IMR: MM-1A: ADDRESS: Augment, Denoise and Debias cRowdsourced mEasurements for Statistical Synthesis of internet access characterization

IMR：MM-1A：地址：互联网接入特征统计综合的增强、降噪和去偏众包测量

• 批准号: 2220417
• 负责人: Elizabeth Belding
• 金额: $ 60万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220417&HistoricalAwards=false
• 关键词: IMR; MM; 1A; ADDRESS; Augment

The U.S. suffers from digital inequality that stretches in multiple dimensions. Several demographic and socioeconomic factors, such as race, ethnicity, and income, as well as population density, are often indicators of Internet availability and quality. To fully bridge the Internet access divide and best allocate available funding, policymakers need to understand the state of Internet accessibility and quality at fine-grained geographical granularity. The goal of this project is to make the best use of crowdsourced Internet measurement data to estimate the distribution of fixed Internet quality at different levels of geospatial granularity with as high accuracy as possible.To do so, the projects plans to make the following contributions: (i) to develop a Broadband Offerings Tool that aggregates ISP broadband plan offerings and links that data with crowdsourced measurements and socioeconomic data to predict user subscription plans, (ii) to develop new methodologies and algorithms that can detect noisy data points, such as speed test measurements that report bottlenecks in WiFi access or remote peering links, which bias the understanding of Internet quality, (iii) to propose new statistical techniques that use the resulting dataset to output a debiased dataset that removes the spatial, temporal, and demographic biases inherent in crowdsourced Internet measurement data.Digital inequality continues to persist in the U.S., deepened by the work-from-home and remote schooling resulting from the COVID-19 pandemic. The outputs of this work will be used to report the distribution of Internet quality in a region and predict that of regions for which less data is available. This work will also reveal demographic variables that most influence the U.S. digital divide. The outcomes of this project can be used to inform local, state and federal governments about where investments must be made to ensure all Americans have access to high quality Internet.A project website will be established, which will contain information about research methodology, models and outcomes. The likely URL is broadband.cs.ucsb.edu. The website will be maintained for the duration of the project, and at least a year thereafter.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国遭受着多方面的数字不平等。一些人口和社会经济因素，如种族、民族和收入，以及人口密度，往往是互联网可用性和质量的指标。为了充分弥合互联网接入鸿沟并最佳地分配可用资金，决策者需要了解互联网接入状况和精细地理粒度的质量。该项目的目标是充分利用众包互联网测量数据，以尽可能高的准确度估计不同地理空间粒度级别的固定互联网质量分布。为此，项目计划做出以下贡献：（一）开发一个宽带提供工具，该工具汇总ISP宽带计划产品，并将该数据与众包测量数据和社会经济数据联系起来，预测用户订阅计划，（ii）开发可以检测噪声数据点的新方法和算法，例如报告WiFi接入或远程对等链路中的瓶颈的速度测试测量，其使对互联网质量的理解产生偏差，（iii）提出新的统计技术，其使用所得数据集来输出去除空间，时间，以及众包互联网测量数据中固有的人口统计偏见。数字不平等在美国继续存在，2019冠状病毒病疫情导致在家工作和远程教育的情况进一步恶化。这项工作的产出将用于报告一个区域的互联网质量分布情况，并预测可获得数据较少的区域的分布情况。这项工作还将揭示对美国数字鸿沟影响最大的人口变量。该项目的成果可用于告知地方、州和联邦政府，必须在哪些方面进行投资，以确保所有美国人都能使用高质量的互联网。将建立一个项目网站，其中将包含有关研究方法、模型和成果的信息。可能的URL是broadband.cs.ucsb.edu。这个奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

解码鸿沟：分析美国主要 ISP 提供的宽带计划的差异

• DOI: 10.1145/3603269.3604831
• 发表时间: 2023
• 期刊: ACM Sigcomm
• 作者: Paul, Udit;Gunasekaran, Vinothini;Liu, Jiamo;Narechania, Tejas N.;Gupta, Arpit;Belding, Elizabeth
• 通讯作者: Belding, Elizabeth

The importance of contextualization of crowdsourced active speed test measurements

• DOI: 10.1145/3517745.3561441
• 发表时间: 2022-10
• 期刊: Proceedings of the 22nd ACM Internet Measurement Conference
• 作者: Udit Paul;Jiamo Liu;Mengyang Gu;Arpit Gupta;E. Belding-Royer