Real-time Mobile Sensor Management Framework for city-scale environmental monitoring

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 needs of city-scale environmental monitoring tasks is proposed. The algorithm is run in forward search and makes use of the predictions of an associated distributed parameter system, modeling flash flood propagation. It partly inherits the causal relation expressed by a search tree, which describes all possible sequential decisions. The computationally heavy data assimilation steps in the forward search tree are replaced by functions dependent on the covariance matrix between observation sets. Taking flood tracking in an urban area as a concrete example, numerical experiments in this paper indicate that this scheduling algorithm can achieve better results than myopic planning algorithms and other heuristics based sensor placement algorithms. Furthermore, this paper relies on a deep learning-based data-driven model to track the system states, and experiments suggest that popular estimation techniques have very good performance when applied to precise data-driven models.