IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine Learning Article Swipe
B. Lalithadevi
,
Akash Yadav
,
Aditya S. Pandey
,
Mahesh Adhikari
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.35940/ijitee.a4316.119119
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.35940/ijitee.a4316.119119
The main source of water in the Indian Subcontinent is Groundwater. It is also the most rapidly depleting resource due to various reasons such as rampant unchecked irrigation and exploitation of groundwater by industries and other organizations. The current system is limited by short communication range, high power consumption and the system monitors only the water level and the report is available only to the consumer i.e. it is a single-user system. Due to the unavailability of a centralized system to monitor and prevent overuse of water resources, sudden water crises have become a major issue in India. This project aims at implementing an IoT (Internet of things) based water monitoring system that monitors the water level and the quality of groundwater and updates real-time data to the database. This system is designed to monitor the groundwater level of an entire village or a town. It updates the people and the concerned government authorities in case of any decrease in water level and water quality below the threshold value, and also monitors the water consumption during a period and predicts exhaustion time. This system predicts the availability of water in the future based on current demand and usage and the recharge rate using machine learning algorithms. The data collected and the analysis of the data is made available in a Public Cloud. The modules are based on Raspberry Pi Zero, sensor nodes and LoRa (Long Range) Module or Wi-Fi module according to the network requirement for connectivity
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.35940/ijitee.a4316.119119
- https://doi.org/10.35940/ijitee.a4316.119119
- OA Status
- diamond
- Cited By
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4243866788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4243866788Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.35940/ijitee.a4316.119119Digital Object Identifier
- Title
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IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-11-19Full publication date if available
- Authors
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B. Lalithadevi, Akash Yadav, Aditya S. Pandey, Mahesh AdhikariList of authors in order
- Landing page
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https://doi.org/10.35940/ijitee.a4316.119119Publisher landing page
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https://doi.org/10.35940/ijitee.a4316.119119Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.35940/ijitee.a4316.119119Direct OA link when available
- Concepts
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Cloud computing, Unavailability, Groundwater recharge, Groundwater, Computer science, Resource (disambiguation), Server, Environmental science, Computer network, Aquifer, Engineering, Operating system, Reliability engineering, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 6, 2023: 4, 2022: 3, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.real-time | 124 |
| abstract_inverted_index.threshold | 167 |
| abstract_inverted_index.unchecked | 26 |
| abstract_inverted_index.exhaustion | 180 |
| abstract_inverted_index.government | 152 |
| abstract_inverted_index.industries | 33 |
| abstract_inverted_index.irrigation | 27 |
| abstract_inverted_index.monitoring | 110 |
| abstract_inverted_index.resources, | 87 |
| abstract_inverted_index.algorithms. | 205 |
| abstract_inverted_index.authorities | 153 |
| abstract_inverted_index.centralized | 78 |
| abstract_inverted_index.consumption | 48, 174 |
| abstract_inverted_index.groundwater | 31, 121, 136 |
| abstract_inverted_index.requirement | 244 |
| abstract_inverted_index.single-user | 70 |
| abstract_inverted_index.Groundwater. | 10 |
| abstract_inverted_index.Subcontinent | 8 |
| abstract_inverted_index.availability | 186 |
| abstract_inverted_index.connectivity | 246 |
| abstract_inverted_index.exploitation | 29 |
| abstract_inverted_index.implementing | 102 |
| abstract_inverted_index.communication | 44 |
| abstract_inverted_index.organizations. | 36 |
| abstract_inverted_index.unavailability | 75 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.68111996 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |