Real-time Mobile Sensor Management Framework for city-scale environmental monitoring
Kun Qian, Christian G. Claudel

TL;DR
This paper presents a real-time mobile sensor scheduling framework for city-scale environmental monitoring, specifically for flash flood detection, using predictive modeling and deep learning to improve decision-making over existing methods.
Contribution
The paper introduces a novel real-time sensor task scheduling algorithm that integrates predictive flood modeling and deep learning, enhancing large-scale environmental monitoring efficiency.
Findings
Outperforms myopic and heuristic sensor placement algorithms
Uses deep learning models for accurate flood state estimation
Achieves better monitoring results in urban flood scenarios
Abstract
Environmental disasters such as flash floods are becoming more and more prevalent and carry an increasing burden on human civilization. They are usually unpredictable, fast in development, and extend across large geographical areas. The consequences of such disasters can be reduced through better monitoring, for example using mobile sensing platforms that can give timely and accurate information to first responders and the public. Given the extended scale of the areas to monitor, and the time-varying nature of the phenomenon, we need fast algorithms to quickly determine the best sequence of locations to be monitored. This problem is very challenging: the present informative mobile sensor routing algorithms are either short-sighted or computationally demanding when applied to large scale systems. In this paper, a real-time sensor task scheduling algorithm that suits the features and…
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