U.S. Rwanda Collaborative Research on Data Flows in IoT Applications

Researcher
Carleen Maitland

Sponsoring Agency
National Science Foundation

Summary

Fulfilling the promise of "Big Data" for fields, such as migration studies, climate science, and food security systems, requires accurate and timely data from countries across the globe. Data collected through Internet-of-Things (IoT) technologies, such as remote sensors and unmanned aerial vehicles (UAVs), have the potential to contribute to global data hubs. However, most IoT applications and their data structures are designed to solve local problems, such as crop protection and local food shortage predictions. This IRES SITTE project will train 18 U.S. master's students in multicriteria design of and policies for IoT systems with the aim of making local IoT data available to global data scientists.

The project involves collaborations with professors/mentors at University of Rwanda and Carnegie Mellon Africa, both in Kigali, Rwanda, the former of which serves as the Center of Excellence for IoT under the World Bank's Eastern and Southern Africa Higher Education Centers of Excellence program. Through the collaboration, students will receive interdisciplinary research training in wireless networks, software engineering, policy studies and sociotechnical systems. This interdisciplinary training will enable designs of low cost IoT systems, usable in a range of contexts, including the U.S., while increasing access to data by biology/agriculture and forced migration scientists. The training will also enhance students' as well as the project's advisory board, consisting of academic scientists and an industry innovation professional, on information and communication technology (ICT) innovations systems in East Africa, expanding global perspectives within the U.S. IT workforce.

Fulfilling the promise of the Internet of Things (IoT) requires ongoing systems innovation to incorporate advances in foundational technologies, lower costs, and fulfill the needs of global data scientists. Designing systems for integration of sensor-derived local data into global scientific data platforms, such as the Global Open Data for Agriculture and Nutrition (GODAN) hub and the International Organization for Migration's (IOM) Migration Data Portal, can simultaneously inform local, regional and global challenges of socioeconomic and environmental development.

This IRES Site Program pursues novel IoT research. The interdisciplinary research combines complementary wireless network, software engineering, policy studies, and sociotechnical systems expertise of the three mentors and the PI. The program consists of three projects, two technical and one focused on policy analysis.

The first technical project involves unmanned aerial vehicles (UAVs, a.k.a. drones) designed for collecting data from refugee gardens. Using cameras, the UAVs collect image data processed through a Normalized Difference Vegetative Index (NDVI), a standard approach for vegetation monitoring. The UAV will also be controlled by software to optimize its path-planning, enhancing both coverage i.e. canvassing the target area, and network connectivity. The second technical project targets device-to-device communications (D2D) for systems without access to a power grid, through innovations in hardware, software and data management systems, designed to improve crop information on tea estates. A third project focuses on IoT policy analyses at national and organizational levels. The analyses will highlight current trends in Rwandan IoT use, system vulnerabilities, as well as national and organizational policies that may affect data sharing practices and hinder access by Big Data scientists.

This IRES Site Program develops interdisciplinary and creative information technology professionals capable of solving multi-criteria design problems. Through intellectually challenging yet appropriately scoped projects, master's students are exposed to international innovation systems and gain insights into the culture of East Africa. The program's design includes both pre-departure and follow-on courses, insuring effective student preparation and widespread dissemination of results.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research Area
Artificial Intelligence and Big Data
Social and Organizational Informatics

Term
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