Robust data mule networks with remote healthcare applications in the Amazon region: A fountain code approach

Abstract

Providing healthcare to the remote and isolated communities in the Brazilian Amazon poses a significant challenge. In those places, healthcare examinations are mainly run by sporadic visits from medical teams from the main city in the region, Belem. An alternative would be to have local nurses or technicians perform routine clinical examinations, such as ultrasounds on pregnant women, elec whose records could be sent to the doctors in Belem for evaluation. However, due to the lack of modern communication infrastructure in these communities, we propose the use of regularly scheduled boats as data mules to ensure fast and timely delivery of the examination records from those communities to physicians in the city for remote analysis. Unpredictable boat delays and break-downs, as well as high transmission failures due to the harsh environment in the region, mandate the design of robust delay-tolerant routing algorithms. The main contributions of this paper are two-fold: First, we propose the use of fountain codes in order to improve the robustness of opportunistic data routing. Second, we develop a simulation model that incorporates the high unpredictability of the Amazon riverine scenario, accounting for boat delays/breakdowns environmental conditions and individual packet losses, and present extensive simulations results to evaluate our proposed approaches. While the results in this paper focus on remote healthcare applications in the Brazilian Amazon, we envision that our approach may also be used for other remote applications, such as distance education, and other similar scenarios.