A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems

Low-Cost Water Quality Sensors for IoT: A Systematic Review

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.

Contactless Sensing of Water Properties for Smart Monitoring of Pipelines

A key milestone for the pervasive diffusion of wireless sensing nodes for smart monitoring of water quality and quantity in distribution networks is the simplification of the installation of sensors. To address this aspect, we demonstrate how two basic contactless sensors, such as piezoelectric transducers and strip electrodes (in a longitudinal interdigitated configuration to sense impedance inside and outside of the pipe with potential for impedimetric leak detection), can be easily clamped on plastic pipes to enable the measurement of multiple parameters without contact with the fluid and, thus, preserving the integrity of the pipe. Here we report the measurement of water flow rate (up to 24 m³/s) and temperature with ultrasounds and of the pipe filling fraction (capacitance at 1 MHz with ~cm³ resolution) and ionic conductivity (resistance at 20 MHz from 700 to 1400 μS/cm) by means of impedance. The equivalent impedance model of the sensor is discussed in detail. Numerical finite-element simulations, carried out to optimize the sensing parameters such as the sensing frequency, confirm the lumped models and are matched by experimental results. In fact, a 6 m long, 30 L demonstration hydraulic loop was built to validate the sensors in realistic conditions (water speed of 1 m/s) monitoring a pipe segment of 0.45 m length and 90 mm diameter (one of the largest ever reported in the literature). Tradeoffs in sensors accuracy, deployment, and fabrication, for instance, adopting single-sided flexible PCBs as electrodes protected by Kapton on the external side and experimentally validated, are discussed as well.

An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring

The development of remote sensing technology has passed an effective means for forest resource management and monitoring, but remote sensing technology is limited by sensor hardware equipment, and the quality of remote sensing image data is low, which is difficult to meet the needs of forest resource change monitoring. This paper presents a remote sensing image classification method based on the combination of the SSIF algorithm and wavelet denoising. Forest information is extracted from PALSAR/PALSAR-2 radar remote sensing data. The forest distribution map is generated by pixel level fusion algorithm, and the accuracy of the forest distribution map is evaluated by a confusion matrix. The remote sensing image is spatio-temporal fused by the SSIF algorithm to capture more details of forest distribution. The simulation analysis shows that the overall accuracy of the forest classification results obtained by the fusion algorithm is 96% ± 1, and the kappa coefficient is 0.66. The accuracy of forest recognition meets the requirements.

Dataset on water quality monitoring from a wireless sensor network in a river in Kosovo

This dataset was collected as part of the InWaterSense project with a wireless sensor network (WSN) installed in a site in river Sitnica in Kosovo, as a case study for monitoring remotely, continuously and in real-time the surface water quality in Kosovo and how it can be extended to all surface waters in the country for quality assurance. Values of four water quality parameters are provided in the dataset, i.e., temperature, electrical conductivity, pH, and dissolved oxygen measured by respective static sensors of WSN in the time frame between May 2015 to beginning of January 2016 and every 10 min, counting to slightly over 100k measurement records in total. The dataset is hosted at the Mendeley Data repository (Ahmedi and Ahmedi 2021), and is related to the research article entitled "InWaterSense: An Intelligent Wireless Sensor Network for Monitoring Surface Water Quality to a River in Kosovo" (Ahmedi et al., 2018). The reuse potential of the dataset to the scientific community is widespread, from environmental engineering to artificial intelligence to the health sector just to mention few. Moreover, practitioners might benefit from this dataset in driving forth the pollution prevention policies and techniques. Data were acquired measuring water quality using static sensors installed as part of a wireless sensor network in Sitnica river in the Plemetin village near Prishtina, then transmitted to the gateway device also in Plemetin via the ZigBee protocol, and finally transmitted remotely via GPRS to the server machine in the premises of the University of Prishtina. The data received from sensors are in real-time stored in the MS SQL server.

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.

A Comprehensive Review on Water Quality Monitoring Devices: Materials Advances, Current Status, and Future Perspective

Water quality monitoring has become more critical in recent years to ensure the availability of clean and safe water from natural aquifers and to understand the evolution of water contaminants across time and space. The conventional water monitoring techniques comprise of sample collection, preservation, preparation, tailed by laboratory testing and analysis with cumbersome wet chemical routes and expensive instrumentation. Despite the high accuracy of these methods, the high testing costs, laborious procedures, and maintenance associated with them don't make them lucrative for end end-users and field testing. As the participation of ultimate stakeholders, that is, common man for water quality and quantity can play a pivotal role in ensuring the sustainability of our aquifers, thus it is essential to develop and deploy portable and user-friendly technical systems for monitoring water sources in real-time or on-site. The present review emphasizes here on possible approaches including optical (absorbance, fluorescence, colorimetric, X-ray fluorescence, chemiluminescence), electrochemical (ASV, CSV, CV, EIS, and chronoamperometry), electrical, biological, and surface-sensing (SPR and SERS), as candidates for developing such platforms. The existing developments, their success, and bottlenecks are discussed in terms of various attributes of water to escalate the essentiality of water quality devices development meeting ASSURED criterion for societal usage. These platforms are also analyzed in terms of their market potential, advancements required from material science aspects, and possible integration with IoT solutions in alignment with Industry 4.0 for environmental application.

An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application

As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.

Handheld Magnetic-Compliant Gamma-Ray Spectrometer for Environmental Monitoring and Scrap Metal Screening

Spotting radioactive material in waste is of paramount importance for environment protection. This is particularly challenging when orphan sources are hidden in scrap metal that shields their activity from the traditional detectors in the portals scanning incoming trucks. In order to address this issue, we present a wireless and compact SiPM-based gamma spectrometer compatible with strong magnetic fields (0.1 T) to be installed in the bore of the lifting electromagnets to scan reduced volumes of metal and thus achieve higher sensitivity. The microcontroller-based instrument provides 11% energy resolution (at 662 keV), an energy range from 60 keV to 1.5 MeV, a max. count rate of 30 kcps, a weight <1 kg, and a power consumption <1 W. The results of its extensive characterization in the laboratory and its validation in the field, including operation in a scrap yard as well as on a drone, are reported.

An ensemble deep neural network approach for predicting TOC concentration in lakes along the middle-lower reaches of Yangtze River

In recent years, lakes pollution has become increasingly serious, so water quality monitoring is becoming increasingly important. The concentration of total organic carbon (TOC) in lakes is an important indicator for monitoring the emission of organic pollutants. Therefore, it is of great significance to determine the TOC concentration in lakes. In this paper, the water quality dataset of the middle and lower reaches of the Yangtze River is obtained, and then the temperature, transparency, pH value, dissolved oxygen, conductivity, chlorophyll and ammonia nitrogen content are taken as the impact factors, and the stacking of different epochs’ deep neural networks (SDE-DNN) model is constructed to predict the TOC concentration in water. Five deep neural networks and linear regression are integrated into a strong prediction model by the stacking ensemble method. The experimental results show the prediction performance, the Nash-Sutcliffe efficiency coefficient (NSE) is 0.5312, the mean absolute error (MAE) is 0.2108 mg/L, the symmetric mean absolute percentage error (SMAPE) is 43.92%, and the root mean squared error (RMSE) is 0.3064 mg/L. The model has good prediction performance for the TOC concentration in water. Compared with the common machine learning models, traditional ensemble learning models and existing TOC prediction methods, the prediction error of this model is lower, and it is more suitable for predicting the TOC concentration. The model can use a wireless sensor network to obtain water quality data, thus predicting the TOC concentration of lakes in real time, reducing the cost of manual testing, and improving the detection efficiency.

Environmental Monitoring of the Littoral Zone of Lake Baikal Using a Network of Automatic Hydro-Meteorological Stations: Development and Trial Run

An automatic hydro-meteorological station (AHMS) was designed to monitor the littoral zone of Lake Baikal in areas with high anthropogenic pressure. The developed AHMS was installed near the Bolshiye Koty settlement (southern basin). This AHMS is the first experience focused on obtaining the necessary competencies for the development of a monitoring network of the Baikal natural territory. To increase the flexibility of adjustment and repeatability, we developed AHMS as a low-cost modular system. AHMS is equipped with a weather station and sensors measuring water temperature, pH, dissolved oxygen, redox potential, conductivity, chlorophyll-a, and turbidity. This article describes the main AHMS functions (hardware and software) and measures taken to ensure data quality control. We present the results of the first two periods of its operation. The data acquired during this periods have demonstrated that, to obtain accurate measurements and to detect and correct errors that were mainly due to biofouling of the sensors and calibration bias, a correlation between AHMS and laboratory studies is necessary for parameters such as pH and chlorophyll-a. The gained experience should become the basis for the further development of the monitoring network of the Baikal natural territory.

Microbial biofilms in biorefinery - Towards a sustainable production of low-value bulk chemicals and fuels

Harnessing the potential of biocatalytic conversion of renewable biomass into value-added products is still hampered by unfavorable process economics. This has promoted the use of biofilms as an alternative to overcome the limitations of traditional planktonic systems. In this paper, the benefits and challenges of biofilm fermentations are reviewed with a focus on the production of low-value bulk chemicals and fuels from waste biomass. Our study demonstrates that biofilm fermentations can potentially improve productivities and product yields by increasing biomass retention and allowing for continuous operation at high dilution rates. Furthermore, we show that biofilms can tolerate hazardous environments, which improve the conversion of crude biomass under substrate and product inhibitory conditions. Additionally, we present examples for the improved conversion of pure and crude substrates into bulk chemicals by mixed microbial biofilms, which can benefit from microenvironments in biofilms for synergistic multi-species reactions, and improved resistance to contaminants. Finally, we suggest the use of mathematical models as useful tools to supplement experimental insights related to the effects of physico-chemical and biological phenomena on the process. Major challenges for biofilm fermentations arise from inconsistent fermentation performance, slow reactor start-up, biofilm carrier costs and carrier clogging, insufficient biofilm monitoring and process control, challenges in reactor sterilization and scale-up, and issues in recovering dilute products. The key to a successful commercialization of the technology is likely going to be an interdisciplinary approach. Crucial research areas might include genetic engineering combined with the development of specialized biofilm reactors, biofilm carrier development, in-situ biofilm monitoring, model-based process control, mixed microbial biofilm technology, development of suitable biofilm reactor scale-up criteria, and in-situ product recovery.

IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

Safe water is becoming a scarce resource, due to the combined effects of increased population, pollution, and climate changes. Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback. Recently developed systems utilizing wireless sensor network (WSN) technology have reported weaknesses in energy management, data security, and communication coverage. Due to the recent advances in Internet-of-Things (IoT) that can be applied in the development of more efficient, secure, and cheaper systems with real-time capabilities, we present here a survey aimed at summarizing the current state of the art regarding IoT based smart water quality monitoring systems (IoT-WQMS) especially dedicated for domestic applications. In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS via a proposed empirical metric, analysis, and discussion and, finally, design recommendations for an efficient system. No doubt, this study will benefit the developing field of smart homes, offices, and cities.

AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

Miniaturized Pervasive Sensors for Indoor Health Monitoring in Smart Cities

Sensors and electronics technologies are pivotal in several fields of science and engineering, especially in automation, industry and environment monitoring. Over the years, there have been continuous changes and advancements in design and miniaturization of sensors with the growth of their application areas. Challenges have arisen in the deployment, fabrication and calibration of modern sensors. Therefore, although the usage of sensors has greatly helped improving the quality of life, especially through their employment in many IoT (Internet of Things) applications, some threats and safety issues still remain unaddressed. In this paper, a brief review focusing on pervasive sensors used for health and indoor environment monitoring is given. Examples of technology advancements in air, water and radioactivity are discussed. This bird’s eye view suggests that solid-state pervasive sensors have become essential parts of all emerging applications related to monitoring of health and safety. Miniaturization, in combination with gamification approaches and machine learning techniques for processing large amounts of captured data, can successfully address and solve many issues of massive deployment. The development paradigm of Smart Cities should include both indoor and outdoor scenarios.

A New Ammonium Smart Sensor with Interference Rejection

In many water samples, it is important to determine the ammonium concentration in order to obtain an overall picture of the environmental impact of pollutants and human actions, as well as to detect the stage of eutrophization. Ion selective electrodes (ISEs) have been commonly utilized for this purpose, although the presence of interfering ions (potassium and sodium in the case of NH₄⁺-ISE) represents a handicap in terms of the measurement quality. Furthermore, random malfunctions may give rise to incorrect measurements. Bearing all of that in mind, a smart ammonium sensor with enhanced features has been developed and tested in water samples, as demonstrated and commented on in detail following the presentation of the complete set of experimental measurements that have been successfully carried out. This has been achieved through the implementation of an expert system that supervises a set of ISEs in order to (a) avoid random failures and (b) reject interferences. Our approach may also be suitable for in-line monitoring of the water quality through the implementation of wireless sensor networks.

Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica

In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (Remote Control, Image Acquisition, Operation Maintenance, and Document Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China’s Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures

Monitoring contemporary water distribution networks (WDN) relies increasingly on smart metering technologies and wireless sensor network infrastructures. Smart meters and sensor nodes are deployed to capture and transfer information from the WDN to a control center for further analysis. Due to difficulties in accessing the water assets, many water utility companies employ battery-powered nodes, which restricts the use of high sampling rates, thus limiting the knowledge we can extract from the recorder data. To mitigate this issue, compressive sensing (CS) has been introduced as a powerful framework for reducing dramatically the required bandwidth and storage resources, without diminishing the meaningful information content. Despite its well-established and mathematically rigorous foundations, most of the focus is given on the algorithmic perspective, while the real benefits of CS in practical scenarios are still underexplored. To address this problem, this work investigates the advantages of a CS-based implementation on real sensing devices utilized in smart water networks, in terms of execution speedup and reduced ener experimental evaluation revealed that a CS-based scheme can reduce compression execution times around 50 % , while achieving significant energy savings compared to lossless compression, by selecting a high compression ratio, without compromising reconstruction fidelity. Most importantly, the above significant savings are achieved by simultaneously enabling a weak encryption of the recorded data without the need for additional encryption hardware or software components.

AC and DC Differential Bridge Structure Suitable for Electrochemical Interfacial Capacitance Biosensing Applications

This paper presents a capacitive differential bridge structure with both AC and DC excitation and balancing capability for low cost electrode-solution interfacial capacitance biosensing applications. The proposed series RC balancing structure offers higher sensitivity, lower susceptibility to common-mode interferences, and drift control. To evaluate the bridge performance in practice, possible effects of initial bridge imbalance due to component mismatches are investigated considering the required resolution of the balancing networks, sensitivity, and linearity. This evaluation is also a guideline to designing the balancing networks, balancing algorithm and the proceeding readout interface circuitry. The proposed series RC bridge structure is implemented along with a custom single frequency real-time amplification/filtering readout board with real-time data acquisition and sine fitting. The main specifications for the implemented structure are 8-bit detection resolution if the total expected fractional capacitance change at the interface is roughly 1%. The characterization and measurement results show the effectiveness of the proposed structure in achieving the design target. The implemented structure successfully achieves distinct detection levels for tiny total capacitance change at the electrode-solution interface, utilizing Microcystin-(Leucine-Arginine) toxin dilutions as a proof of concept.