194 research outputs found
Panda: Neighbor Discovery on a Power Harvesting Budget
Object tracking applications are gaining popularity and will soon utilize
Energy Harvesting (EH) low-power nodes that will consume power mostly for
Neighbor Discovery (ND) (i.e., identifying nodes within communication range).
Although ND protocols were developed for sensor networks, the challenges posed
by emerging EH low-power transceivers were not addressed. Therefore, we design
an ND protocol tailored for the characteristics of a representative EH
prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND
accounting for unique prototype characteristics (i.e., energy costs for
transmission/reception, and transceiver state switching times/costs). Then, we
present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in
which nodes transition between the sleep, receive, and transmit states. We
analyze \name and select its parameters to maximize the ND rate subject to a
homogeneous power budget. We also present Panda-D, designed for non-homogeneous
EH nodes. We perform extensive testbed evaluations using the prototypes and
study various design tradeoffs. We demonstrate a small difference (less then
2%) between experimental and analytical results, thereby confirming the
modeling assumptions. Moreover, we show that Panda improves the ND rate by up
to 3x compared to related protocols. Finally, we show that Panda-D operates
well under non-homogeneous power harvesting
SoK: Inference Attacks and Defenses in Human-Centered Wireless Sensing
Human-centered wireless sensing aims to understand the fine-grained
environment and activities of a human using the diverse wireless signals around
her. The wireless sensing community has demonstrated the superiority of such
techniques in many applications such as smart homes, human-computer
interactions, and smart cities. Like many other technologies, wireless sensing
is also a double-edged sword. While the sensed information about a human can be
used for many good purposes such as enhancing life quality, an adversary can
also abuse it to steal private information about the human (e.g., location,
living habits, and behavioral biometric characteristics). However, the
literature lacks a systematic understanding of the privacy vulnerabilities of
wireless sensing and the defenses against them.
In this work, we aim to bridge this gap. First, we propose a framework to
systematize wireless sensing-based inference attacks. Our framework consists of
three key steps: deploying a sniffing device, sniffing wireless signals, and
inferring private information. Our framework can be used to guide the design of
new inference attacks since different attacks can instantiate these three steps
differently. Second, we propose a defense-in-depth framework to systematize
defenses against such inference attacks. The prevention component of our
framework aims to prevent inference attacks via obfuscating the wireless
signals around a human, while the detection component aims to detect and
respond to attacks. Third, based on our attack and defense frameworks, we
identify gaps in the existing literature and discuss future research
directions
Optimizing Sectorized Wireless Networks: Model, Analysis, and Algorithm
Future wireless networks need to support the increasing demands for high data
rates and improved coverage. One promising solution is sectorization, where an
infrastructure node (e.g., a base station) is equipped with multiple sectors
employing directional communication. Although the concept of sectorization is
not new, it is critical to fully understand the potential of sectorized
networks, such as the rate gain achieved when multiple sectors can be
simultaneously activated. In this paper, we focus on sectorized wireless
networks, where sectorized infrastructure nodes with beam-steering capabilities
form a multi-hop mesh network for data forwarding and routing. We present a
sectorized node model and characterize the capacity region of these sectorized
networks. We define the flow extension ratio and the corresponding
sectorization gain, which quantitatively measure the performance gain
introduced by node sectorization as a function of the network flow. Our
objective is to find the optimal sectorization of each node that achieves the
maximum flow extension ratio, and thus the sectorization gain. Towards this
goal, we formulate the corresponding optimization problem and develop an
efficient distributed algorithm that obtains the node sectorization under a
given network flow with an approximation ratio of 2/3. Through extensive
simulations, we evaluate the sectorization gain and the performance of the
proposed algorithm in various network scenarios with varying network flows. The
simulation results show that the approximate sectorization gain increases
sublinearly as a function of the number of sectors per node
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Algorithms and Experimentation for Future Wireless Networks: From Internet-of-Things to Full-Duplex
Future and next-generation wireless networks are driven by the rapidly growing wireless traffic stemming from diverse services and applications, such as the Internet-of-Things (IoT), virtual reality, autonomous vehicles, and smart intersections. Many of these applications require massive connectivity between IoT devices as well as wireless access links with ultra-high bandwidth (Gbps or above) and ultra-low latency (10ms or less). Therefore, realizing the vision of future wireless networks requires significant research efforts across all layers of the network stack. In this thesis, we use a cross-layer approach and focus on several critical components of future wireless networks including IoT systems and full-duplex (FD) wireless, and on experimentation with advanced wireless technologies in the NSF PAWR COSMOS testbed.
First, we study tracking and monitoring applications in the IoT and focus on ultra-low-power energy harvesting networks. Based on realistic hardware characteristics, we design and optimize Panda, a centralized probabilistic protocol for maximizing the neighbor discovery rate between energy harvesting nodes under a power budget. Via testbed evaluation using commercial off-the-shelf energy harvesting nodes, we show that Panda outperforms existing protocols by up to 3x in terms of the neighbor discovery rate. We further explore this problem and consider a general throughput maximization problem among a set of heterogeneous energy-constrained ultra-low-power nodes. We analytically identify the theoretical fundamental limits of the rate at which data can be exchanged between these nodes, and design the distributed probabilistic protocol, EconCast, which approaches the maximum throughput in the limiting sense. Performance evaluations of EconCast using both simulations and real-world experiments show that it achieves up to an order of magnitude higher throughput than Panda and other known protocols.
We then study FD wireless - simultaneous transmission and reception at the same frequency - a key technology that can significantly improve the data rate and reduce communication latency by employing self-interference cancellation (SIC). In particular, we focus on enabling FD on small-form-factor devices leveraging the technique of frequency-domain equalization (FDE). We design, model, and optimize the FDE-based RF canceller, which can achieve >50dB RF SIC across 20MHz bandwidth, and experimentally show that our prototyped FD radios can achieve a link-level throughput gain of 1.85-1.91x. We also focus on combining FD with phased arrays, employing optimized transmit and receive beamforming, where the spatial degrees of freedom in multi-antenna systems are repurposed to achieve wideband RF SIC. Moving up in the network stack, we study heterogeneous networks with half-duplex and FD users, and develop the novel Hybrid-Greedy Maximum Scheduling (H-GMS) algorithm, which achieves throughput optimality in a distributed manner. Analytical and simulation results show that H-GMS achieves 5-10x better delay performance and improved fairness compared with state-of-the-art approaches.
Finally, we described experimentation and measurements in the city-scale COSMOS testbed being deployed in West Harlem, New York City. COSMOS' key building blocks include software-defined radios, millimeter-wave radios, a programmable optical network, and edge cloud, and their convergence will enable researchers to remotely explore emerging technologies in a real world environment. We provide a brief overview of the testbed and focus on experimentation with advanced technologies, including the integrating of open-access FD radios in the testbed and a pilot study on converged optical-wireless x-haul networking for cloud radio access networks (C-RANs). We also present an extensive 28GHz channel measurements in the testbed area, which is a representative dense urban canyon environment, and study the corresponding signal-to-noise ratio (SNR) coverage and achievable data rates. The results of this part helped drive and validate the design of the COSMOS testbed, and can inform further deployment and experimentation in the testbed.
In this thesis, we make several theoretical and experimental contributions to ultra-low-power energy harvesting networks and the IoT, and FD wireless. We also contribute to the experimentation and measurements in the COSMOS advanced wireless testbed. We believe that these contributions are essential to connect fundamental theory to practical systems, and ultimately to real-world applications, in future wireless networks
On the detection of functionally coherent groups of protein domains with an extension to protein annotation
<p>Abstract</p> <p>Background</p> <p>Protein domains coordinate to perform multifaceted cellular functions, and domain combinations serve as the functional building blocks of the cell. The available methods to identify functional domain combinations are limited in their scope, e.g. to the identification of combinations falling within individual proteins or within specific regions in a translated genome. Further effort is needed to identify groups of domains that span across two or more proteins and are linked by a cooperative function. Such functional domain combinations can be useful for protein annotation.</p> <p>Results</p> <p>Using a new computational method, we have identified 114 groups of domains, referred to as domain assembly units (DASSEM units), in the proteome of budding yeast <it>Saccharomyces cerevisiae</it>. The units participate in many important cellular processes such as transcription regulation, translation initiation, and mRNA splicing. Within the units the domains were found to function in a cooperative manner; and each domain contributed to a different aspect of the unit's overall function. The member domains of DASSEM units were found to be significantly enriched among proteins contained in transcription modules, defined as genes sharing similar expression profiles and presumably similar functions. The observation further confirmed the functional coherence of DASSEM units. The functional linkages of units were found in both functionally characterized and uncharacterized proteins, which enabled the assessment of protein function based on domain composition.</p> <p>Conclusion</p> <p>A new computational method was developed to identify groups of domains that are linked by a common function in the proteome of <it>Saccharomyces cerevisiae</it>. These groups can either lie within individual proteins or span across different proteins. We propose that the functional linkages among the domains within the DASSEM units can be used as a non-homology based tool to annotate uncharacterized proteins.</p
MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars
Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars
play a critical role in many of the advanced driver assistance systems (ADAS)
featured on today's vehicles. While previous works have demonstrated (only)
successful false-positive spoofing attacks against these sensors, all but one
assumed that an attacker had the runtime knowledge of the victim radar's
configuration. In this work, we introduce MadRadar, a general black-box radar
attack framework for automotive mmWave FMCW radars capable of estimating the
victim radar's configuration in real-time, and then executing an attack based
on the estimates. We evaluate the impact of such attacks maliciously
manipulating a victim radar's point cloud, and show the novel ability to
effectively `add' (i.e., false positive attacks), `remove' (i.e., false
negative attacks), or `move' (i.e., translation attacks) object detections from
a victim vehicle's scene. Finally, we experimentally demonstrate the
feasibility of our attacks on real-world case studies performed using a
real-time physical prototype on a software-defined radio platform
Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer
BackgroundIdentifying predictive markers for breast cancer (BC) prognosis and immunotherapeutic responses remains challenging. Recent findings indicate that N7-methylguanosine (m7G) modification and the tumor microenvironment (TME) are critical for BC tumorigenesis and metastasis, suggesting that integrating m7G modifications and TME cell characteristics could improve the predictive accuracy for prognosis and immunotherapeutic responses.MethodsWe utilized bulk RNA-sequencing data from The Cancer Genome Atlas Breast Cancer Cohort and the GSE42568 and GSE146558 datasets to identify BC-specific m7G-modification regulators and associated genes. We used multiple m7G databases and RNA interference to validate the relationships between BC-specific m7G-modification regulators (METTL1 and WDR4) and related genes. Single-cell RNA-sequencing data from GSE176078 confirmed the association between m7G modifications and TME cells. We constructed an m7G-TME classifier, validated the results using an independent BC cohort (GSE20685; n = 327), investigated the clinical significance of BC-specific m7G-modifying regulators by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and performed tissue-microarray assays on 192 BC samples.ResultsImmunohistochemistry and RT-qPCR results indicated that METTL1 and WDR4 overexpression in BC correlated with poor patient prognosis. Moreover, single-cell analysis revealed relationships between m7G modification and TME cells, indicating their potential as indicators of BC prognosis and treatment responses. The m7G-TME classifier enabled patient subgrouping and revealed significantly better survival and treatment responses in the m7Glow+TMEhigh group. Significant differences in tumor biological functions and immunophenotypes occurred among the different subgroups.ConclusionsThe m7G-TME classifier offers a promising tool for predicting prognosis and immunotherapeutic responses in BC, which could support personalized therapeutic strategies
Analysis of dynamic stability for wind turbine blade under fluid-structure interaction
Aiming at improving vibration performance of 1.5 MW wind turbine blades, the theoretical model and the calculation process of vibration problems under geometric nonlinearity and unidirectional fluid-structure interaction (UFSI) were presented. The dynamic stability analysis on a 1.5 MW wind turbine blade was carried out. Both the maximum brandish displacement and the maximum Mises stress increase nonlinearly with the increase of wind speed. The influences of turbulent effect, wind shear effect and their joint effect on displacement and stress increase sequentially. Furthermore, the stability critical curves are calculated and analyzed. As a result, the stability region is established where the wind turbine blade can run safely
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