UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Jing, Bai; Gui, Jinsong; Huang, Guosheng; Zhang, Shaobo; Liu, Anfeng

doi:10.1016/j.dcan.2023.02.001

Cited by 12 publications

(1 citation statement)

References 33 publications

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“…Bai et al [15] developed data-based models to reduce the number of water quality parameters of monitoring programs using data mining. This approach was a promising alternative that can reduce the frequency of analyses in the laboratory and increase the spatial and temporal coverage of water quality monitoring networks.…”

Section: Related Workmentioning

confidence: 99%

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

Chen

Mao

2023

Wireless Communications and Mobile Computing

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The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.

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Section: Related Workmentioning

confidence: 99%