基于低成本微云的水下普适数据科学的海洋雾计算升级

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2022-12-08 DOI:10.1145/3575801
Farooq Dar, M. Liyanage, Marko Radeta, Zhigang Yin, Agustin Zuniga, Sokol Kosta, S. Tarkoma, P. Nurmi, Huber Flores
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引用次数: 2

摘要

由于水下传感器技术部署的增加,水下环境正在成为数据科学的新前沿。在水下操作计算的挑战,加上缺乏覆盖大多数水域的高速通信技术,意味着数据的收集和分析之间存在显着的延迟。这反过来限制了可以基于这些数据操作的应用程序的规模和复杂性。在本文中,我们使用低成本的微云开发水下雾计算支持,并演示如何使用它们为数据量大的水下应用程序提供经济高效的支持。我们开发了一个概念验证微云原型,并使用它来执行广泛的基准测试,以评估水下微云对各种水下数据科学场景的适用性。我们在控制和现场部署中使用河流和海水进行严格的测试。我们还解决了实现水下雾的技术挑战,评估了不同通信接口的性能,并演示了如何使用加速度计来检测通信故障的可能性并确定使用哪种通信接口。我们的工作提供了一种经济有效的方法来增加水下数据科学应用的规模和复杂性,并展示了如何采用现成的设备来实现这一目的。
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Upscaling Fog Computing in Oceans for Underwater Pervasive Data Science Using Low-Cost Micro-Clouds
Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.
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CiteScore
5.20
自引率
3.70%
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0
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