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Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication
Abstract
The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.
... The integration of IoT sensors allows continuous monitoring, providing insight into complex interrelationships among water quality parameters [25][26][27]. This facilitates prompt anomaly detection and informed conservation efforts, including citizen science [28]. ...
This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.
Citizen science is increasingly being promoted as a means of gathering more data to help inform the management of ecosystems. Involving the participants in the design of data collection activities is a form of co‐design often proposed by those calling for a translational ecology.
In addition, novel monitoring approaches have the potential to improve the quality of data collected by citizen scientists. We explored the potential of environmental DNA (eDNA) for vertebrate (mainly fish) species monitoring through a co‐designed catchment monitoring strategy.
Having been introduced to the potential of eDNA, citizen scientists designed and executed an eDNA‐based survey of a small chalk stream catchment to explore questions of concern.
The eDNA survey provided data about fish and other vertebrate diversity in the catchment which would have otherwise required sampling approaches difficult for citizen scientists. These data give a preliminary answer to some of the citizen scientists' priority questions and are comparable to fish data collected through traditional electrofishing surveys.
Recommendations are offered for co‐design and the use of novel research techniques by citizen scientists.
Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.
The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.
Wireless Sensor Network (WSN) consists of several Sensor Nodes (SN) for monitoring various applications and sensing the environmental data. The WSN gathers and compiles the detected data before sending it to the Base Station (BS). The nodes have limited battery power, so efficient data transmission techniques and data collection methods are required to enhance the sensor network lifetime. In this paper, the Particle Swarm Optimization (PSO) method is utilized to form the cluster, and a Fuzzy based Energy Efficient Routing Protocol (E-FEERP) is proposed using average distance of SN from BS, node density, energy and communication quality to transmit data from cluster head to the BS in an optimal manner. The proposed protocol used parallel fitness function computing to quickly converge to the best possible solution with fewer iterations. The protocol used PSO-based clustering algorithm that recognize how birds act when they are in a flock. It is an optimization strategy that uses parallel fitness function computing to get to an optimal solution quickly and with a small number of iterations. Fuzzy is combined with PSO to increase coverage with reduced computational overhead. The proposed E-FEERP improves network performance in terms of packet delivery ratio, Residual Energy (RE), throughput, energy consumption, load balancing ratio, and network lifetime.
Abstract Citizen‐science projects focused on ecology and conservation have been growing in popularity in recent years, offering many opportunities for researchers and volunteers alike. Two principal approaches to citizen‐science projects in ecology can be characterized as the data‐first approach and the question‐first approach. Here, we highlight the value of question‐first citizen‐science projects for providing insights into the ecology and management of urban wildlife, using case studies on (1) beneficial insects (pollinators, predators and parasitoids) and (2) possums and gliders in Australian cities and towns. The question‐first approach has many benefits, offering a platform to engage volunteers with the scientific process and the broader context of an ecological or conservation problem, while also connecting them with their local environment. Identifying the questions to be addressed in a citizen‐science project ahead of data collection allows for co‐design and stronger collaboration with volunteers, community groups, local experts, and landscape managers. Question‐first citizen science can also provide valuable ecological data that extend substantially beyond presence‐only records, including presence‐absence data collected via timed surveys and information on animal behavior and interspecific interactions. However, establishing and maintaining question‐first citizen‐science projects can be challenging, requiring the building and maintenance of many relationships and a multidisciplinary approach that goes well beyond the usual activities of an academic researcher. Well‐designed, question‐first citizen science has the capacity to achieve both scientific rigor and meaningful engagement with volunteer participants.
span lang="EN-US">Water pollution is one of the most serious environmental problems in Malaysia. The most notable occurrence of pollution happened in Selangor. Currently, there are various water quality monitoring (WQM) methods to observe the quality of water. One of the methods used is the internet of things (IoT) for wireless sensor network technology to obtain real-time data measurement. In this study, the developed WQM system is equipped with a sensor that can measure total dissolved solid (TDS) and electrical conductivity (EC). Arduino UNO was used in this system as a microcontroller to interact with the sensor. The Wi-Fi module, ESP8266, was used to transfer the collected data to ThingSpeak, which acts as a cloud to store all the data. The results showed that both sample populations can be discriminated since the p-value is greater than 0.05 in the normality test, while in the paired sample t-test, the p-value is less than 0.05. In conclusion, this research provides an easier way to monitor water quality by taking up less time at less cost, as well as being reliable in giving real-time data reading.</span
High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.
The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?
Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water quantity and that near‐term water quality forecasts are fewer in number and in early stages of development (i.e., non‐operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
This invited paper presents potential solutions for tackling some of the main underlying challenges toward developing sustainable Internet-of-things (IoT) devices with a focus on eco-friendly manufacturing, sustainable powering, and wireless connectivity for next-generation IoT devices. The diverse applications of IoT systems, such as smart cities, wearable devices, self-driving cars, and industrial automation, are driving up the number of IoT systems at an unprecedented rate. In recent years, the rapidly-increasing number of IoT devices and the diverse application-specific system requirements have resulted in a paradigm shift in manufacturing processes, powering methods, and wireless connectivity solutions. The traditional cloud-centering IoT systems are moving toward distributed intelligence schemes that impose strict requirements on IoT devices, e.g., operating range, latency, and reliability. In this article, we provide an overview of hardware-related research trends and application use cases of emerging IoT systems and highlight the enabling technologies of next-generation IoT. We review eco-friendly manufacturing for next-generation IoT devices, present alternative biodegradable and eco-friendly options to replace existing materials, and discuss sustainable powering IoT devices by exploiting energy harvesting and wireless power transfer. Finally, we present (ultra-)low-power wireless connectivity solutions that meet the stringent energy efficiency and data rate requirements of future IoT systems that are compatible with a batteryless operation.
Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.
In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.
Several studies in Lake Tanganyika have effectively employed traditional methods to explore changes in water quality in open waters; however, coastal monitoring has been restricted and sporadic, relying on costly sample and analytical methods that require skilled technical staff. This study aims in validating citizen science water quality collected data (nitrate, phosphate and turbidity) with those collected and measured by professional scientists in the laboratory. A second objective of the study is to use citizen scientist data to identify the patterns of seasonal and spatial variations in nutrient conditions and forecast potential changes based on expected changes in population and climate (to 2050). The results showed that the concentrations of nitrate and phosphate measured by citizen scientists nearly matched those established by professional scientists, with overall accuracy of 91% and 74%, respectively. For total suspended solids measured by professional and turbidity measured by citizen scientists, results show that, using 14 NTU as a cut-off, citizen scientist measurements of Secchi tube depth to identify lake TSS below 7.0 mg/L showed an accuracy of 88%. In both laboratory and citizen scientist-based studies, all measured water quality variables were significantly higher during the wet season compared to the dry season. Climate factors were discovered to have a major impact on the likelihood of exceeding water quality restrictions in the next decades (2050), which could deteriorate lake conditions. Upscaling citizen science to more communities on the lake and other African Great Lakes would raise environmental awareness, inform management and mitigation activities, and aid long-term decision-making.
This paper details the physical and hardware design of a flexible open-source IoT (Internet of Things) platform for environmental sensing. The application is a remote water quality monitoring buoy that can be deployed in calm, shallow near-shore aquatic environments with fresh or brackish water. The system’s development has been informed by experience through conducting multiple actual water quality studies over a prolonged period. The system runs an Arduino Mega 2560 microcontroller using off-the-shelf Adafruit lux and temperature sensors. A light attenuation turbidity sensor is adapted and integrated into the design. A TinySine 3G GSM module transmits data to a server that is displayed via a ThingsBoard IoT dashboard. The system is stable over time, provides reliable remote sensor readings, has low energy consumption, and is powered by renewable energy (up-cycled batteries). The hardware aspires to be general-purpose so that future environmental monitoring applications can repurpose the electronics by adding new compatible sensors and modifying the physical design to match the requirements.
This study presents the ongoing activities of citizen science (CS) monitoring of lake water quality that was developed in the SIMILE project. After presenting the tools and the initiatives that were implemented to gather volunteer contributions, the CS data are analyzed: to understand the main factors driving the contribution provided by citizens, through a proximity analysis using hierarchical clustering; to evaluate their added value and complementary nature in view of the proposed integrated lake monitoring with respect to remote sensing (RS) water quality parameters maps and in situ high-frequency monitoring (HFM) data that were acquired in the framework of SIMILE project, through an intercomparison of temperature data; and to highlight water quality patterns in the lakes under study. It is then discussed how the project outcomes could directly or indirectly contribute to the measurement of specific Sustainable Development Goals (SDGs) indicators highlighting the potential impact of CS activities. The conclusions highlight the complementary nature of CS and the need of supporting activities for a continuous lake water quality monitoring. The possibility for the partner local authorities to maintain the CS monitoring establishing a Citizen Observatory (CO) and the opportunity to extend the SIMILE approach to neighboring lakes and beyond is also discussed.
Water quality monitoring networks in the global south often display inefficiencies because
monitoring strategies are frequently designed based on subjective professional judgments to define the temporal and spatial attributes of the networks, leading to poor cost–benefit relationships. The Lerma-Santiago Hydrological System (LSHS) in Mexico currently experiences severe environmental degradation caused by uncontrolled pollutant emissions from urban centers, agricultural, livestock, and industrial activities settled in the basin. While both the national and state authorities monitor this hydrological system, there has never been an effort to assess the monitoring efficiency of these two networks. The aim of the present study was to assess through multivariate statistical analyses the potential for coordination between these two interacting networks. For this purpose, two independent
large water quality datasets with temporal and spatial attributes measured by two different authorities (the federal and the state) were used to identify those sites where coordination should be rationalized and those parameters that should continue to be monitored. The case study herein presented highlights the duplication in efforts to monitor surface water quality in the Lerma-Santiago hydrologic system, which implies a lack of coordination between the authorities and shows that water quality monitoring networks have not been reassessed since they were first implemented. Furthermore, using the case study of the Lerma-Santiago in Mexico, we expanded on various deficiencies, such as
the use of different sampling frequencies and analytical methods by the authorities and inefficient communication among federal and state authorities. This study has revealed a large potential for coordinating two water quality monitoring networks (WQMN) in the Lerma-Santiago Hydrological System and a methodological approach that may be used to assess this potential. Coordination strategies for WQMNs can lead to significant cost reductions, extended network reach, and higher overall data quality in developing countries with limited financial resources and technical capabilities.
Global water security presents a complex problem for human societies and will become more acute as the impacts of climate change escalate. Water security connects the practical water and sanitation challenges of households to the dynamics of global hydroclimates and ecosystems in the Anthropocene. To ensure the successful deployment of attention and resources, it is necessary to identify the most pressing questions for water research. Here, we present the results of a scoping exercise conducted across the global water sector. More than 400 respondents submitted an excess of 4,000 potential questions. Drawing on expert analysis, we highlight 100 indicative research questions across six thematic domains: water and sanitation for human settlements; water and sanitation safety risk management; water security and scarcity; hydroclimate-ecosystem-Anthropocene dynamics; multi-level governance; and knowledge production. These questions offer an interdisciplinary and multi-scalar framework for guiding the nature and space of water research for the coming decades.
Water quality indices (WQIs) are used for the simple assessment and classification of the
water quality of surface water sources. However, considerable time, financial resources, and effort are required to measure the parameters used for their calculation. Prediction of WQIs through supervised machine learning is a useful and simple approach to reduce the cost of the analysis through the development of predictive models with a reduced number of water quality parameters. In this study, regression and classification machine-learning models were developed to estimate the ecosystem-specific WQI previously developed for the Santiago-Guadalajara River (SGR-WQI), which involves the measurement of 17 water quality parameters. The best subset selection method was employed to reduce the number of significant parameters required for the SGR-WQI prediction. The multiple linear regression model using 12 parameters displayed a residual square error (RSE) of
3.262, similar to that of the multiple linear regression model using 17 parameters (RSE = 3.255), which translates into significant savings for WQI estimation. Additionally, the generalized additive model not only displayed an adjusted R2 of 0.9992, which is the best fit of all the models evaluated, but also fitted the rating curves of each parameter developed for the original algorithm for the SGR-WQI calculation with great accuracy. Regarding the classification models, an overall proportion of 93% and 86% of data were correctly classified using the logistic regression model with 17 and 12 parameters, respectively, while the linear discriminant functions using 12 parameters correctly classified an overall
proportion of 84%. The models evaluated were found to be efficient in predicting the SGR-WQI with a reduced number of parameters as complementary tools to extend the current water quality monitoring program of the Santiago-Guadalajara River.
The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.
Progressively more community initiatives have been undertaken over last decades to monitor water quality. Biological data collected by volunteers has been used for biodiversity and water quality studies. Despite the many citizen science projects collecting and using macroinvertebrates, the number of scientific peer-reviewed publications that use this data, remains limited. In 2018, a citizen science project on biological water quality assessment was launched in the Netherlands. In this project, volunteers collect macroinvertebrates from a nearby waterbody, identify and count the number of specimens, and register the catch through a web portal to instantaneously receive a water quality score based on their data. Water quality monitoring in the Netherlands is traditionally the field of professionals working at water authorities. Here, we compare the data from the citizen science project with the data gathered by professionals. We evaluate information regarding type and distribution of sampled waterbodies and sampling period, and compare general patterns in both datasets with respect to collected animals and calculated water quality scores. The results show that volunteers and professionals seldomly sample the same waterbody, that there is some overlap in sampling period, and that volunteers more frequently sampled urban waters and smaller waterbodies. The citizen science project is thus yielding data about understudied waters and this spatial and temporal complementarity is useful. The character and thoroughness of the assessments by volunteers and professionals are likely to differentiate. Volunteers collected significantly lower numbers of animals per sample and fewer animals from soft sediments like worms and more mobile individuals from the open water column such as boatsmen and beetles. Due to the lack of simultaneous observations at various locations by volunteers and professionals, a direct comparison of water quality scores is impossible. However, the obtained patterns from both datasets show that the water quality scores between volunteers and professionals are dissimilar for the different water types. To bridge these differences, new tools and processes need to be further developed to increase the value of monitoring biological water quality by volunteers for professionals.
Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.
We present and evaluate an IoT-enabled sensing and actuating system for localizing illegal industrial harsh discharges of polluting wastewater in sewer networks. The special conditions of the sewer environment bring special challenges for the design of an IoT system and of its real-time algorithm for anomaly detection and localization in wastewater networks. The proposed design fulfills these requirements by using a new IoT architecture pattern, which we generalize and name Hop-by-hop Anomaly Detection and Actuation (HADA). The distributed anomaly detection and localization algorithm makes predictions over previous sensor measurements, while taking into account seasonality effects of wastewater and noise of the sensors. Based on simulations in a large network with three common illegal industrial wastewater pollutants, the advantages and limitations of the proposed wastewater anomaly localization system are discussed. The IoT system, including its anomaly detection and localization algorithm, was implemented using in a low-power microcontroller and tested in flowing wastewater with different harsh industrial waste.
Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.
In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.
Urban lakes play a major role in the socio-cultural and ecological sustainability of many cities, but are often under major development and pollution pressures. Urban decision makers are faced with a challenging task of identifying the causes of their decline and building plans for their conservation or rejuvenation. Powai Lake is a perfect example of an urban water body with historic, cultural, and ecological importance to the population of Metropolitan Mumbai, with local and regional authorities, including the Urban Development Department, Government of Maharashtra, working to identify methods for rejuvenating the Lake. In this context, characterization of pollution dynamics, hotspots, and extent is fundamental to the development of management plans and appropriate technologies for the remediation and rejuvenation of Powai Lake—the long-term goal of the present study. A two-year monitoring program at eight sampling locations on the Lake’s periphery, with the engagement of citizen scientists along with environmental researchers, revealed clear seasonal and spatial dynamics that allowed for the identification of pollution drivers and the development of a three-phase rejuvenation plan. The plan represents a novel and holistic approach that recognizes Powai Lake as a complex system with multiple drivers, and aims at ecological balance and sustainable delivery of ecosystem services.
In the present study, adopting of citizen science approach for monitoring water quality of a lake (Uzungöl, Turkey) was investigated. The study consisted of selection of sampling points and water quality parameters, training of volunteers, sampling and analysis by volunteers, and development of a mobile application for data collection and storage. In the scope of the study, four measurement points around the lake were selected and elementary school students were trained both theoretically and practically to collect water quality data. During the project (June-December, 2018), volunteers collected water samples and measured temperature, pH, nitrate, and phosphate with the given test kits. The mobile application was developed using open source code and used to collect and store the volunteer data. According to the volunteer data, temperature values were between 8.5 and 15.5 °C, pH values were between 2 and 8.5, nitrate values were between 0.5 and 2.5 mg/L, and phosphate values were between 0.5 and 5 mg/L in the lake. Most of the pH and temperature results were compatible with field measurements done by research group during field visits. The motivation of the volunteers and mobile application development were the achievements of the project. Low number of the data and its reliability were the main limitations. Therefore, this study showed that citizen science has both capabilities and constraints for collection of water quality data, however; outstanding potential of citizen science is obvious.
Urbanization and climate change are together exacerbating water scarcity—where water demand exceeds availability—for the world’s cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693–2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153–422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193–284, including 10–20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.
As smart devices are increasingly getting deployed in distinct scenarios it is important to examine how the various demands of these practical uses will affect the dynamics of protection. This study provides an outline of the connection among the various security threats, specifications, implementations, and network safety as well as an overview. In addition, a few of the device communication services are often overviewed, evaluating the protection mechanisms. The Internet of Things (IoT) has been a focal point in the previous few years. On analysis, here are several kinds of problems and difficulties with the tremendous ability of the IoT. For IoT technologies, applications, and networks, cybersecurity is one of the crucial challenges. This study discusses the work advancement of IoT to examine every main part of IoT, and finds how some safety problems and concerns have to be recognized and discusses them momentarily. To secure information privacy, professional conduct, honesty, encryption, intrusion detection, and capability to recognize as well as versatility, interoperability, and usability, reliable and usable IoT protection is needed to be brought into account. In terms of certain realities, new IoT approaches from the scientific, educational and industrial sectors are presented and addressed by analyzing a few of the current study in the IoT field Depending on the results of this report, it is important to develop and implement suitable IoT applications that can ensure integrity, security, and honesty in interconnected conditions.
Within the United Nations Sustainable Development 2030 agenda, sustainable growth in the marine and maritime sector needs sea water quality monitoring. This is a very demanding and expensive task which results in the sea being largely undersampled. MaDCrow is a research and development project supported by the European Regional Development Fund, that involves citizens as data collectors while aiming to improve public environmental awareness and participation in scientific research. Its goal is to create an innovative technological infrastructure for real-time acquisition, integration and access of data, thus generating knowledge on sea water quality and marine ecosystem of the Gulf of Trieste. Data acquisition is based on an autonomous and removable device, developed within the project, that can be deployed on any small size sailing boat, recreational vessel, or fishing boat. The device holds low-cost sensors to measure pH, temperature, dissolved oxygen and salinity and the hardware and software to acquire, georeference and transmit the environmental data without interfering with the activities of the boats. In this work we analyze the use, capabilities and advantages of low-cost sensors but also their limitations, comparing, with a special focus on pH, their performances with those of the traditional ones. Applying the paradigm in a highly anthropized area such as the Gulf of Trieste, which is characterized also by a very high spatial and temporal variability of environments, we point out that this new approach allows to monitor sea water quality and highlight local anomalies with a resolution and spatial and temporal coverage that was not achievable with previous procedures, but yet at very low costs. Once received, data are then processed and submitted to a mediation flow that contextualizes and disseminates them for public use on a website. The final products have been customized to reach stakeholders such as tourists, fishermen and policy makers. The availability of information understandable to everyone, while fostering environmental awareness, stimulates, at the same time, involvement and participation of citizen scientists in the initiative. In the future, while committing to enlarge the number of participants, we will extend the analysis also toward other types of sensors.
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.
A novel method is proposed for the development of ecosystem specific water quality indices in accordance with specific guidelines for the protection of aquatic life and with the capacity to demonstrate annual cycles, spatial and temporal variations, and trends in water quality. An ecosystem-specific water quality index for the Santiago-Guadalajara River (SGR-WQI) was developed using a method based entirely on multivariate statistical methods and historical data distributions. The SGR-WQI was developed using data from 13 water quality monitoring stations in the Santiago-Guadalajara River basin, collected monthly from 2009 to 2019 by the State Water Commission of Jalisco, México. The behaviour of 44 water quality parameters was statistically analyzed. It was found that 17 of the parameters best represented the water quality of the River. Parameter selection also considered the legal limits, established by the Government of Mexico, for the protection of aquatic life. To consider the protection of aquatic biota in the assessment of the index, a method was proposed to incorporate these limits in the development of rating curves providing the WQI with high sensitivity to parameter values that are outside the legal limits. Furthermore, a well-structured method based on multivariate statistical techniques was used to determine the parameter weights that best distinguish between sampling points and seasons, clearly reflecting temporal and spatial variability, as well as to demonstrate the annual cycle and trends in water quality. The methodology proposed can be used elsewhere to develop ecosystem specific water quality indices that evaluate surface water for protection of aquatic life in accordance with local limits/standards/guidelines and that adequately reflect spatial and temporal variability in agreement with the specific context.
Citizen science aims to mobilise the general public, motivated by curiosity, to collect scientific data and contribute to the advancement of scientific knowledge. In this article, we describe a citizen science network that has been developed to assess the water quality in a 100 km long tropical lake-estuarine system (Vembanad Lake), which directly or indirectly influences the livelihood of around 1.6 million people. Deterioration of water quality in the lake has resulted in frequent outbreaks of water-associated diseases, leading to morbidity and occasionally, to mortality. Water colour and clarity are easily measurable and can be used to study water quality. Continuous observations on relevant spatial and temporal scales can be used to generate maps of water colour and clarity for identifying areas that are turbid or eutrophic. A network of citizen scientists was established with the support of students from 16 colleges affiliated with three universities of Kerala (India) and research institutions, and stakeholders such as houseboat owners, non-government organisations (NGOs), regular commuters, inland fishermen, and others residing in the vicinity of Vembanad Lake and keen to contribute. Mini Secchi disks, with Forel-Ule colour scale stickers, were used to measure the colour and clarity of the water. A mobile application, named “TurbAqua,” was developed for easy transmission of data in near-real time. In-situ data from scientists were used to check the quality of a subset of the citizen observations. We highlight the major economic benefits from the citizen network, with stakeholders voluntarily monitoring water quality in the lake at low cost, and the increased potential for sustainable monitoring in the long term. The data can be used to validate satellite products of water quality and can provide scientific information on natural or anthropogenic events impacting the lake. Citizens provided with scientific tools can make their own judgement on the quality of water that they use, helping toward Sustainable Development Goal 6 of clean water. The study highlights potential for world-wide application of similar citizen-science initiatives, using simple tools for generating long-term time series data sets, which may also help monitor climate change.
Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Never-theless, due to the limited types of Internet of Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this paper, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network model and a multivariate polynomial regression model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The general regression neural network model can successfully distinguish the abnormal increase of parameters against normal situations. For the multivariate polynomial regression model of degree eight, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.
Public-private partnerships are crucial for advancing agricultural sustainability and tackling issues related to enhancing global food security. They help make technology accessible to farmers so they can access markets. As PPPs bring together participants from the public, private, and civil society, they are commonly touted as a means of boosting productivity and fostering growth in the agriculture and food sectors. The PPP can assist in implementing cutting-edge technological breakthroughs and promoting private sector participation to reduce risks that could otherwise be excessive. PPPs are commonly understood as having the potential to modernise the agriculture industry and offer numerous concessions to help farmers achieve sustainable agricultural growth. The objective of the present study is to understand how these new partnerships are expected to play significant roles in identifying answers to the most important agricultural problems that Indian Agriculture is confronting. The study further aims to understand the critical issues in PPPs in the agriculture sector in the Indian context by analyzing their interrelationships and prioritization. The study utilizes Grey DEMATEL to determine these contextual relationships for PPP issues. Grey systems theory is a methodology that incorporates improbability and vagueness into the analysis.
The Internet of Things (IoT) has become an essential part of everyday life, automating manual systems and providing comfort, security, and privacy. However, IoT systems are susceptible to faults due to hardware, firmware, or software failures, which can interfere with their proper functioning in real-life environments. To ensure fault-free automation of IoT systems, it is important to implement a proactive maintenance strategy that uses deep learning techniques to forecast failures in smart home applications by analyzing each device’s log of events and calculating its failure rate per attempt. This will help service providers and connected devices extend better serviceability of the system while assisting management in making timely decisions. Additionally, the system’s early prediction feature alerts consumers to take necessary actions when devices behave unexpectedly. The performance evaluation of the proposed predictive system using Bi-directional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) demonstrates its effectiveness in predicting upcoming failures in smart home applications. Bi-LSTM has a Mean Squared Error (MSE) of 36.99, Root Mean Squared Error (RMSE) of 6.082, and Mean Absolute Error (MAE) of 5.305, while GRU has an MSE of 26.081, RMSE of 5.106, and MAE of 4.282.
The Internet of Things (IoT) is a pervasive computing technology that provides solutions to critical sustainable smart city applications. Each sustainable application has its own set of requirements, including energy efficiency, Quality of Service (QoS), hardware, and software resources. Even though green IoT devices operate in a resource-constrained environment. Monitoring, recognizing, and responding to activities that entail continuous access to timely information in a partially or fully distributed ecosystem is a difficult task. To overcome the challenges of resource management in the IoT, we proposed an energy-efficient Dynamic Clustering Routing (DCR) protocol using a neuro-fuzzy technique for restricting the resources of IoT devices. The proposed protocol uses a dynamic self-organizing neural network to create dynamic clusters in a network. The test-bed analysis is for computing the real-time event detection and clustering sensor nodes using TinyOS. The simulation result shows that the proposed protocol achieved a significant gain over peer-competing well-known green communication routing protocols like Low-energy Adaptive Clustering Hierarchy (LEACH) and Low-energy Adaptive Clustering Hierarchy-Centralized (LEACH-C). The proposed model results show that using neuro-fuzzy logic is effective for sustainable IoT devices and green smart city applications in terms of resource management and dynamic clustering. The result analysis shows that the proposed protocol shows an average 35% significant gain on the First Node Dies (FND), Last Node Dies (LND), the number of packets sent to CH & BS, network convergence time, network overhead, and average packet delay to compare with the LEACH and LEACH-C.
In regions of Eastern Kentucky, access to potable water has been diminished due to pollution and ageing infrastructure. Current communications regarding contaminated water are often inaccessible and infrequent to appropriately address the issues in target communities. To explore possible improvements to the community’s communication infrastructure, the researchers explored what types of stories should be used to communicate about water quality risks, who should communicate the stories, and how stories should be communicated. Researchers conduct 24 interviews with community members to derive emergent themes. The researchers concluded the communication infrastructure should be enhanced to engage the public about water quality risks. Risk messaging should share water quality information through stories that are designed to be easily digested and frequently distributed using laypeople’s terms, visuals, graphs, and maps. These stories should be shared using an integrated communication infrastructure where key community storytellers work together to share risk information across platforms and channels.
This paper surveys the published work on how blockchain technology will impact accounting in general, but AI-enabled auditing specifically. The purpose is to investigate how blockchain technology can improve transparency and trust in accounting practice and how professionals can use blockchain data to improve decision-making, based on the qualities of immutability, append-only, shared, verified, and agreed-upon (i.e., consensus-driven) blockchain data. The multi-party validation of blockchain protocols adds real-time trusted data for the AI systems used by auditors to improve assurance and efficiency. This review summarizes four themes emerging from the literature focusing on how blockchain technology has changed record-keeping in accounting: event approach to accounting; real-time accounting; triple entry-accounting and continuous auditing. The research interprets the findings using agency theory and stakeholder theory to advance how using blockchain to mitigate information asymmetry and improve stakeholder collaborations is understood. The investigation also summarizes the challenges and clarifies organizations’ reasons to be cautious about adopting blockchain. Lastly, the study suggests that future researchers use this study in two ways that enrich blockchain literature: first, to apply the themes and answer the questions identified within this review to improve the business methods of practitioners and policymakers; and second, to encourage stakeholders such as practitioners, system designers/developers, and policymakers to collaborate in designing blockchain ecosystems that suit accounting and auditing as they transform digitally.
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains. Contributory citizen science is a method in which non-professional participants contribute to data collection in whole or in part to advance scientific research. This Primer outlines the use of citizen science in the environmental and ecological sciences, discussing participant engagement, data quality assurance and bias correction.
Achieving and maintaining suitable water quality is one of the important parameters to ensure health and well-being of the human as well as ecosystems. Among the various aquatic ecosystems, riverine ecosystems are more prone to pollution and therefore needs to be monitored frequently and on regular time intervals. In this context, real-time water quality monitoring system offers excellent opportunity to keep track of the water quality on a continuous basis; which not only helps to identify the affected location and pollution source, but also creates alert enabling the authorities to take immediate action. One such real-time water quality monitoring system was installed in the River Ganga (India), considering the fragility and significance of the Gangetic ecosystem. In this paper, we have presented the details of the real-time water quality monitoring system installed in River Ganga and results obtained through it for various parameters. The results have also been compared with the standard values. Additionally, based on this preliminary investigation, limitations and recommendations have also been presented to further enhance the utility of the system.
The Internet of Things (IoT) is a rapidly evolving technology that connects a huge number of devices for a wide range of applications such as smart cities, logistics, fleet management, etc. These applications demand a variety of requirements such as energy efficiency, cost, and battery life. The battery life of an IoT device is a significant issue, particularly for remote applications. This paper proposes an energy efficient algorithm for LoRa end-nodes for extended battery life. The efficacy of the proposed algorithm is demonstrated in a water quality monitoring system. A maximum energy saving of 62% is achieved compared to the conventional scheme. Additionally, a carbon footprint analysis is carried out, saving 8.6 kg of annual carbon emissions per node.
To address the issue of soft-sensing of effluent total phosphorus in wastewater treatment processes (WWTPs), a soft-sensing system based on an adaptive recursive fuzzy neural network with Gustafson-Kessel (GK) clustering and hierarchical adaptive second-order optimization algorithm (HAS) is proposed in this paper. In GK-ARFNN, first, the GK clustering algorithm was utilized to cluster the input-output dataset. Thus, the establishment of the initial fuzzy rule base and the determination of the parameter value of the fuzzy set membership function was realized. Then, the recursive layer was added into FNN to improve the dynamic mapping ability of the system. Finally, the HAS algorithm was developed based on the improved Levenberg-Marquardt (LM) optimization algorithm, and all the free parameters of the GK-ARFNN were adjusted online using HAS to improve the generalization capability and prediction accuracy of the soft-sensing system. In addition, the convergence of the proposed GK-ARFNN algorithm was also analyzed in this paper, which can ensure the effectiveness of the solutions to modelling issues for practical industrial processes. The simulation results demonstrate that the GK-ARFNN-based soft-sensing system introduced in this paper achieved satisfactory accuracy in the prediction of effluent total phosphorus in WWTPs. The source codes of GK-ARFNN and some competitors can be downloaded from https://github.com/hyitzhb/GK-ARFNN.
Recent technologies and innovations have encouraged users to adopt cloud-based environment. Network intrusion detection (NID) is an important method for network system administrators to detect various security holes. The performance of traditional NID methods can be affected when unknown or new attacks are detected. For instance, existing intrusion detection systems may have overfitting, low classification accuracy, and high false positive rate (FPR) when faced with significantly large volume and variety of network data. For that reason, this system has been agreed by many establishments to allure the users with its suitable features. Because of its design, it is exposed to malicious attacks. An Intrusion Detection System (IDS) is required to handle these issues which can detect such attacks accurately in a cloud environment. To analyze the IDS datasets some of the predominant choices are Deep learning and Machine learning (ML) algorithms. By adopting nature-inspired algorithms, the problems concerning the data quality and the usage of high-dimensional data can be managed. In this study the datasets KDD Cup 99 and NSL-KDD are used. The dataset is cleaned using the min-max normalization technique and it is processed using the 1-N encoding approach for achieving homogeneity. Dimensionality reduction is done using the Ant colony optimization (ACO) algorithm and further processing is done using the deep neural network (DNN). To minimize the energy consumption we have adopted the Dynamic Voltage and Frequency Scaling (DVFS) mechanism to the system. The main reason to set up this concept is to develop a balance between the energy consumption and the time of different modes of VMs or hosts. The proposed model is validated and compared with ACO and Principal component analysis (PCA)-based (Naïve Bayes) NB models, the experimental outcomes proved the superiority of the ACO-DNN model over the existing methods.
Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions.
Worldwide, scientists are increasingly collaborating with the general public. Citizen science methods are readily applicable to freshwater research, monitoring, and education. In addition to providing cost‐effective data on spatial and temporal scales that are otherwise unattainable, citizen science provides unique opportunities for engagement with local communities and stakeholders in resource management and decision‐making. However, these methods are not infallible. Citizen science projects require deliberate planning in order to collect high data quality and sustain meaningful community partnerships. Citizen science practitioners also have an ethical responsibility to ensure that projects are not putting the safety of participants at stake. We discuss here how citizen science is being applied in freshwater research, emerging challenges in project planning and implementation, as well as how citizen science is shaping public understanding, policy, and management of freshwaters.
This article is categorized under: Science of Water > Water Quality
Water and Life > Conservation, Management, and Awareness
Human Water > Water Governance
Water and Life > Methods
Citizen scientists-based water quality surveys are becoming popular because of their wide applications in environmental monitoring and public education. At present, many similar studies are reported on collecting samples for later laboratory analysis. For environmentally toxic analytes such as ammonium and nitrite, on-site detection is a promising choice. However, this approach is limited by the availability of suitable methods and instruments. Here, a simple on-site detection method for ammonium and nitrite is reported. The chemistry of this method is based on the classic Griess reaction and modified indophenol blue reaction. Digital image colorimetry is carried out using a smartphone with a custom-made WeChat mini-program or free built-in applications (APPs). Using a simple and low-cost analytical kit, the detection limit of 0.27 μmol/L and 0.84 μmol/L is achieved for nitrite and ammonium, respectively, which are comparable to those achieved with a benchtop spectrophotometer. Relative standard deviations (n = 7) for low and high concentrations of nitrite are 3.6% and 4.3% and for ammonium are 5.6% and 2.6%, respectively. Identical results with a relative error of less than 10% are obtained using different smartphones (n = 3), color extracting software (n = 6), and with multiple individual users (n = 5). These results show the robustness and applicability of the proposed method. The on-site application is carried out in an in-campus wastewater treatment plant and at a local river. A total of 40 samples are analyzed and the analytical results are compared with that obtained by a standard method and a spectrophotometer, followed by a paired t-test at a 95% confidence level. This proposed on-site analytical kit has the advantages of simplicity and portability and has the potential to be popular and useful for citizen science-based environmental monitoring.
Water quality monitoring programs are developed to meet goals including attaining regulatory compliance, evaluating long-term environmental changes, or quantifying the impact of an emergency event. Methods for developing these programs often fail to address multiple aspects of development (hazard identification, parameter selection, monitoring locations/frequency) simultaneously. We develop a framework for monitoring program development that is both versatile and systematic, the Hazard Based Water Quality Monitoring Planning framework, and apply it to the Quabbin watershed in Massachusetts, USA. We use a novel application of dataset deconstruction of long-term water quality datasets and the Seasonal Kendall test for trends to evaluate the effects of sampling frequency on long-term trend detection at several watershed sites. Results showed that when sampling frequency is decreased, ability to detect statistically significant trends often decreases. Absolute error in trend slopes between biweekly (twice monthly) and reduced sampling frequencies was relatively small for specific conductance and turbidity but was high for total coliform, likely due to interannual variation in rainfall and temperature We found that no one sampling reduction method resulted in a consistently lower absolute error compared to the “truth” (biweekly sampling), highlighting the importance of evaluating conditions that may affect water quality at sites in different parts of a watershed. We demonstrate the framework's usefulness, particularly for parameter and sampling frequency selection, using methods that can be readily applied to other watershed systems.
Recent technological advances and developments have evolved the application of the Internet of Things (IoT), low-cost sensors, and three-dimensional (3D) printing for near-real-time water quality monitoring; however, these technologies have not yet been widely implemented in field operations. In this study, a solar-powered 3D-printed IoT-based water quality monitoring system (WQMS) that measures turbidity and water level every 2 h was developed and utilized in a palm oil plantation on Carey Island, Malaysia, for two months (November 28, 2019-January 21, 2020). The WQMS consists of four modules: energy, time, monitoring, and communication. The electrical consumption values of the WQMS in the standby, operating, and data transmission modes were calculated to determine the optimal monitoring frequency. The monitored data were uploaded to Ambient (an open cloud platform) for visualization and anomaly detection. The turbidity and ultrasonic water level sensors were calibrated and validated, and high linearities (R 2 > 0.97) were obtained between the signal received and the actual measurements. The turbidity sensor provided an accurate measurement of turbidity within a range of 10-1000 FNU, whereas the optimum measurement range of the water level sensor was determined to be 2-400 cm. From the field study results, it can be observed that the contact-type sensor (for turbidity) requires monthly maintenance to prevent the deposition of mud/silt and biofouling problems, whereas the non-contact sensor (for water level) can consistently provide accurate measurements throughout a period of at least two months. The proposed WQMS implemented demonstrates the effective integration of IoT with 3D printing, microcomputers, and low-cost sensors, paving a new path for the development of cost-effective and reliable systems for water quality monitoring.
The active participation of citizens in scientific research, through citizen science, has been proven successful. However, knowledge on the potential of citizen science within formal chemistry learning, at the conceptual...
Public water systems across the U.S. are required to annually issue a water quality report (more formally, consumer confidence report or CCR) and make it publicly accessible. CCRs are ineffective at communicating risk and safety information to the customers for several reasons: they are hard to find and poorly advertised, present complex scientific data at a high reading level, and are written predominantly in English. In this paper, we analyze a representative sample of 268 CCRs to measure their accessibility along three dimensions: adherence to Web Content Accessibility Guidelines (WCAG) 2.0 standards, their Flesch Reading Ease score, and the availability of translation to non-English languages. Our analysis found that water utilities of all sizes, customer demographics, and geographic locations scored poorly on each measure of accessibility. However, accessibility scores were correlated with utility size, racial composition, and the presence of bilingual speakers. As one of the few mandated non-crisis communication to customers, CCRs present a meaningful opportunity for water systems to share information about water quality, public health, and community concerns. Accessible and easy-to-understand CCRs can achieve all that and build customer trust.