Quality Assessment and Monitoring of River Water Using IoT Infrastructure

Kumar, Manish; Singh, Tinku; Maurya, Manish Kumar; Shivhare, Anubhav; Raut, Ashwin; Singh, Pramod Kumar

doi:10.1109/jiot.2023.3238123

Cited by 31 publications

(21 citation statements)

References 29 publications

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“…The assessed water quality parameters are promptly displayed on a PC or mobile device in real time. The study extends our previous research on Quality Assessment and Monitoring of River Water Using IoT Infrastructure [9] by proposing a real-time water quality monitoring system.…”

Section: Water Quality Monitoring On Streaming Data 101mentioning

confidence: 52%

“…Machine learning, grounded in both statistical and computational principles, is showcased as a powerful tool for handling complex environmental datasets. The use of IoT devices can also be used for continuous water quality monitoring, as demonstrated in [9]. The study presents an IoT infrastructurebased river water quality monitoring and assessment system.…”

Section: Related Workmentioning

confidence: 99%

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Water Quality Monitoring on Streaming Data

Kumar,

Singh,

Kumar

et al. 2024

CJACSIT

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The increasing contamination of natural water bodies due to diverse human activities necessitates a comprehensive approach to monitoring water quality, especially considering its widespread use in daily life. This study addresses the escalating contamination of natural water bodies, emphasizing the need for a robust real-time water quality monitoring system. Focused on evaluating Triveni Sangam, Prayagraj, where the Ganga and Yamuna rivers converge, the study recognizes the crucial role of continuous monitoring in safeguarding precious water resources. To achieve this, a sophisticated framework has been proposed, leveraging a Spark server to simulate streaming data. This dynamic approach ensures uninterrupted and real-time assessment of water quality, crucial for the effective management of water resources. The system categorizes training data using the Water Quality Index (WQI) and employs Naive Bayes classification for real-time data, achieving an impressive accuracy of 82.21%. The results underscore the effectiveness of learning from streaming data, emphasizing its utility for monitoring water quality in real-time. This study contributes significantly to ongoing water resource management initiatives but also highlights the pivotal role of machine learning in addressing pressing environmental challenges.

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Section: Water Quality Monitoring On Streaming Data 101mentioning

confidence: 52%

Section: Related Workmentioning

confidence: 99%

Water Quality Monitoring on Streaming Data

Kumar,

Singh,

Kumar

et al. 2024

CJACSIT

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show abstract

“…"MERN Stack: A Comprehensive Guide for Modern Web Development" by Johnson et al [2] tells us how MERN stack is a comprehensive solution for building dynamic and scalable web applications. This research provides several case studies where the MERN stack was very suitable for building CMS as MongoDB is a NoSQL database that is more flexible to handle dynamic data, Express.js provides a robust set of features for web and mobile applications, React.js a JavaScript library for building user-friendly interface and Node.js allowing the server-side scripting.…”

Section: IImentioning

confidence: 99%

Develop a Canteen Circuit in a Real-World System

Singh,

Sharma,

Kaushik

et al. 2024

IJRASET

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In the current corporate and educational environment canteen plays a crucial role in feeding the nutritional needs of the employees and the students but looking forward to the traditional canteen management system lacks in various ways like long waiting queues, inefficiency, resource wastage, scalability issues and many more problems. To address this issue this research proposes Canteen-Circuit, a solution to the canteen management system that uses MERN stack and AI to provide a seamless connection for both canteen staff and customers. The Research will involve designing, developing, and evaluating the Canteen Circuit in a real-world system. Overall research not only streamlines daily operations but also provides a foundation for enhanced canteen experience for customers and administrators.

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“…The synthetic dataset consists of 100,000 observations of a four-dimensional random vector taken from a combination of six multivariate Gaussian distributions. The water monitoring [47] data set contains a stream of five sensors that continuously monitor the level of pollution in the river stream. A sample visualization of three dataset is shown in Figure 5.…”

Section: Table 2: Datasets Used For the Experimental Analysismentioning

confidence: 99%

KIHCDP: An Incremental Hierarchical Clustering Approach for IoT Data Using Dirichlet Process

Chowdhury,

Pal,

Raut

et al. 2024

IEEE Access

Self Cite

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Internet of Things (IoT) devices are constantly producing vast amounts of data, necessitating efficient storage and processing to extract useful information. However, the models used to extract relevant information from IoT data are often hindered by the lack of useful data and the ever-changing distribution of this data. This paper introduces an incremental data clustering technique on a continuous stream of data through a Dirichlet process-based approach that is adept at handling the formation of clusters in streaming data. The complete approach is twofold; firstly, it starts with an estimated distribution of data and allocates an incoming data point to the estimated data distribution. Secondly, it refines the estimated data distribution after the allocation of the current point and over the subsequent arrival of data points. The influx of data leads to greater challenges in determining clusters for incoming points and preserving the current clusters for improved decision making. In this context, our proposed approach deals with the increasing amount of data using a selective elimination technique on both existing and incoming data. To assess the performance of the proposed approach, benchmark experiments have been performed using benchmark datasets. The results of the experiments demonstrate that the proposed model has a gain ranging from 2% to 4% as compared to the existing state-of-the-art and recent adaptive clustering approaches in terms of clustering accuracy with incremental data addition and variable clustering parameters. The proposed method shows a high gain in terms of running time ranging from 2% to 20% as compared to the existing approaches depending on the data reduction parameter. Furthermore, research findings through this work indicate that it is possible to set a trade-off between accuracy and running time by adjusting the elimination parameter depending on the requirements of the considered application.

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Quality Assessment and Monitoring of River Water Using IoT Infrastructure

Cited by 31 publications

References 29 publications

Water Quality Monitoring on Streaming Data

Water Quality Monitoring on Streaming Data

Develop a Canteen Circuit in a Real-World System

KIHCDP: An Incremental Hierarchical Clustering Approach for IoT Data Using Dirichlet Process

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