A Bibliometric Analysis and Review of Resource Management in Internet of Water Things: The Use of Game Theory

Olatinwo, Segun O.; Joubert, Trudi-Heleen

doi:10.3390/w14101636

Cited by 9 publications

(5 citation statements)

References 97 publications

(143 reference statements)

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“…From a technical point of view, the issues identified affect the physical, chemical, and biological properties of water, thus making it unclean for several purposes related to drinking, production, irrigation, marine ecosystems, etc. [8]. Key organizations such as the WHO, United Nations, and United States Environmental Protection Agency (USEPA) have identified in their strategic report plans that monitoring water infrastructure including sources and supply systems is a priority.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Assessment Tool for On-Site Water Quality Monitoring

Olatinwo,

Joubert

2024

IEEE Sensors J.

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Reliable water quality monitoring requires on-site processing and assessment of water quality data in near real-time. This helps to promptly detect changes in water quality, prevent biodiversity loss, safeguard the health and wellbeing of communities, and mitigate agricultural problems. To this end, we proposed a Highway-Bidirectional Long Short-term Memory (Highway-BiLSTM)-based water quality classification tool for potential integration into an edge-enabled water quality monitoring system to facilitate on-site water quality classification. The performance of the proposed classifier was validated by comparing it with several baseline water quality classifiers. The proposed classifier outperformed the baseline water classifier in terms of accuracy, precision, sensitivity, F1-score, and confusion matrix. Specifically, the proposed water classifier surpassed the random forest (RF) classifier with 2% accuracy, precision, sensitivity, and F1-score. Moreover, the proposed classifier achieved an increase of 4% in accuracy, precision, sensitivity, and F1-score for classifying water quality compared with the Gradient Boosting classifier. Additionally, the proposed method has 4% increase in accuracy, sensitivity, F1-score, and 3% increase in precision compared to the support vector machine (SVM) water quality classifier. The proposed method outperformed the artificial neural network (ANN) classifier by 1% accuracy, precision, sensitivity, and F1-score. Finally, the proposed method demonstrated rare errors in accurately classifying complex water quality samples. These findings suggest that our proposed method could be used to effectively classify water quality to aid accurate decision making and environmental management.

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Section: Introductionmentioning

confidence: 99%

Water Quality Assessment Tool for On-Site Water Quality Monitoring

Olatinwo,

Joubert

2024

IEEE Sensors J.

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“…Water is an essential resource to humanity [1][2][3][4][5]. Water is life as the human body is made up of approximately 65% water [6].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, humans need to consume clean water for their survival [4,5,7]. Clean water also plays a critical role in improving people's health, well-being, and quality of life [1,2,5]. However, clean water is becoming a scarce commodity because of the negative impacts of several anthropogenic activities [1,5,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

Olatinwo,

Joubert

2023

Sensors

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Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system.

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“…The knowledge atlas 2 helps monitor hot‐spots of online public opinions by combing the literature. Theoretical analysis methods such as game theory 3 and case analysis 4 are primarily used to study hot spot communication mode. Single‐pass, 5 K‐means, 6 and other clustering algorithms are often used to improve the accuracy and precision of hot‐spot identification.…”

Section: Introductionmentioning

confidence: 99%

Relationship modeling and short‐term prediction analysis between public attention and teaching research

Zhao

Liu

et al. 2022

Engineering Reports

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Analyzing the relationship between Internet attention and teaching research can provide a reference for extracting and identifying research hot spots in the discipline. Currently, many kinds of literature obtain social hot‐spot information based on a single platform, such as the Baidu Index, China National Knowledge Infrastructure (CNKI), and Web of Science. Still, they ignore the common information reflected between the Baidu Index platform and the professional academic platform and the lack of relationship demonstration between them. Therefore, based on the CNKI database and Baidu Index platform, the most critical data statistical analysis service platform in the Internet era, this article proposes a relationship modeling and prediction framework (RMPF). First, RMPF analyzes the relationship between public attention and teaching research from the perspective of “high school information technology” by word frequency analysis and Pearson. Second, RMPF constructs the relationship model between them using leave‐one‐out cross‐validation. Third, RMPF predicts the development of subject teaching and research using the autoregressive integrated moving average model and multiple regressions. The results show that the correlation coefficient between public attention and teaching research is more than 0.65 from the perspective of “high school information technology,” showing a strong positive correlation. In recent years, the focus of high school information technology discipline tends to be integrating multimedia technology, cloud classroom, artificial intelligence, and other technologies and disciplines. In addition, discipline literature volume growth will slow in the next 2 years, with an average annual publication of 350 articles. The overall number of literature decreased by about 120 compared with 2020. Based on the proposed RMPF, this study clarifies the relationship between subject attention and teaching research through experimental demonstration and provides an implementation framework reference for researchers of relevant literature.

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A Bibliometric Analysis and Review of Resource Management in Internet of Water Things: The Use of Game Theory

Cited by 9 publications

References 97 publications

Water Quality Assessment Tool for On-Site Water Quality Monitoring

Water Quality Assessment Tool for On-Site Water Quality Monitoring

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

Relationship modeling and short‐term prediction analysis between public attention and teaching research

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