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.

This work uses experimental measurements to study the impact of network configuration and flight planning on Unmanned Aircraft System (UAS) assisted data collection in a 2.4GHz IEEE 802.15.4 outdoor aerial testbed. Our paper builds on previous work in UAS data collection from 802.15.4 outdoor sensor networks by conducting a novel investigation of the impact of antenna orientation on transceivers with external straight wire antennae. We study the effects of toroidal radiation and antenna polarization on signal strength, and we compare external antenna configurations to the commonly used embedded coiled antenna modules. We model our data using a Zero Inflated Negative Binomial (ZINB) model. For each hardware configuration and orientation, we identify the optimal altitude to fly a UAS. Our results show that choosing antenna configuration (including type and orientation) for an IoT network depends on the intended UAS collection flight plan.

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.

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.

Unmanned Aircraft Systems (UAS) are a promising technology for data collection from outdoor sensor networks. Environmental and agricultural networks may not have existing internet backhauls for data delivery due to low population densities in rural areas, making UASs a potential data delivery alternative. UASs can be deployed as aerial network relay nodes [1, 2, 3, 4] or as data mules [5, 6]. In addition to mending network fragmentation, UAS applications include post-disaster data collection involving inoperative communication infrastructure [7, 8, 9], supplementing existing communication in- frastructure for vehicular networks [10], and rural applications in environmental monitoring [11, 12] and precision agriculture [13, 14].