Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

It made it possible to improve people's lives. Now it prevents all forms of discrimination in the world. It helps to improve our world.

Universal Declaration of Human Rights

An important goal of macrosystems ecology (MSE) research is to advance understanding of ecological systems at both fine and broad temporal and spatial scales. Our premise in this paper is that MSE projects require integrated information management at their inception. Such efforts will lead to improved communication and sharing of knowledge among diverse project participants, better science outcomes, and more transparent and accessible (ie “open”) science. We encourage researchers to “complete the data life cycle” by publishing well-documented datasets, thereby facilitating re-use of the data to answer new and different questions from the ones conceived by those involved in the original projects. The practice of documenting and submitting datasets to data repositories that are publicly accessible ensures that research results and data are available to and use-able by other researchers, thus fostering open science. However, ecologists are often unfamiliar with the requirements and information management tools for effectively preserving data and receive little institutional or professional incentive to do so. Here, we provide recommendations for achieving these ends and give examples from current MSE projects to demonstrate why information management is critical for ensuring that scientific results can be reproduced and that data can be shared for future use.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/120375

Completing the data life cycle: using information management in macrosystems ecology research

Universal Declaration of Human Rights (1948)

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors’ battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor’s density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance.

https://biblio.ugent.be/publication/8672750/file/8672751.pdf

System Assessment of WUSN Using NB-IoT UAV-Aided Networks in Potato Crops

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.

https://dl.acm.org/doi/pdf/10.1145/3366423.3380251

#Outage: Detecting Power and Communication Outages from Social Networks

Rural IoT sensor networks, prevalent in environmental monitoring and precision agriculture, commonly operate over some variant of the IEEE 802.15.4 standard. Data collection from these networks is often challenging, as they may be deployed in remote regions where existing backhaul infrastructure is expensive or absent. With the commercial and industrial success of Unmanned Aircraft Systems (UAS), there is understandable interest in using UASs for delay tolerant data collection from 802.15.4 IoT sensor networks. In this study, we investigate how to optimize 802.15.4 networks for aerial data collection, which, unlike other wireless standards, has not received rigorous evaluation for three-dimensional aerial communication. We analyze experimental measurements from an outdoor aerial testbed, examining how factors, such as antenna orientation, altitude, antenna placement, and obstruction, affect signal strength and packet reception rate. In our analysis, we model and predict the quality of service for aerial data collection, based on these network configuration variables, and contrast that with the Received Signal Strength Indication (RSSI)-a commonly used signal strength metric. We find that network configuration plays a significant role in network quality, which RSSI, a mediator variable, struggles to account for in the presence of high packet loss. We conclude with a discussion of strategies for optimizing sensor network configuration for aerial data collection, in light of our results.

https://www.mdpi.com/1424-8220/19/16/3479/pdf

Optimizing 802.15.4 Outdoor IoT Sensor Networks for Aerial Data Collection.

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.

Evaluating LTE Coverage and Quality from an Unmanned Aircraft System

DataTurbine is a robust real-time streaming data engine that lets you quickly stream live data from experiments, labs, web cams and even Java-enabled cell phones. It acts as a “black box” to which applications and devices send and receive data. Think of it as express delivery for your data, be it numbers, video, sound or text. For ecological applications, DataTurbine is useful for moving data in near real time from sensors to field stations to data centers. DataTurbine handles time series data over networks with intermittent connectivity. It is vendor-neutral, so it works with sensors and dataloggers from a variety of manufacturers and research labs.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/0012-9623-93.3.242

The Open Source DataTurbine Initiative: Empowering the Scientific Community with Streaming Data Middleware

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.

https://dl.acm.org/doi/pdf/10.1145/3355369.3355574

Michael Nekrasov

Papers

#Outage: Detecting Power and Communication Outages from Social Networks

Optimizing 802.15.4 Outdoor IoT Sensor Networks for Aerial Data Collection.

Evaluating LTE Coverage and Quality from an Unmanned Aircraft System

The Open Source DataTurbine Initiative: Empowering the Scientific Community with Streaming Data Middleware

Packet-level Overload Estimation in LTE Networks using Passive Measurements