arXiv subjects

Elizabeth Belding

Publications and source records attributed to Elizabeth Belding.

At least 19 recordsLinked to original sources

arXiv2026

Fake It No More: Evaluating L4S with SCReAM on Video Traffic

The growing interest in Low Latency, Low Loss, and Scalable Throughput (L4S) reflects the need for lower latency in interactive multimedia applications. In this paper, we use an open-source DualPI2 implementation over the Mahimahi emulator to evaluate the impact of L4S on SCReAM congestion controlled video traffic. To do so, we augment the SCReAM BW tool with a video codec, enabling the generation of video traffic in addition to its original synthetic RTP mode. We evaluate both network-level and Quality of Experience (QoE) metrics on a mobile network trace, under random packet loss, and with different motion-complexity levels. In our baseline scenario, L4S reduces the median per-run $95^{th}$ percentile queue delay by 35%, at the cost of a 42% drop in sender throughput. Under 1% packet loss, L4S yields more pronounced QoE gains compared to the lossless scenario, despite narrower network-level benefits. Across video content complexities, L4S also maintains more stable QoE than Classic. These results underscore the importance of evaluating QoE alongside network-level metrics when assessing the effect of L4S on end-user application performance.

cs.NI↗

arXiv2026

Where's Waldo Library? Using Reverse IP Geolocation to Identify Library IPs

Community anchor institutions (CAIs), such as libraries, schools, and community centers, are critical for providing Internet access to un- or under-served individuals and communities. Because many of these institutions are themselves under-provisioned, analyzing the reliability and quality of their Internet service is important. Doing so at scale requires knowing the IP addresses of these institutions so that broadband measurement and policy evaluation can occur. Unfortunately, these IPs are not systematically documented. As a first step towards widespread, scalable evaluation of CAI Internet connectivity, this paper presents Reverse IP Geolocation (RG), a new framework to infer IP addresses from physical address data. A key insight is that CAI street addresses are publicly known, which allows us to identify a candidate set of IPs from commercial geolocation that are likely serving the location associated with a CAI. In this paper, \textbf{we focus on US public libraries}, which offer both geographic diversity across thousands of locations, and some publicly available institutional records (\eg{}WHOIS registrations) that enable systematic validation of our approach. Our approach offers a novel integration of IP geolocation databases, DNS PTR records, WHOIS registrations, broadband provider data, and active measurements to identify IPs likely assigned to libraries and validate them. Based on evaluations, our approach can map a library to its IP prefix approx. half of the time, with coverage across all US states, as well as urban and rural areas. Our results highlight the feasibility of mapping CAI presence in IP space and offer a foundation for large-scale, remote broadband infrastructure evaluation.

cs.NI↗

arXiv2026

Stratifying the Digital Divide: Analysis of Socio-Economic Influences on Internet Performance

Despite numerous technological advancements, the digital divide remains a pressing issue affecting millions worldwide. We present a framework for diagnosing internet inequality at the Census Block Group level by pairing approximately 170 million crowdsourced Ookla speed tests (2021--2025) with U.S. Census demographics across six metropolitan regions. After quantifying and correcting for sampling bias, we use Random Forest regression with permutation importance to identify the socio-economic drivers of download speed, upload speed, and latency. Population density dominates all three metrics at the regional level, but this dominance is an artifact of scale: once areas are stratified into density bins, its influence vanishes in medium- and higher-density neighborhoods, revealing that socio-economic conditions are the true differentiators of internet quality in most urban settings. After controlling for density, income and racial composition emerge as the primary drivers, income consistently dictating upload speed and racial composition proving to be a stronger predictor of download speed than either income or education. Our findings demonstrate that internet inequality is locally configured: no single national narrative explains it, and effective policy demands region-specific intervention.

cs.NI↗

arXiv2026

A DualPI2 Module for Mahimahi: Behavioral Characterization and Cross-Platform Analysis

Low Latency, Low Loss, and Scalable Throughput (L4S) is an emerging paradigm for latency control based on DualPI2 active queue management and scalable congestion control. While a Linux kernel implementation of DualPI2 is available, controlled and reproducible experimentation on L4S mechanisms can be facilitated by a modular, user-space alternative. In this paper, we present a DualPI2 module for the Mahimahi network emulator, designed to support extensible, component-level experimentation without kernel modification. We conduct a statistical behavioral characterization of the Mahimahi implementation by examining key metrics across diverse traffic patterns and network conditions, using the Linux kernel implementation as a reference baseline. Our analysis shows that behavioral alignment across execution environments is not automatic: identical DualPI2 parameterization does not guarantee identical dynamics. Instead, key control parameters exhibit environment-dependent sensitivity, leading to regime-dependent discrepancies across bandwidth-delay product (BDP) conditions. Through targeted parameter exploration, we identify configurations that improve cross-platform alignment in low BDP regimes, while revealing structural differences that persist under higher load. This work provides both a practical tool for experimental L4S research and empirical insight into cross-platform behavioral differences, highlighting the importance of systematic characterization and environment-aware parameter selection in emulation-based AQM studies.

cs.NI↗

arXiv2026

Robust and Extensible Measurement of Broadband Plans with BQT+

Independent, street address-level broadband data is essential for evaluating Internet infrastructure investments, such as the $42B Broadband Equity, Access, and Deployment (BEAD) program. Evaluating these investments requires longitudinal visibility into broadband availability, quality, and affordability, including data on pre-disbursement baselines and changes in providers' advertised plans. While such data can be obtained through Internet Service Provider (ISP) web interfaces, these workloads impose three fundamental system requirements: robustness to frequent interface evolution, extensibility across hundreds of providers, and low technical overhead for non-expert users. Existing systems fail to meet these three essential requirements. We present BQT+, a broadband plan measurement framework that replaces monolithic workflows with declarative state/action specifications. BQT+ models querying intent as an interaction state space, formalized as an abstract nondeterministic finite automaton (NFA), and selects execution paths at runtime to accommodate alternative interaction flows and localized interface changes. We show that BQT+ sustains longitudinal monitoring of 64 ISPs, supporting querying for over 100 ISPs. We apply it to two policy studies: constructing a BEAD pre-disbursement baseline and benchmarking broadband affordability across over 124,000 addresses in four states.

cs.NI↗

arXiv2025

Enabling Data-Driven Policymaking Using Broadband-Plan Querying Tool (BQT+)

Poor broadband access undermines civic and economic life, a challenge exacerbated by the fact that millions of Americans still lack reliable high-speed connectivity. Federal broadband funding initiatives aim to address these gaps, but their success depends on accurate availability and affordability data. Existing data, often based on self-reported ISP information, can overstate coverage and speeds, risking misallocation of funds and leaving unserved communities behind. We present BQT+, an AI-agent data collection platform that queries ISP web interfaces by inputting residential street addresses and extracting data on service availability, quality, and pricing. BQT+ has been used in policy evaluation studies, including an independent assessment of broadband availability, speed tiers, and affordability in areas targeted by the $42.45 billion BEAD program.

cs.NI↗

arXiv2025

TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests

Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and collectively, platforms like Ookla, M-Lab, and Fast.com generate petabytes of traffic each month. Reducing this burden requires deciding when a test can be stopped early without sacrificing accuracy. We frame this as an optimal stopping problem and show that existing heuristics-static thresholds, BBR pipe-full signals, or throughput stability rules from Fast.com and FastBTS-capture only a narrow slice of the achievable accuracy-savings trade-off. This paper introduces TurboTest, a systematic framework for speed test termination that sits atop existing platforms. The key idea is to decouple throughput prediction (Stage 1) from test termination (Stage 2): Stage 1 trains a regressor to estimate final throughput from partial measurements, while Stage 2 trains a classifier to decide when sufficient evidence has accumulated to stop. Leveraging richer transport-level features (RTT, retransmissions, congestion window) alongside throughput, TurboTest exposes a single tunable parameter epsilon for accuracy tolerance and includes a fallback mechanism for high-variability cases. Evaluation on 1 million M-Lab NDT speed tests (2024-2025) shows that TurboTest achieves 1.8-4.4x higher data savings than an approach based on BBR signals while reducing median error. These results demonstrate that adaptive ML-based termination can deliver accurate, efficient, and deployable speed tests at scale.

cs.NI↗

arXiv2025

Quality of Coverage (QoC): Quantifying Cellular Network Coverage Quality, Usability and Stability

Characterizing cellular network performance is complex. Current representations of cellular coverage, such as service provider and FCC coverage maps, focus only on the minimal level of available bandwidth (e.g., 35/3Mbps download/upload speed for 5G) and omit critical dimensions of quality: network usability and stability over space and time. Because cellular performance can vary substantially along both dimensions, a more fine-grained characterization is necessary. We introduce Quality of Coverage (QoC), a novel multi-dimensional set of key performance indicators (KPIs) that capture measured temporal and spatial performance quality, usability and stability. To evaluate QoC, we first analyze whether the QoC KPIs accurately reflect expected network behavior at individual locations and across spatially-aggregated regions. Then, we apply QoC to more than 15 million measurements from a production network to evaluate its ability to characterize real-world network behavior. Together, our results demonstrate the need for KPIs that capture the full spectrum of cellular performance and show how QoC enables rigorous evaluation of coverage quality across multiple geographic scales.

cs.NI↗

arXiv2024

Learning Cellular Network Connection Quality with Conformal

In this paper, we address the problem of uncertainty quantification for cellular network speed. It is a well-known fact that the actual internet speed experienced by a mobile phone can fluctuate significantly, even when remaining in a single location. This high degree of variability underscores that mere point estimation of network speed is insufficient. Rather, it is advantageous to establish a prediction interval that can encompass the expected range of speed variations. In order to build an accurate network estimation map, numerous mobile data need to be collected at different locations. Currently, public datasets rely on users to upload data through apps. Although massive data has been collected, the datasets suffer from significant noise due to the nature of cellular networks and various other factors. Additionally, the uneven distribution of population density affects the spatial consistency of data collection, leading to substantial uncertainty in the network quality maps derived from this data. We focus our analysis on large-scale internet-quality datasets provided by Ookla to construct an estimated map of connection quality. To improve the reliability of this map, we introduce a novel conformal prediction technique to build an uncertainty map. We identify regions with heightened uncertainty to prioritize targeted, manual data collection. In addition, the uncertainty map quantifies how reliable the prediction is in different areas. Our method also leads to a sampling strategy that guides researchers to selectively gather high-quality data that best complement the current dataset to improve the overall accuracy of the prediction model.

cs.NI↗

arXiv2024

The Efficacy of the Connect America Fund in Addressing US Internet Access Inequities

Residential fixed broadband internet access in the United States (US) has long been distributed inequitably, drawing significant attention from researchers and policymakers. This paper evaluates the efficacy of the Connect America Fund (CAF), a key policy intervention aimed at addressing disparities in US internet access. CAF subsidizes the creation of new regulated broadband monopolies in underserved areas, aiming to provide comparable internet access, in terms of price and speed, to that available in urban regions. Oversight of CAF largely relies on data self-reported by internet service providers (ISPs), which is often questionable. We use the broadband-plan querying tool (BQT) to curate a novel dataset that complements ISP-reported information with ISP-advertised broadband plan details (download speed and monthly cost) on publicly accessible websites. Specifically, we query advertised broadband plans for 687k residential addresses across 15 states, certified as served by ISPs to regulators. Our analysis reveals significant discrepancies between ISP-reported data and actual broadband availability. We find that the serviceability rate-defined as the fraction of addresses ISPs actively serve out of the total queried, weighted by the number of CAF addresses in a census block group-is only 55%, dropping to as low as 18% in some states. Additionally, the compliance rate-defined as the weighted fraction of addresses where ISPs actively serve and advertise download speeds above the FCC's 10 Mbps threshold-is only 33%. We also observe that in a subset of census blocks, CAF-funded addresses receive higher broadband speeds than their monopoly-served neighbors. These results indicate that while a few users have benefited from this multi-billion dollar program, it has largely failed to achieve its intended goal, leaving many targeted rural communities with inadequate or no broadband connectivity.

cs.NI↗

arXiv2023

Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications.

stat.AP↗

arXiv2023

Analyzing Disparity and Temporal Progression of Internet Quality through Crowdsourced Measurements with Bias-Correction

Crowdsourced speedtest measurements are an important tool for studying internet performance from the end user perspective. Nevertheless, despite the accuracy of individual measurements, simplistic aggregation of these data points is problematic due to their intrinsic sampling bias. In this work, we utilize a dataset of nearly 1 million individual Ookla Speedtest measurements, correlate each datapoint with 2019 Census demographic data, and develop new methods to present a novel analysis to quantify regional sampling bias and the relationship of internet performance to demographic profile. We find that the crowdsourced Ookla Speedtest data points contain significant sampling bias across different census block groups based on a statistical test of homogeneity. We introduce two methods to correct the regional bias by the population of each census block group. Whereas the sampling bias leads to a small discrepancy in the overall cumulative distribution function of internet speed in a city between estimation from original samples and bias-corrected estimation, the discrepancy is much smaller compared to the size of the sampling heterogeneity across regions. Further, we show that the sampling bias is strongly associated with a few demographic variables, such as income, education level, age, and ethnic distribution. Through regression analysis, we find that regions with higher income, younger populations, and lower representation of Hispanic residents tend to measure faster internet speeds along with substantial collinearity amongst socioeconomic attributes and ethnic composition. Finally, we find that average internet speed increases over time based on both linear and nonlinear analysis from state space models, though the regional sampling bias may result in a small overestimation of the temporal increase of internet speed.

stat.AP↗

arXiv2023

Watching Stars in Pixels: The Interplay of Traffic Shaping and YouTube Streaming QoE over GEO Satellite Networks

Geosynchronous satellite (GEO) networks are a crucial option for users beyond terrestrial connectivity. However, unlike terrestrial networks, GEO networks exhibit high latency and deploy TCP proxies and traffic shapers. The deployment of proxies effectively mitigates the impact of high network latency in GEO networks, while traffic shapers help realize customer-controlled data-saver options that optimize data usage. It is unclear how the interplay between GEO networks' high latency, TCP proxies, and traffic-shaping policies affects the quality of experience (QoE) for commonly used video applications. To fill this gap, we analyze the quality of over $2$k YouTube video sessions streamed across a production GEO network with a $900$Kbps shaping rate. Given the average bit rates for the selected videos, we expected seamless streaming at $360$p or lower resolutions. However, our analysis reveals that this is not the case: $28\%$ of TCP sessions and $18\%$ of gQUIC sessions experience rebuffering events, while the median average resolution is only $380$p for TCP and $299$p for gQUIC. Our analysis identifies two key factors contributing to sub-optimal performance: (i)unlike TCP, gQUIC only utilizes $63\%$ of network capacity; and (ii)YouTube's imperfect chunk request pipelining. As a result of our study, the partner GEO ISP discontinued support for the low-bandwidth data-saving option in U.S. business and residential markets to avoid potential degradation of video quality -- highlighting the practical significance of our findings.

cs.NI↗

arXiv2023

Decoding the Divide: Analyzing Disparities in Broadband Plans Offered by Major US ISPs

Digital equity in Internet access is often measured along three axes: availability, affordability, and adoption. Most prior work focuses on availability; the other two aspects have received little attention. In this paper, we study broadband affordability in the US. Specifically, we focus on the nature of broadband plans offered by major ISPs across the US. To this end, we develop a broadband plan querying tool (BQT) that obtains broadband plans (upload/download speed and price) offered by seven major ISPs for any street address in the US. We then use this tool to curate a dataset, querying broadband plans for over 837k street addresses in thirty cities for seven major wireline broadband ISPs. We use a plan's carriage value, the Mbps of a user's traffic that an ISP carries for one dollar, to compare plans. Our analysis provides us with the following new insights: (1) ISP plans vary inter-city. Specifically, the fraction of census block groups that receive high and low carriage value plans varies widely by city; (2) ISP plans intra-city are spatially clustered, and the carriage value can vary as much as 600% within a city; (3) Cable-based ISPs offer up to 30% more carriage value to users when competing with fiber-based ISPs in a block group; and (4) Average income in a block group plays a critical role in dictating who gets a fiber deployment (i.e., a better carriage value) in the US. While we hope our tool, dataset, and analysis in their current form are helpful for policymakers at different levels (city, county, state), they are only a small step toward understanding digital equity. Based on our learnings, we conclude with recommendations to continue to advance our understanding of broadband affordability.

cs.NI↗

arXiv2021

Characterizing Performance Inequity Across U.S. Ookla Speedtest Users

The Internet has become indispensable to daily activities, such as work, education and health care. Many of these activities require Internet access data rates that support real-time video conferencing. However, digital inequality persists across the United States, not only in who has access but in the quality of that access. Speedtest by Ookla allows users to run network diagnostic tests to better understand the current performance of their network. In this work, we leverage an Internet performance dataset from Ookla, together with an ESRI demographic dataset, to conduct a comprehensive analysis that characterizes performance differences between Speedtest users across the U.S. Our analysis shows that median download speeds for Speedtest users can differ by over 150Mbps between states. Further, there are important distinctions between user categories. For instance, all but one state showed statistically significant differences in performance between Speedtest users in urban and rural areas. The difference also exists in urban areas between high and low income users in 27 states. Our analysis reveals that states that demonstrate this disparity in Speedtest results are geographically bigger, more populous and have a wider dispersion of median household income. We conclude by highlighting several challenges to the complex problem space of digital inequality characterization and provide recommendations for furthering research on this topic.

cs.NI↗

arXiv2021

A Tale of Three Datasets: Towards Characterizing Mobile Broadband Access in the United States

Understanding and improving mobile broadband deployment is critical to bridging the digital divide and targeting future investments. Yet accurately mapping mobile coverage is challenging. In 2019, the Federal Communications Commission (FCC) released a report on the progress of mobile broadband deployment in the United States. This report received a significant amount of criticism with claims that the cellular coverage, mainly available through Long-Term Evolution (LTE), was over-reported in some areas, especially those that are rural and/or tribal [12]. We evaluate the validity of this criticism using a quantitative analysis of both the dataset from which the FCC based its report and a crowdsourced LTE coverage dataset. Our analysis is focused on the state of New Mexico, a region characterized by diverse mix of demographics-geography and poor broadband access. We then performed a controlled measurement campaign in northern New Mexico during May 2019. Our findings reveal significant disagreement between the crowdsourced dataset and the FCC dataset regarding the presence of LTE coverage in rural and tribal census blocks, with the FCC dataset reporting higher coverage than the crowdsourced dataset. Interestingly, both the FCC and the crowdsourced data report higher coverage compared to our on-the-ground measurements. Based on these findings, we discuss our recommendations for improved LTE coverage measurements, whose importance has only increased in the COVID-19 era of performing work and school from home, especially in rural and tribal areas.

cs.NI↗

arXiv2020

Measuring and Characterizing Hate Speech on News Websites

The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles on other platforms like Reddit. These features empower and enable discussions among the users; however, they also act as the medium for the dissemination of toxic discourse and hate speech. The research community lacks a general understanding on what type of content attracts hateful discourse and the possible effects of social networks on the commenting activity on news articles. In this work, we perform a large-scale quantitative analysis of 125M comments posted on 412K news articles over the course of 19 months. We analyze the content of the collected articles and their comments using temporal analysis, user-based analysis, and linguistic analysis, to shed light on what elements attract hateful comments on news articles. We also investigate commenting activity when an article is posted on either 4chan's Politically Incorrect board (/pol/) or six selected subreddits. We find statistically significant increases in hateful commenting activity around real-world divisive events like the "Unite the Right" rally in Charlottesville and political events like the second and third 2016 US presidential debates. Also, we find that articles that attract a substantial number of hateful comments have different linguistic characteristics when compared to articles that do not attract hateful comments. Furthermore, we observe that the post of a news articles on either /pol/ or the six subreddits is correlated with an increase of (hateful) commenting activity on the news articles.

cs.SI↗

arXiv2019

Towards Understanding Gender Bias in Relation Extraction

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. WikiGenderBias has sentences specifically curated to analyze gender bias in relation extraction systems. We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE. We also analyze how name anonymization, hard debiasing for word embeddings, and counterfactual data augmentation affect gender bias in predictions and performance.

cs.LG↗