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Robust Data Mule Networks with Remote Healthcare Applications in the Amazon Region: A Fountain Code Approach

机译:具有亚马逊地区的远程医疗保健应用程序的强大数据骡网络:喷泉代码方法

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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.
机译:向巴西亚马逊的偏远和孤立社区提供医疗保健造成重大挑战。在这些地方,医疗检查主要由来自该地区主要城市的医疗团队的零星访问,贝伦。另一种替代方案将是当地护士或技术人员进行常规临床检查,例如孕妇的超声,ELEC,其记录可以被送往贝伦的医生进行评估。然而,由于这些社区中缺乏现代通信基础设施,我们建议使用定期预定的船只作为数据骡子,以确保快速,及时地将这些社区的检查记录交付到城市的医生进行远程分析。由于该地区的恶劣环境,不可预测的船舶延迟和休息,以及高传输故障,要求强大的延迟宽容路由算法设计。本文的主要贡献是两倍:首先,我们建议使用喷泉码,以提高机会数据路由的鲁棒性。其次,我们开发了一种模拟模型,融合了亚马逊河道情景的高不可预测性,船舶延迟/崩溃环境条件和个别数据包损失,以及呈现广泛的模拟结果,以评估我们提出的方法。虽然本文的结果专注于巴西亚马逊的远程医疗保健应用,但我们设想我们的方法也可用于其他远程应用,例如远程教育和其他类似场景。

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