Assessing the Feasibility of Exploiting Edge Computing for Real-Time Monitoring of Flash Floods

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.