It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed; (2) average download speed in rural areas is 2.5 times lower than urban areas; (3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes; and (4) the U.S. Federal Communication Commission’s (FCC’s) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test.

Despite widespread LTE deployment, coverage does not necessarily translate to usable service. Even in well-provisioned urban networks, unusually high usage (such as during a public event or after a natural disaster) can lead to congestion that makes the LTE service difficult, if not impossible, to use, even if the user is solidly within the coverage area. A typical approach to detect and quantify congestion on LTE networks is to secure the cooperation of the network provider for access to internal metrics. An alternative approach is to deploy multiple mobile devices with active subscriptions to each network operator. Both approaches are resource and time intensive. In this work, we propose a novel method to estimate congestion from overloaded LTE networks using only passive measurements, and without requiring provider cooperation. We analyze packet-level traces for four commercial LTE service providers, from several locations during both typical levels of usage and during public events that yield large, dense crowds. This study presents the first look at congestion detection through overload estimation by examining unencrypted broadcast messages. We show that an upsurge in broadcast reject and cell barring messages, leading to overload, can accurately detect an increase in network congestion.

Cellular network performance does not cleanly generalize. A variety of factors, such as location, terrain, signal quality and network load, affect the performance of services delivered over LTE networks. As a result, the presence of LTE coverage does not always equate to usable service; coverage can be of poor quality, or it can be congested and difficult to access. Given that reliance on LTE networks for Internet connectivity has exploded, it is critical to understand the quality of experience for applications delivered over these networks in a variety of scenarios. To this end, we develop a robust measurement suite that we use to conduct a unique measurement campaign in tribal, rural, congested urban and uncongested urban regions, representing a variety of under-provisioned, congested, and well-provisioned operational LTE networks run by four major providers. Our analysis confirms that the performance of LTE networks in tribal and rural areas is typically worse than even heavily congested urban networks. More specifically, in the regions that we study, LTE networks in under-provisioned (tribal/rural) areas have $ 9\times$ poorer video streaming quality, $ 10\times$ higher video start-up delay, undergo more than $ 10\times$ the number of resolution switches, and lead to more than $ 2\times$ slower Web browsing experience as compared to urban deployments. We show that throughput and latency are $ 11\times$ and $ 3\times$ worse in tribal and rural locations, despite identical LTE carrier subscription plans.

Natural disasters are increasing worldwide at an alarming rate. To aid relief operations during and post disaster, humanitarian organizations rely on various types of situational information such as missing, trapped or injured people and damaged infrastructure in an area. Crucial and timely identification of infrastructure and utility damage is critical to properly plan and execute search and rescue operations. However, in the wake of natural disasters, real-time identification of this information becomes challenging. In this research, we investigate the use of tweets posted on the Twitter social media platform to detect power and communication outages during natural disasters. We first curate a data set of 18,097 tweets based on domain-specific keywords obtained using Latent Dirichlet Allocation. We annotate the gathered data set to separate the tweets into different types of outage-related events: power outage, communication outage and both power-communication outage. We analyze the tweets to identify information such as popular words, length of words and hashtags as well as sentiments that are associated with tweets in these outage-related categories. Furthermore, we apply machine learning algorithms to classify these tweets into their respective categories. Our results show that simple classifiers such as the boosting algorithm are able to classify outage related tweets from unrelated tweets with close to 100% f1-score. Additionally, we observe that the transfer learning model, BERT, is able to classify different categories of outage-related tweets with close to 90% accuracy in less than 90 seconds of training and testing time, demonstrating that tweets can be mined in real-time to assist first responders during natural disasters.

Today's smartphones are equipped with both Wi-Fi and cellular interfaces, creating usage opportunities for protocols such as Multi-path TCP (MPTCP), which enable devices to use more than one interface concurrently. One of the biggest hurdles in implementing MPTCP is the heterogeneity in performance characteristics that exists across multiple interfaces. This makes the selection of primary interface of paramount importance, as this interface is also used for DNS resolution. In this work, we explore performance and IP reachability over real world networks. Our findings indicate that widespread MPTCP deployment faces significant obstacles. In particular, we perform controlled and real world experiments over multiple paths with differing loss rates and round trip latencies to assess the effect of primary path selection, and the range of issues that arise from selecting the under-performing path. Using results from our experiments, we show how heterogeneous paths can adversely affect MPTCP performance, especially when one path is lossy.

Over 87% of US mobile wireless subscriptions are currently held by LTE-capable devices [34]. However, prior work has demonstrated that connectivity may not equate to usable service. Even in well-provisioned urban networks, unusually high usage (such as during a public event or after a natural disaster) can lead to overload that makes the LTE service difficult, if not impossible to use, even if the user is solidly within the coverage area. A typical approach to detect and quantify overload on LTE networks is to secure the cooperation of the network provider for access to internal metrics. An alternative approach is to deploy multiple mobile devices with active subscriptions to each mobile network operator (MNO). Both approaches are resource and time intensive. In this work, we propose a novel method to estimate overload in LTE networks using only passive measurements, and without requiring provider cooperation. We use this method to analyze packet-level traces for three commercial LTE service providers, T-Mobile, Verizon and AT&T, from several locations during both typical levels of usage and during public events that yield large, dense crowds. This study presents the first look at overload estimation through the analysis of unencrypted broadcast messages. We show that an upsurge in broadcast reject and cell barring messages can accurately detect an increase in network overload.

Despite widespread LTE adoption and dependence, rural areas lag behind in coverage availability and quality. In the United States, while the Federal Communications Commission (FCC), which regulates mobile broadband, reports increases in LTE availability, the most recent FCC Broadband Report was criticized for overstating coverage. Physical assessments of cellular coverage and quality are essential for evaluating actual user experience. However, measurement campaigns can be resource, time, and labor intensive; more scalable measurement strategies are urgently needed. In this work, we first present several measurement solutions to capture LTE signal strength measurements, and we compare their accuracy. Our findings reveal that simple, lightweight spectrum sensing devices have comparable accuracy to expensive solutions and can estimate quality within one gradation of accuracy when compared to user equipment. We then show that these devices can be mounted on Unmanned Aircraft Systems (UAS) to more rapidly and easily measure coverage across wider geographic regions. Our results show that the low-cost aerial measurement techniques have 72% accuracy relative to the ground readings of user equipment, and fall within one quality gradation 98% of the time.