Automated spectrum analytics inform critical decisions in dynamic spectrum access networks such as (i) how to allocate network resources to clients, (ii) when to enforce penalties due to malicious or disruptive activity, and (iii) how to chart policies for future regulations. The insights gleaned from a spectrum trace, however, are as objective as the trace itself, and artifacts introduced by sensor imperfections or improper configuration will inevitably affect analysis outcomes. Yet, spectrum analytics have been largely developed in isolation from the underlying data collection and are oblivious to sensor-induced artifacts.To address this challenge, we develop VIA, a framework that attributes sensor properties and configuration to spectrum data fidelity, and models the relationship between spectrum analytics performance and data quality. VIA does not require expert input or intervention and can be used to profile the fidelity of unknown sensors. VIA takes as an input a spectrum trace and the sensor configuration, and benchmarks data quality along three dimensions: (i) Veracity, or how truthfully a scan captures spectrum activity, (ii) Intermittency, characterizing the temporal persistence of spectrum scans and (iii) Ambiguity quantifying the likelihood of false detection. We employ VIA to measure the data fidelity of five common sensor platforms. We then predict the outcome of several spectrum analysis tasks including occupancy and transmitter detection, and modulation recognition using both controlled and real-world measurements. We demonstrate high prediction performance with an average mean squared error of 0.0013 across all tasks using both regression and neural network models.

Mobile wireless networks revolutionize our lives and livelihoods. Yet, rural areas, characterized with sparse populations and rugged terrain, consistently lag behind in mobile connectivity compared to their urban counterparts. As a result, community-owned networks realized through fixed wireless tech-nologies, have become an increasingly viable Internet option for otherwise disconnected areas. Fixed wireless, however, is inherently designed for residential/stationary access and is not readily applicable for the use of mobile agents that might travel through a rural community. In this paper we explore the extension of fixed wireless networks for mobile access. A key factor for continuous mobile access is efficient rate adaptation. To that end, we develop WideRate- a reinforcement learning framework that employs signal strength measurements for optimal rate adaptation. We showcase WideRate in the context of wide-area Television White Space networks, whereby we design a vehicular mobile unit and carry out an extensive measurement campaign in a real community network. We use the collected traces to motivate the need for rate adaptation and implement a realistic network simulator that aids in our evaluation. We demonstrate that WideRate significantly outperforms counterparts from the literature including a reinforcement learning model.

The unique socio-economic structure of rural communities makes them particularly vulnerable to emergencies. However, rural emergency preparedness and response (EPR) significantly lag behind their urban counterparts. A key obstacle to timely dissemination of emergency information is limited broadband, which in turn limits agencies’ abilities to (i) disseminate preparedness and response information to residents and (ii) coordinate in the face of a disaster.

While mobile devices are ubiquitous, their supporting communication infrastructure is cost-effective only in densely populated urban areas and is often lacking in rural settings. This lack of connectivity leads to lost opportunities in applications such as rural emergency preparedness and response. Peer-to-peer exchange that uses predictable human mobility can enable delay-tolerant information access in rural settings. We propose, an adaptive distributed solution for device-to-device Connectivity Optimization via REinforcement Learning (CORE) in wireless adhoc networks. Our solution is designed for collaborative distributed agents with intermittent connectivity and limited battery power, but predictable mobility within short temporal horizons. We seek to maximize the utility of connection attempts while keeping the power expenditure within a predefined battery budget. Agents learn to adaptively make automated decisions for when to attempt connections and exchange information, based on a local RL model of their mobility and that of other agents they learn about from exchanges. Using both synthetic and real-world mobility traces, we demonstrate that agents are able to materialize 95% of the possible connections using 20% of their battery and successfully adapting to changes in the underlying mobility patterns within several days of learning.

Large-scale emergencies, both natural and man-made, are increasingly incurring devastating losses in terms of infrastructure and human lives. Rural areas are particularly vulnerable to such losses. Emergency preparedness and response services, in rural areas, severely lag behind that of their urban counterparts. One of the key limiting factors is the lack of adequate broadband connectivity, which limits agencies' capabilities to (i) disseminate emergency preparedness and response information to residents and (ii) efficiently coordinate in the face of a disaster. In this demo, we present the EApp; a smartphone application that strives to improve the information access regarding emergencies for rural residents and first responders.