Use of Big Data Analytics in WASH Sector

Adhikari, Binod Kumar; Zuo, Wan Li; Maharjan, Ramesh; Yadav, R. N. S.

doi:10.1109/iccons.2018.8662931

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Datadriven decision-making, scientific discovery, and process optimization of WDN can be achieved with the help of Big data analytics [239]. Demand forecasting, water leak predictions, reservoir capacity prediction, reservoir water level predictions, queries, reports, visualization and interpretation, modeling and prediction, service improvement, and auditing and evaluation [240], [241] are the other outcomes of the data analytics for WDN system. The following are the major outcomes of WDN integrated with Big data analytics:…”

Section: ) Iot Cloud Planementioning

confidence: 99%

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

et al. 2022

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Water distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of the water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technologies (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks -IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that to build a robust water distribution network, these components need to be designed and implemented effectively. INDEX TERMS Internet of things, IoT communication technologies, IoT services, water distribution networkThis article has been accepted for publication in IEEE Access.

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Section: ) Iot Cloud Planementioning

confidence: 99%

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

et al. 2022

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“…Among all of these characteristics, data size is the most distinctive feature of big data analytics compared to the other features [18]. The use of big data analytical tools and techniques [19] is very useful for identifying sensitive data, because they use parallel and distributed computing to process voluminous data [20]. To prevent unexpected event that may occur in future, security agencies must be one step ahead of terrorists [21].…”

Section: Introductionmentioning

confidence: 99%

Detection of Sensitive Data to Counter Global Terrorism

et al. 2019

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Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale.

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Social Accountability of Local Governments in Smart Cities: A Multiple Case Study

Loia

Maione

2022

Managing Smart Cities

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Use of Big Data Analytics in WASH Sector

Cited by 5 publications

References 6 publications

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

IoT-Enabled Water Distribution Systems—A Comparative Technological Review

Detection of Sensitive Data to Counter Global Terrorism

Social Accountability of Local Governments in Smart Cities: A Multiple Case Study

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