Camera sensor-based contamination detection for water environment monitoring

Wang, Yong; Zhang, Xufan; Cheng, Zhuo; Wang, Dianhong

doi:10.1007/s11356-018-3645-z

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…To evaluate the quality of water in real-time, electrochemical sensors can monitor changes in water parameters that become affected by chemical and biological pollutants, such as turbidity, free/total chlorine, oxidation-reduction potential, electrical conductivity, pH, nitrates level and temperature [ 223 , 224 , 225 ]. Furthermore, further approaches have proposed the detection of floating debris in contaminated water by means of aquatic sensors embedding a CMOS camera [ 226 ].…”

Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

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The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

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Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Sensor nodes may use for controlling and monitoring purpose but it required large computational power and maximum energy so it will be competent to operate image processing operations. Vision based sensor nodes for aquatic medium, prototype for low cost help to provide a reliable solution for continuously monitoring of water environment [9].…”

Section: Related Workmentioning

confidence: 99%

Water Pollution Management and detection techniques: a Review

Ansari¹

2021

ijcci

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Now a day’s water pollution became a national and global issue not only in India but over the whole world. Like India other countries are also facing the same problem of water pollution due to agricultural waste, industrial waste, sewage waste and so on. In this paper it has been explained clearly with the help of considerable number of references. It gives the information about the pollutants which pollutes the water. Sensors have a ability to Control and monitor the quality of water as well as able to detect the contaminants added due to human generating activities. Sensors can be used for the selection and identification of the techniques which will be suitable for performing the given task that is identification of pollutants present in water. Various pollutants found in water are pesticides, harmful chemicals, heavy metals, nutrients, etc. We will also going to discuss about the different techniques used for water pollution detections. Tethered.

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“…Yong Wang et al [7]proposed the following model whose main body, which is the carrier, is a buoy box made up of waterproof foam board.This contains the sensing platform. Customized node components: Raspberry Pi 3 which includes a 64-bit ARMv8 processor with a maximum speed of 1.2 GHz.…”

Section: Fig 1prototype Selected [7]mentioning

confidence: 99%

IoT-Based Aquatic Pollution Monitoring using Convolutional Neural Networks

Daji¹

2019

IJRASET

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The continuously decreasing quality of drinking water is of great concern across the globe. Due to the incessant mixing of unprocessed industrial and domestic wastes, the contamination rate of water is rapidly increasing.The contaminations are of physical, organic, and chemical nature. Owing to this rise in pollution, the idea proposed is to build a model equipped with a visual device such as a camera, and other sensors, that by means of floating on water, can constantly monitorthe water body. Using techniques of computer vision, floating or other debris is detected on the surface of water. The use of sensors can help to keep in check the physical as well as the chemical parameters of water. Conditions like temperature, pH, turbidity, conductivity etcetera can be used either directly or indirectly to judge the overall quality of water.The optical device captures real-time images and a debris detection algorithm using Convolutional Neural Network (CNN) sends vital measurements to the user. The processed dataafter converting intoa user-understandable format can be used to get a general idea of whether the water is safe for consumption, that is, to determine whether it is potable or could be made potable by some treatment. Secondly, it could be used to monitor the balance in marine ecosystems.

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Camera sensor-based contamination detection for water environment monitoring

Cited by 8 publications

References 28 publications

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Water Pollution Management and detection techniques: a Review

IoT-Based Aquatic Pollution Monitoring using Convolutional Neural Networks

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