Dynamic Sensor Nodes Distribution with Coordinated Autonomous Vehicles for Environment Pollution Monitoring and Modeling

Alfa Budiman, Wenbo Wu, Edisson A. Naula-Duchi, Patricia Portillo Jiménez, Hanifeh Imanian, P. Payeur, Luis E. GarzaCastañón, A. Mohammadian, E. Lanteigne
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Abstract

– This research aims toward collecting air and water samples over opportunistically selected locations to monitor pollutants distribution. To support precise sensor nodes deployment over a variety of terrains and changing conditions, not only appropriate sensor devices must be designed, but means for deployment must also be carefully studied, developed, and implemented. This paper investigates methodologies to efficiently distribute environment sensor nodes while maximizing space coverage, minimizing acquisition time, and leveraging the benefits of autonomous robotic agents to carry environmental sensors to strategic locations. The research contributes to fill existing gaps in local and global sensor networks for environment pollution monitoring by developing innovative technologies to dynamically deploy sensor nodes using mobile unmanned ground, air and water vehicles. The dispatch of dynamic sensor nodes on autonomous robotic agents to collect measurements on pollution can efficiently cover territories of different size, automatically detect areas where pollution varies significantly or reaches concerning levels, and strategically concentrate data acquisition over those regions to support the formation of more accurate data-centric pollutants dispersion models.
基于协调自动驾驶车辆的环境污染监测与建模动态传感器节点分布
-这项研究的目的是收集空气和水的样本,在机会主义选择的地点监测污染物的分布。为了在各种地形和不断变化的条件下支持精确的传感器节点部署,不仅必须设计合适的传感器设备,而且还必须仔细研究、开发和实施部署手段。本文研究了有效分布环境传感器节点的方法,同时最大化空间覆盖,最小化获取时间,并利用自主机器人代理的优势将环境传感器运送到战略位置。该研究通过开发创新技术,利用移动无人地面、空中和水上车辆动态部署传感器节点,有助于填补当地和全球环境污染监测传感器网络的现有空白。在自主机器人代理上调度动态传感器节点收集污染测量数据,可以有效地覆盖不同大小的区域,自动检测污染变化显著或达到相关水平的区域,并在这些区域战略性地集中数据采集,以支持形成更准确的以数据为中心的污染物扩散模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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