This paper 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 WeMo (Water Environmental Mobile Observer) 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 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 that 4∙10 -2. Moreover, once the vehicle is moving along a given trajectory, the status in the spatial domain can be reconstructed also in non monitored locations. The whole paper aims then at developing a complete monitoring ecosystem covering all the tasks of data collection, storage and analysis.
1 August 2014 in Estuarine, Coastal and Shelf Science
The aim of this article is to discuss the usability of the long-range (LoRa) transmission technology together with the LoRa wide-area network (LoRaWAN) protocol for underground monitoring activities. In particular, this article focuses on the transmission performances in different soils (i.e., gravel, sand, and clay), for an underground-to-aboveground (UG2AG) communication. The three soils have been chosen in order to test the system behavior in case of pure soil compositions, in order to provide a general result that can be used to evaluate the transmission chances for any kind of soil. The performances of the transmission channel have been tested using an experimental setup for depths up to 50 cm, acquiring the values of the received signal strength indicator (RSSI) and the signal-to-noise ratio (SNR) for every transmission and analyzing the packet loss (PL). Such a kind of system may be crucial in several application scenarios, such as environmental monitoring or smart agriculture, where the real-time, remote acquisition of underground parameters at different depths is required.