ترغب بنشر مسار تعليمي؟ اضغط هنا

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

344   0   0.0 ( 0 )
 نشر من قبل Tarun Mangla
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف Tarun Mangla




اسأل ChatGPT حول البحث

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.

قيم البحث

اقرأ أيضاً

The COVID-19 pandemic has resulted in dramatic changes to the daily habits of billions of people. Users increasingly have to rely on home broadband Internet access for work, education, and other activities. These changes have resulted in correspondin g changes to Internet traffic patterns. This paper aims to characterize the effects of these changes with respect to Internet service providers in the United States. We study three questions: (1)How did traffic demands change in the United States as a result of the COVID-19 pandemic?; (2)What effects have these changes had on Internet performance?; (3)How did service providers respond to these changes? We study these questions using data from a diverse collection of sources. Our analysis of interconnection data for two large ISPs in the United States shows a 30-60% increase in peak traffic rates in the first quarter of 2020. In particular, we observe traffic downstream peak volumes for a major ISP increase of 13-20% while upstream peaks increased by more than 30%. Further, we observe significant variation in performance across ISPs in conjunction with the traffic volume shifts, with evident latency increases after stay-at-home orders were issued, followed by a stabilization of traffic after April. Finally, we observe that in response to changes in usage, ISPs have aggressively augmented capacity at interconnects, at more than twice the rate of normal capacity augmentation. Similarly, video conferencing applications have increased their network footprint, more than doubling their advertised IP address space.
Objectives To test the feasibility of using Twitter data to assess determinants of consumers health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). Methods We used three Twitter datasets span ning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers discussions. We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). Results We collected 2,846,495 tweets and analyzed 335,681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is cervical cancer screening; while in promotional tweets, the most popular topic is to increase awareness of HPV causes cancer. 87 out of the 122 topics are correlated between promotional information and consumers discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. 35 topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. Conclusion Not only mining Twitter to assess consumers health behaviors can obtain results comparable to surveys but can yield additional insights via a theory-driven approach. Limitations exist, nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape.
123 - Rui Wang , Jiahui Chen , Kaifu Gao 2020
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been mutating since it was first sequenced in early January 2020. The genetic variants have developed into a few distinct clusters with different properties. Since the United States (US) has the highest number of viral infected patients globally, it is essential to understand the US SARS-CoV-2. Using genotyping, sequence-alignment, time-evolution, $k$-means clustering, protein-folding stability, algebraic topology, and network theory, we reveal that the US SARS-CoV-2 has four substrains and five top US SARS-CoV-2 mutations were first detected in China (2 cases), Singapore (2 cases), and the United Kingdom (1 case). The next three top US SARS-CoV-2 mutations were first detected in the US. These eight top mutations belong to two disconnected groups. The first group consisting of 5 concurrent mutations is prevailing, while the other group with three concurrent mutations gradually fades out. Our analysis suggests that female immune systems are more active than those of males in responding to SARS-CoV-2 infections. We identify that one of the top mutations, 27964C$>$T-(S24L) on ORF8, has an unusually strong gender dependence. Based on the analysis of all mutations on the spike protein, we further uncover that three of four US SASR-CoV-2 substrains become more infectious. Our study calls for effective viral control and containing strategies in the US.
Dense deployment of base stations (BSs) and multi-antenna techniques are considered key enablers for future mobile networks. Meanwhile, spectrum sharing techniques and utilization of higher frequency bands make more bandwidth available. An important question for future system design is which element is more effective than others. In this paper, we introduce the concept of technical rate of substitution (TRS) from microeconomics and study the TRS of spectrum in terms of BS density and antenna number per BS. Numerical results show that TRS becomes higher with increasing user data rate requirement, suggesting that spectrum is the most effective means of provisioning extremely fast mobile broadband.
The Fifth Generation (5G) wireless service of sensor networks involves significant challenges when dealing with the coordination of ever-increasing number of devices accessing shared resources. This has drawn major interest from the research communit y as many existing works focus on the radio access network congestion control to efficiently manage resources in the context of device-to-device (D2D) interaction in huge sensor networks. In this context, this paper pioneers a study on the impact of D2D link reliability in group-assisted random access protocols, by shedding the light on beneficial performance and potential limitations of approaches of this kind against tunable parameters such as group size, number of sensors and reliability of D2D links. Additionally, we leverage on the association with a Geolocation Database (GDB) capability to assist the grouping decisions by drawing parallels with recent regulatory-driven initiatives around GDBs and arguing benefits of the suggested proposal. Finally, the proposed method is approved to significantly reduce the delay over random access channels, by means of an exhaustive simulation campaign.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا