This article discusses the architecture of a low-cost unmanned surface vehicle (USV) to be employed for the collection of crucial parameters about water quality in rivers, lakes, or sea. The vehicle, called water environmental mobile observer (WeMo), has been realized exploiting off-the-shelf components and is provided with a modular array of sensors to measure chemical and physical parameters as well as to perform bathymetry. The low-cost requirement is crucial since the vehicle is expected to be replicated in large quantities and then used for pervasive monitoring operations by providing it to local communities, administrations, or even private stakeholders, in order to set up a sort of “social sensor network.” In this sense, data analytics tools have also been introduced in order to automatically drive the vehicle along desired and suitable trajectories and to process the collected data. These data can be used to estimate the parameters of a mathematical model describing the ecological status of the monitored system. In particular, we apply an estimation procedure to a simple mathematical model of oxygen concentration in the water with explicit dependence on biophysical inputs. The estimation provides very satisfying performances, indeed the relative square error is less than 4 . 10-2 . Moreover, once the vehicle is moving along a given trajectory, the status in the spatial domain can be reconstructed also in nonmonitored locations. The whole article aims then at developing a complete monitoring ecosystem covering all the tasks of data collection, storage, and analysis.

中文翻译：

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用于普遍水质监测的低成本无人地面车辆


本文讨论了用于收集河流、湖泊或海洋水质关键参数的低成本无人水面车辆 (USV) 的架构。该车辆被称为水环境移动观测器（WeMo），是利用现成的组件实现的，并配备了模块化传感器阵列，用于测量化学和物理参数以及进行测深。低成本要求至关重要，因为该车辆预计将被大量复制，然后通过将其提供给当地社区、政府部门甚至私人利益相关者来用于普遍的监控操作，以建立一种“社会传感器”网络。”从这个意义上说，还引入了数据分析工具，以便沿着所需且合适的轨迹自动驾驶车辆并处理收集到的数据。这些数据可用于估计描述受监测系统的生态状态的数学模型的参数。特别是，我们将估计程序应用于水中氧浓度的简单数学模型，该模型明确依赖于生物物理输入。该估计提供了非常令人满意的性能，实际上相对平方误差小于 4 。 10-2.此外，一旦车辆沿着给定的轨迹移动，空间域中的状态也可以在非监控位置重建。整篇文章的目标是开发一个完整的监控生态系统，涵盖数据收集、存储和分析的所有任务。