SoftCast: Clean-slate Scalable Wireless Video

Video broadcast and mobile video challenge the conventional wireless design. In broadcast and mobile scenarios the bit rate supported by the channel differs across receivers and varies quickly over time. The conventional design however forces the source to pick a single bit rate and degrades sharply when the channel cannot not support the chosen bit rate. This paper presents SoftCast, a clean-slate design for wireless video where the source transmits one video stream that each receiver decodes to a video quality commensurate with its specific instantaneous channel quality. To do so, SoftCast ensures the samples of the digital video signal transmitted on the channel are linearly related to the pixels' luminance. Thus, when channel noise perturbs the transmitted signal samples, the perturbation naturally translates into approximation in the original video pixels. Hence, a receiver with a good channel (low noise) obtains a high fidelity video, and a receiver with a bad channel (high noise) obtains a low fidelity video. We implement SoftCast using the GNURadio software and the USRP platform. Results from a 20-node testbed show that SoftCast improves the average video quality (i.e., PSNR) across broadcast receivers in our testbed by up to 5.5dB. Even for a single receiver, it eliminates video glitches caused by mobility and increases robustness to packet loss by an order of magnitude

Physical Layer Wireless Security Made Fast and Channel Independent

There is a growing interest in physical layer security. Recent work has demonstrated that wireless devices can generate a shared secret key by exploiting variations in their channel. The rate at which the secret bits are generated, however, depends heavily on how fast the channel changes. As a result, existing schemes have a low secrecy rate and are mainly applicable to mobile environments. In contrast, this paper presents a new physical-layer approach to secret key generation that is both fast and independent of channel variations. Our approach makes a receiver jam the signal in a manner that still allows it to decode the data, yet prevents other nodes from decoding. Results from a testbed implementation show that our method is significantly faster and more accurate than state of the art physical-layer secret key generation protocols. Specifically, while past work generates up to 44 secret bits/s with a 4% bit disagreement between the two devices, our design has a secrecy rate of 3-18 Kb/s with 0% bit disagreement

ChitChat: Making Video Chat Robust to Packet Loss

Video chat is increasingly popular among Internet users. Often, however, chatting sessions suffer from packet loss, which causes video outage and poor quality. Existing solutions however are unsatisfying. Retransmissions increase the delay and hence can interact negatively with the strict timing requirements of interactive video. FEC codes introduce extra overhead and hence reduce the bandwidth available for video data even in the absence of packet loss. This paper presents ChitChat, a new approach for reliable video chat that neither delays frames nor introduces bandwidth overhead. The key idea is to ensure that the information in each packet describes the whole frame. As a result, even when some packets are lost, the receiver can still use the received packets to decode a smooth version of the original frame. This reduces frame loss and the resulting video freezes and improves the perceived video quality. We have implemented ChitChat and evaluated it over multiple Internet paths. In comparison to Windows Live Messenger 2009, our method reduces the occurrences of video outage events by more than an order of magnitude

ZigZag Decoding: Combating Hidden Terminals in Wireless Networks

This paper presents ZigZag, an 802.11 receiver that combats hidden terminals. ZigZag exploits 802.11 retransmissions which, in the case of hidden terminals, cause successive collisions. Due to asynchrony, these collisions have different interference-free stretches at their start, which ZigZag uses to bootstrap its decoding. ZigZag makes no changes to the 802.11 MAC and introduces no overhead when there are no collisions. But, when senders collide, ZigZag attains the same throughput as if the colliding packets were a priori scheduled in separate time slots. We build a prototype of ZigZag in GNU Radio. In a testbed of 14 USRP nodes, ZigZag reduces the average packet loss rate at hidden terminals from 82.3% to about 0.7%

ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Since humans typically rely on such global structures in classifying images, the process makes the network mode compatible with human perception. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks.Comment: ICML 201

iJam: Jamming Oneself for Secure Wireless Communication

Wireless is inherently less secure than wired networks because of its broadcast nature. Attacks that simply snoop on the wireless medium successfully defeat the security of even 802.11 networks using the most recent security standards (WPA2-PSK). In this paper we ask the following question: Can we prevent this kind of eavesdropping from happening? If so, we can potentially defeat the entire class of attacks that rely on snooping. This paper presents iJam, a PHY-layer protocol for OFDM-based wireless systems. iJam ensures that an eavesdropper cannot successfully demodulate a wireless signal not intended for it. To achieve this iJam strategically introduces interference that prevents an eavesdropper from decoding the data, while allowing the intended receiver to decode it. iJam exploits the properties of 802.11â s OFDM signals to ensure that an eavesdropper cannot even tell which parts of the signal are jammed. We implement iJam and evaluate it in a testbed of GNURadios with an 802.11-like physical layer. We show that iJam makes the data bits at the adversary look random, i.e., the BER becomes close to 50%, whereas the receiver can perfectly decode the data

SoftCast

The focus of this demonstration is the performance of streaming video over the mobile wireless channel. We compare two schemes: the standard approach to video which transmits H.264/AVC-encoded stream over 802.11-like PHY, and SoftCast -- a clean-slate design for wireless video where the source transmits one video stream that each receiver decodes to a video quality commensurate with its specific instantaneous channel quality

Iterative Collaborative Ranking of Customers and Providers

This paper introduces a new application: predicting the Internet provider-customer market. We cast the problem in the collaborative filtering framework, where we use current and past customer-provider relationships to compute for each Internet customer a ranking of potential future service providers. Furthermore, for each Internet service provider (ISP), we rank potential future customers. We develop a novel iterative ranking algorithm that draws inspiration from several sources, including collaborative filtering, webpage ranking, and kernel methods. Further analysis of our algorithm shows that it can be formulated in terms of an affine eigenvalue problem. Experiments on the actual Internet customer-provider data show promising results

Multi-Person Motion Tracking via RF Body Reflections

Recently, we have witnessed the emergence of technologies that can localize a user and track her gestures based purely on radio reflections off the person's body. These technologies work even if the user is behind a wall or obstruction. However, for these technologies to be fully practical, they need to address major challenges such as scaling to multiple people, accurately localizing them and tracking their gestures, and localizing static users as opposed to requiring the user to move to be detectable. This paper presents WiZ, the first multi-person centimeter-scale motion tracking system that pinpoints people's locations based purely on RF reflections off their bodies. WiZ can also locate static users by sensing minute changes in their RF reflections due to breathing. Further, it can track concurrent gestures made by different individuals, even when they carry no wireless device on them. We implement a prototype of WiZ and show that it can localize up to five users each with a median accuracy of 8-18 cm and 7-11 cm in the x and y dimensions respectively. WiZ can also detect 3D pointing gestures of multiple users with a median orientation error of 8 -16 degrees for each of them. Finally, WiZ can track breathing motion and output the breath count of multiple people with high accuracy

On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond

Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance.Comment: ECCV 202