BE frames and reduces the network load from frame caching. Evaluation of our imple- mentation on top of Unity and Google Daydream shows our new VR framework, Coterie, reduces per-player network requirement by 10.6X-25.7X and easily supports 4 players for high-resolution VR apps on Pixel 2 over 802.11ac, with 60 FPS and under 16ms responsiveness.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCCN.2018.8487433
