PBE-CC: Congestion Control via Endpoint-Centric, Physical-Layer Bandwidth Measurements

Wireless networks are becoming ever more sophisticated and overcrowded, imposing the most delay, jitter, and throughput damage to end-to-end network flows in today's internet. We therefore argue for fine-grained mobile endpoint-based wireless measurements to inform a precise congestion control algorithm through a well-defined API to the mobile's wireless physical layer. Our proposed congestion control algorithm is based on Physical-Layer Bandwidth measurements taken at the Endpoint (PBE-CC), and captures the latest 5G New Radio innovations that increase wireless capacity, yet create abrupt rises and falls in available wireless capacity that the PBE-CC sender can react to precisely and very rapidly. We implement a proof-of-concept prototype of the PBE measurement module on software-defined radios and the PBE sender and receiver in C. An extensive performance evaluation compares PBE-CC head to head against the leading cellular-aware and wireless-oblivious congestion control protocols proposed in the research community and in deployment, in mobile and static mobile scenarios, and over busy and quiet networks. Results show 6.3% higher average throughput than BBR, while simultaneously reducing 95th percentile delay by 1.8x

WaveFlex: A Smart Surface for Private CBRS Wireless Cellular Networks

We present the design and implementation of WaveFlex, the first smart surface that enhances Private LTE/5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the base station or mobile users, yet it remain compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio-based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks an operational Private LTE network running at full line rate. Results demonstrate an 8.50 dB average SNR gain, and an average throughput gain of 4.36 Mbps for a single small cell, and 3.19 Mbps for four small cells, in a realistic indoor office scenario.Comment: 15 page

AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.Comment: Accepted by IEEE Transactions on Mobile Computing 202

xD-track: Leveraging multi-dimensional information for passive wi-fi tracking

We describe the design and implementation of xD-Track, the first practical Wi-Fi based device-free localization system that employs a simultaneous and joint estimation of time-of-flight, angle-of-arrival, angle-of-departure, and Doppler shift to fully characterize the wireless channel between a sender and receiver. Using this full characterization, xD-Track introduces novel methods to measure and isolate the signal path that reflects off a person of interest, allowing it to localize a human with just a single pair of access points, or a single client-access point pair. Searching the multiple dimensions to accomplish the above is highly computationally burdensome, so xD-Track introduces novel methods to prune computational requirements, making our approach suitable for real-time person tracking. We implement xD-Track on the WARP software-defined radio platform and evaluate in a cluttered office environment. Experiments tracking people moving indoors demonstrate a 230% angle-of-arrival accuracy improvement and a 98% end-to-end tracking accuracy improvement over the state of the art localization scheme SpotFi, adapted for device-free localization. The general platform we propose can be easily extended for other applications including gesture recognition and Wi-Fi imaging to significantly improve performance

Biokinetics and Subchronic Toxic Effects of Oral Arsenite, Arsenate, Monomethylarsonic Acid, and Dimethylarsinic Acid in v-Ha-ras Transgenic (Tg.AC) Mice

Previous research demonstrated that 12-O-tetradecanoylphorbol-13-acetate (TPA) treatment increased the number of skin papillomas in v-Ha-ras transgenic (Tg.AC) mice that had received sodium arsenite [(As(III)] in drinking water, indicating that this model is useful for studying the toxic effects of arsenic in vivo. Because the liver is a known target of arsenic, we examined the pathophysiologic and molecular effects of inorganic and organic arsenical exposure on Tg.AC mouse liver in this study. Tg.AC mice were provided drinking water containing As(III), sodium arsenate [As(V)], monomethylarsonic acid [(MMA(V)], and 1,000 ppm dimethylarsinic acid [DMA(V)] at dosages of 150, 200, 1,500, or 1,000 ppm as arsenic, respectively, for 17 weeks. Control mice received unaltered water. Four weeks after initiation of arsenic treatment, TPA at a dose of 1.25 μg/200 μL acetone was applied twice a week for 2 weeks to the shaved dorsal skin of all mice, including the controls not receiving arsenic. In some cases arsenic exposure reduced body weight gain and caused mortality (including moribundity). Arsenical exposure resulted in a dose-dependent accumulation of arsenic in the liver that was unexpectedly independent of chemical species and produced hepatic global DNA hypomethylation. cDNA microarray and reverse transcriptase–polymerase chain reaction analysis revealed that all arsenicals altered the expression of numerous genes associated with toxicity and cancer. However, organic arsenicals [MMA(V) and DMA(V)] induced a pattern of gene expression dissimilar to that of inorganic arsenicals. In summary, subchronic exposure of Tg.AC mice to inorganic or organic arsenicals resulted in toxic manifestations, hepatic arsenic accumulation, global DNA hypomethylation, and numerous gene expression changes. These effects may play a role in arsenic-induced hepatotoxicity and carcinogenesis and may be of particular toxicologic relevance

The Wnt inhibitory factor 1 restoration in prostate cancer cells was associated with reduced tumor growth, decreased capacity of cell migration and invasion and a reversal of epithelial to mesenchymal transition

<p>Abstract</p> <p>Background</p> <p>Aberrations in the Wnt pathway have been reported to be involved in the metastasis of prostate cancer (PCa) to bone. We investigated the effect and underlying mechanism of a naturally-occurring Wnt inhibitor, WIF1, on the growth and cellular invasiveness of a bone metastatic PCa cell line, PC3.</p> <p>Results</p> <p>The WIF1 gene promoter was hypermethylated and its expression down-regulated in the majority (7 of 8) of PCa cell lines. Restoration of WIF1 expression in PC-3 cells resulted in a decreased cell motility and invasiveness via up-regulation of epithelial markers (E-cadherin, Keratin-8 and-18), down-regulation of mesenchymal markers (N-cadherin, Fibronectin and Vimentin) and decreased activity of MMP-2 and -9. PC3 cells transfected with WIF1 consistently demonstrated reduced expression of Epithelial-to-Mesenchymal Transition (EMT) transcription factors, Slug and Twist, and a change in morphology from mesenchymal to epithelial. Moreover, WIF1 expression significantly reduced tumor growth by approximately 63% in a xenograft mouse model. This was accompanied by an increased expression of E-cadherin and Keratin-18 and a decreased expression of vimentin in tumor tissues.</p> <p>Conclusion</p> <p>These data suggest that WIF1 regulates tumor invasion through EMT process and thus, may play an important role in controlling metastatic disease in PCa patients. Blocking Wnt signaling in PCa by WIF1 may represent a novel strategy in the future to reduce metastatic disease burden in PCa patients.</p

Global Gene Expression Associated with Hepatocarcinogenesis in Adult Male Mice Induced by in Utero Arsenic Exposure

Our previous work has shown that exposure to inorganic arsenic in utero produces hepatocellular carcinoma (HCC) in adult male mice. To explore further the molecular mechanisms of transplacental arsenic hepatocarcinogenesis, we conducted a second arsenic transplacental carcinogenesis study and used a genomewide microarray to profile arsenic-induced aberrant gene expression more extensively. Briefly, pregnant C3H mice were given drinking water containing 85 ppm arsenic as sodium arsenite or unaltered water from days 8 to 18 of gestation. The incidence of HCC in adult male offspring was increased 4-fold and tumor multiplicity 3-fold after transplacental arsenic exposure. Samples of normal liver and liver tumors were taken at autopsy for genomic analysis. Arsenic exposure in utero resulted in significant alterations (p < 0.001) in the expression of 2,010 genes in arsenic-exposed liver samples and in the expression of 2,540 genes in arsenic-induced HCC. Ingenuity Pathway Analysis revealed that significant alterations in gene expression occurred in a number of biological networks, and Myc plays a critical role in one of the primary networks. Real-time reverse transcriptase–polymerase chain reaction and Western blot analysis of selected genes/proteins showed > 90% concordance. Arsenic-altered gene expression included activation of oncogenes and HCC biomarkers, and increased expression of cell proliferation–related genes, stress proteins, and insulin-like growth factors and genes involved in cell–cell communications. Liver feminization was evidenced by increased expression of estrogen-linked genes and altered expression of genes that encode gender-related metabolic enzymes. These novel findings are in agreement with the biology and histology of arsenic-induced HCC, thereby indicating that multiple genetic events are associated with transplacental arsenic hepatocarcinogenesis

Enhancing the performance of Wi-Fi system by exploiting physical layer information

Wi-Fi network has been ubiquitous nowadays and has changed our lifestyle. As a communication system, Wi-Fi delivers more than 50% of IP traffic. Consequently, the demand for higher transmission capacity has been increasing continuously and rapidly. The wireless spectrum for Wi-Fi communication is, however, finite. Therefore, one important research direction is to fully utilize the limited resources and at the same time improve the transmission throughput for Wi-Fi network. On the other hand, a lot of emerging applications, e.g., indoor navigation, human tracking, device free gesture control, are built on top of existing commercial Wi-Fi infrastructures to provide a variety of functionalities except communication. All those applications rely on Wi-Fi's capability of sensing the physical world: strong sensing capability will significantly improve the performance of existing applications and extend the scope of potential applications. Therefore, another important research direction is to enhance the sensing capability of existing Wi-Fi infrastructure. This thesis focuses on these two directions and exploits rich information in the PHY layer to build a betterWi-Fi system that has higher communication speed and stronger sensing capability. We observe that modern widebandWi-Fi communication has unevenly distributed bit BERs in a packet because of the frequency selective fading. Based on such an observation, we propose UnPKT, a system that can unequally protect Wi-Fi packet bits according to their BERs. By doing so, we can best match the effective transmission rate of each bit to channel condition, and improve throughput. We derive an accurate relationship between the frequency selective channel condition and the decoded packet bit BERs, all the way through the complex 802.11 PHY layer. A cluster-based protection scheme is proposed to protect packet bits using different MAC-layer FEC redundancies based on bit-wise BER estimation to augment wide band 802.11 transmissions. UnPKT is software-implementable and compatible with the existing 802.11 architecture. Extensive evaluations based on Atheros 9580 NICs and GNU-Radio platforms show the effectiveness of our design. UnPKT can achieve a significant goodput improvement over state-of-the-art approaches. When sensing the physical world usingWi-Fi, power delay profile is widely used in motionor localization-based applications as it characterizes multipath channel features. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. Therefore, we build Splicer, a software-based system that derives high resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. A set of key techniques has also been proposed to separate the mixed hardware errors from the collected CSI measurements. Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than 2m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of 0.95m.DOCTOR OF PHILOSOPHY (SCE