Title | ||
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Assessing the Feasibility of Exploiting Edge Computing for Real- Time Monitoring of Flash Floods |
Abstract | ||
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Monitoring flash floods and providing just-in-time notification to city officials for taking appropriate action and prompt intervention is crucial for any smart city located in flood-prone areas around the world. Flood monitoring systems that exploit image analysis via Machine Learning (ML) techniques have been already proposed in literature. Such systems, however, adopt a cloud-based approach that generates significant data traffic and could be susceptible to failures due to network outages. In such a framework, images are continuously offloaded from cameras deployed in flood-prone areas of the city towards a cloud infrastructure where a service is deployed to analyze the images and detect the rise of water in rivers or city canals in a timely way. In this paper, we present the activities of the project EdgeFlooding, which aims at investigating the opportunity of adopting a distributed approach based on edge computing for the implementation of more resilient and reliable flash flood monitoring systems, that helps mitigate the limitations of the cloud-based systems. We have developed a prototype of an edge computing flood monitoring system based on micro-services, and we run an extensive set of experiments exploiting one European Fed4Fire+ testbed, i.e., the Grid'5000 testbed. The aim of those experiments is to assess whether a distributed edge/cloud computing approach is feasible for the implementation of future flood or environmental monitoring systems. |
Year | DOI | Venue |
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2022 | 10.1109/SMARTCOMP55677.2022.00068 | 2022 IEEE International Conference on Smart Computing (SMARTCOMP) |
Keywords | DocType | ISSN |
Cloud Computing,EdgeFlooding,Edge computing | Conference | 2693-8332 |
ISBN | Citations | PageRank |
978-1-6654-8153-3 | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Francesca Righetti | 1 | 4 | 3.37 |
Carlo Vallati | 2 | 0 | 2.03 |
Andrea Klaus Tubak | 3 | 0 | 0.34 |
Nirmalya Roy | 4 | 0 | 0.34 |
Bipendra Basnyat | 5 | 4 | 3.57 |
Giuseppe Anastasi | 6 | 47 | 4.14 |