scholarly journals H2O Sense: a WSN-based monitoring system for fish tanks

2020 ◽  
Vol 2 (10) ◽  
Author(s):  
Khushboo Qayyum ◽  
Idrees Zaman ◽  
Anna Förster

Abstract In oceans, fish usually live in an environment that is best suited for their growth. When these fish are introduced into man-made environment, e.g. in mariculture and aquaculture set-ups, the physical parameters might stray from their ideal values, resulting in improper growth and undesired outcomes. Hence, to prevent these undesirable outcomes, continuous monitoring of the physical parameters of the water such as pH, temperature and dissolved oxygen is required. In this work, we present a system called H2O sense, which continuously monitors the physical parameters of the water in tanks and alerts the user in case the values deviate from ideal. We use only low-power, low-cost hardware and open-source development tools, which makes the system easily applicable to various settings. The deployment of our system in the Maritime Laboratory of the University of Namibia shows its efficacy. Furthermore, we evaluate in detail the performance of our system and discuss its applicability in similar challenged environments.

2021 ◽  
Author(s):  
Elias Dimitriou ◽  
Georgios Poulis ◽  
Anastasios Papadopoulos

<p>Good water quality status in rivers and lakes is vital for both human well-being and biodiversity conservation and requires efficient monitoring and restoration strategies. This is reflected in an increasing number of International and National legislations which enforce water resources management and monitoring at a basin scale.</p><p>For this purpose, state-of-the-art monitoring schemes have been developed by using low-cost, technologically advanced sensors and Internet of Things (IoT) infrastructure. Remote sensing offers also a good water monitoring alternative but is more appropriate for medium to large water bodies with less dynamic character in comparison to small scale, temporary rivers.</p><p>Recent technological advances in sensors technology, energy supply, telecommunication protocols and data handling, facilitate the use of automated monitoring stations, but still, deployment of extended networks with readily available data remains far from common practice. Installation and operational costs for the development of such monitoring networks are among the most commonly faced challenges.</p><p>The main aim of this effort is to present the development of a network of automatic monitoring stations that measure in near real time water level and physicochemical parameters in several Greek rivers. This infrastructure has been developed under the project “Open ELIoT” (Open Internet of Things infrastructure for online environmental services - https://www.openeliot.com/en/), which was funded by the Greek National Structural Funds. It includes a low cost and easy to produce hardware node, coupled with commercial sensors of industrial specifications, as well as an IoT data platform, elaborating and presenting data, based on open technologies.</p><p>During its initial operation phase, the system has been deployed in sites with different hydrological regimes and various pressures to water quality, including (a) an urban Mediterranean stream (Pikrodafni stream), and (b) the urban part of a continental river running through an agricultural area (Lithaios stream).</p><p>Preliminary data on the continuous monitoring of sites (a) and (b) are presented here, reflecting the differences in pressures to the respective water bodies. Pikrodafni stream which is located close to the center of Athens – Greece and receives a lot of pressure from urban waste, illustrates Dissolved Oxygen (DO) concentration with a heavily skewed distribution towards low values (mean value: 2.15 mg/l and median: 0.93 mg/l). On the contrary, in Lithaios stream, which is more affected by agricultural runoff, dissolved oxygen data approach a normal distribution (mean value: 6.93 mg/l and median: 7.03 mg/l). The 25<sup>th</sup> and 75<sup>th</sup> percentiles in Pikrodafni stream are: 0.1 mg/l and 3.47 mg/l respectively while in Lithaios stream are: 5.6 mg/l and 8.45 mg/l. The average water temperature is similar to both streams (18.8 oC in Pikrodafni and 16.2 oC in Lithaios). Therefore, the significant differences in DO concentrations between the two streams indicate the need for continuous monitoring of data that facilitates the identification of pressures and enables stakeholders to respond to pollution events in time.</p>


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Takeshi Tsuji ◽  
Tatsunori Ikeda ◽  
Ryosuke Matsuura ◽  
Kota Mukumoto ◽  
Fernando Lawrens Hutapea ◽  
...  

AbstractWe have developed a new continuous monitoring system based on small seismic sources and distributed acoustic sensing (DAS). The source system generates continuous waveforms with a wide frequency range. Because the signal timing is accurately controlled, stacking the continuous waveforms enhances the signal-to-noise ratio, allowing the use of a small seismic source to monitor extensive areas (multi-reservoir). Our field experiments demonstrated that the monitoring signal was detected at a distance of ~ 80 km, and temporal variations of the monitoring signal (i.e., seismic velocity) were identified with an error of < 0.01%. Through the monitoring, we identified pore pressure variations due to geothermal operations and rains. When we used seafloor cable for DAS measurements, we identified the monitoring signals at > 10 km far from the source in high-spatial resolution. This study demonstrates that multi-reservoir in an extensive area can be continuously monitored at a relatively low cost by combining our seismic source and DAS.


2020 ◽  
Author(s):  
Ramesh Madipally ◽  
Sheela Nair L ◽  
Rui Taborda

&lt;p&gt;In recent years, Coastal video monitoring methods have been widely accepted tools for continuous monitoring of complex coastal processes. In this paper, the progress made on a new python based coastal video monitoring system, PI-COSMOS (Portuguese Indian COaStal MOnitoring System) which is being developed and tested jointly in India and Portuguese coasts is presented. PI-COSMOS system aims at providing open source, high speed video monitoring toolboxes for the coastal community that can be used anywhere in the world. PI-COSMOS is camera independent system and comprises four modules viz. PI-Calib for camera calibration, RectiPI for video imagery rectification, PI-ImageStacks for image product and pixel product generation and PI- DB for efficient database management. The applicability of PICOSMOS system under different coastal environment conditions has been tested using the data collected from the India as well as the Portugal coast. The results from one of the Indian stations installed at Kozhikode beach, Kerala, India situated at 11&amp;#176;15'14.12&quot; N, 75&amp;#176; 46'15.40&quot; E are presented here to demonstrate the capabilities of the newly developed PI-COSMOS system. the performance of PI-COSMOS is evaluated by conducting a comparative study among PICOSMOS and existing video monitoring toolboxes like UAV processing toolbox provided by Coastal Research Imaging Network and RectifyExtreme provided by the University of Lisbon and it is found that the processing speed of PI-COSMOS is very much high i.e. more than 5 times when compared to UAV processing toolbox and RectifyExtreme.&amp;#160; The high speed performance, camera independent nature and easiness in the operation made PI-COSMOS as the simplest and advanced open source video monitoring system.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;


2021 ◽  
Author(s):  
Takeshi Tsuji ◽  
Tatsunori Ikeda ◽  
Ryosuke Matsuura ◽  
Kota Mukumoto ◽  
Hutapea Lawrens ◽  
...  

Abstract We have developed a new continuous monitoring system based on small seismic sources and distributed acoustic sensing (DAS). The source system generates continuous waveforms with a wide frequency range. Because the signal timing is accurately controlled, stacking continuous waveforms enhances the signal-to-noise ratio, allowing the use of a small seismic source to monitor extensive areas (multi-reservoir). Our field experiments demonstrated that the monitoring signal was detected at a distance of ~80 km, and temporal variations of the monitoring signal (i.e., seismic velocity) were identified with >99.9% accuracy. Through the monitoring, we identified pore pressure variations due to geothermal operations and rains. When we used seafloor cable for DAS measurements, we identified the monitoring signals at >10 km far from the source in high-spatial resolution. This study demonstrates that multi-reservoir in an extensive area can be continuously monitored at a relatively low cost by combining our seismic source and DAS technology.


2014 ◽  
Vol 71 (4) ◽  
Author(s):  
E.E. Eyo ◽  
T. A. Musa ◽  
K. M. Omar ◽  
K. M. Idris ◽  
T. Bayrak ◽  
...  

The main goal of our ongoing research is to design a low-cost continuous monitoring system for landslide investigation using the Reverse RTK (RRTK) technique. The main objectives of this paper are to review the existing Global Positioning System (GPS) tools and techniques used for landslide monitoring, and to propose a novel low-cost landslide monitoring technique using Reverse RTK GPS. A general overview of GPS application in landslide monitoring is presented, followed by a review of GPS deformation monitoring systems and some of the factors used for their categorization. Finally, the concept, principles and advantages of the proposed new landslide monitoring system are discussed.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7703
Author(s):  
Manal Mohamed ◽  
Eunjung Jo ◽  
Nourelhuda Mohamed ◽  
Minhee Kim ◽  
Jeong-dae Yun ◽  
...  

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of −115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.


2020 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Rowida Meligy ◽  
Peio Lopez-Iturri ◽  
José Javier Astrain ◽  
Imanol Picallo ◽  
Hicham Klaina ◽  
...  

This paper presents the design of a cost-effective online wireless monitoring system for two linear Fresnel reflector (LFR) solar plants located in two different countries. The first LFR plant is installed in the SEKEM medical center near Belbis city, Egypt, while the second is installed in the campus of the University of Palermo, Italy. The proposed system is a standalone system that reduces the interaction of labor as it offers online wireless monitoring for important parameters of the LFR such as solar irradiance, ambient temperature, outlet and inlet collector temperature and heat transfer fluid flow. For that purpose, a wireless sensor network (WSN) based on Arduino Mega boards coupled with XBee modules are used. The ZigBee XBee modules operate at 2.4 GHz, which have the advantages of being low cost and relatively low power consumption. The wireless nodes are supplied by solar paneled power banks, and send the data to a cloud in order to monitor both LFR plants remotely. The proposed system has been implemented and tested successfully before the future deployment on the LFR plants.


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