The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
IoT Based Real-time River Water Quality Monitoring System
Open accessAbstract
Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.
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Cites
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A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty
Hossain, M. S., Ahmed, F., Fatema-Tuj-Johora, & Andersson, K. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0685-8 · Cited by 128
Self-citationReal-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data
Чэн, Б., Song, Y., Jiang, T., Chen, Z., Huang, B., & Xu, B. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15040573 · Cited by 101
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Cited by
IoT in smart cities: A survey of technologies, practices and challenges
Syed, A. S., Sierra-Sosa, D., Kumar, A., & Elmaghraby, A. (2021) · Smart Cities · doi:10.3390/smartcities4020024 · Cited by 527
Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)
Ubina, N. A., Lan, H.-Y., Cheng, S.-C., Chang, C.-C., Lin, S.-S., Zhang, K.-X., Lu, H.-Y., Cheng, C.-Y., & Hsieh, Y.-Z. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100285 · Cited by 82
Machine learning based soft sensor model for BOD estimation using intelligence at edge
Pattnaik, B. S., Pattanayak, A. S., Udgata, S. K., & Panda, A. K. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00259-9 · Cited by 61
Metadata
- Authors
- Chowdury, M. S. U., Emran, T. B., Ghosh, S., Pathak, A., Alam, M. M., Absar, N., Andersson, K., & Hossain, M. S.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 155
- Pages
- 161-168
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2972632198
- Cited by
- 319
- References
- 16
- FWCI
- 92.11
- Type
- conference paper
- Language
- en
- Open access
- diamond
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