ArticleSensors (Basel, Switzerland)2020
Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- LoRaWAN Transmissions in Salt Water for Superficial Marine Sensor Networking: Laboratory and Field Tests.Sensors (Basel, Switzerland) · 2023Article
- Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance.Sensors (Basel, Switzerland) · 2022Article
- Dataset on water quality monitoring from a wireless sensor network in a river in Kosovo.Data in brief · 2022Article
- A Comprehensive Review on Types, Methods and Different Regions Related to Water-Energy-Food Nexus.International journal of environmental research and public health · 2021Review
- Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow.Environmental science and pollution research international · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a "backward pollution source tracing" (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu
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Registered trials
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