Monitoring of Input and Output Water Quality in Treatment of Urban Waste Water Using IOT and Artificial Neural Network

Aggarwal, Sachin; Gulati, Rahul; Bhushan, Bharat

doi:10.1109/icicict46008.2019.8993244

Cited by 9 publications

(4 citation statements)

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“…Here, we highlight some work that, while not on anomaly detection, highlights the usefulness of graph-based representations of data and IoT devices. The work in [ 25 ] highlighted a useful use-case of IoT-based data: water monitoring using low-cost multi-sensor IoT devices. The work in [ 26 ] expanded on the LMST algorithm [ 27 ] for determining the topology of an IoT network at the node level and finding the minimum power required to do so.…”

Section: Related Workmentioning

confidence: 99%

A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

DeMedeiros

Hendawi

Álvarez

2023

Sensors

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Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our person, and in our vehicles, and the transition to smart infrastructure and the Industrial IoT (IIoT), anomaly detection in these devices is critical. This paper is a survey of anomaly detection in sensor networks/the IoT. This paper defines what an anomaly is and surveys multiple sources based on those definitions. The goal of this survey was to highlight how anomaly detection is being performed on the Internet of Things and sensor networks, identify anomaly detection approaches, and outlines gaps in the research in this domain.

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Section: Related Workmentioning

confidence: 99%

A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

DeMedeiros

Hendawi

Álvarez

2023

Sensors

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“…WWTP monitoring systems using the Internet of Things (IoT) technology were proposed in [20][21][22][23]. A minimal model of IoT-based monitoring system was proposed in [20], where only three critical sensors, i.e., the temperature, turbidity, and pH sensors, were used to monitor the operational status of WWTP.…”

Section: Introductionmentioning

confidence: 99%

“…A low-cost sensor and IoT system to monitor the Electronic Conductivity, pH, Dissolved Oxygen, Color, and Turbidity of the wastewater was proposed in [22]. A Thingspeak [23] was adopted as the IoT cloud server for displaying and analyzing the data.…”

Section: Introductionmentioning

confidence: 99%

“…Second, compared to the existing real-time monitoring in phytoremediation [13,14], our approach offers a simple hardware configuration and adopts a recent IoT technology. Third, compared to the existing WWTP monitoring systems [15][16][17][18][19][20][21][22][23][24][25][26][27][28], our approach offers an efficient tool to monitor and examine the phytoremediation models by integrating the IoT platform and the mathematical model, which is implemented on the embedded hardware.…”

Section: Introductionmentioning

confidence: 99%

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IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

Hendriarianti¹,

Soetedjo²

2021

IJIES

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This paper presents the Internet of things (IoT) technology for real-time monitoring of wastewater phytoremediation. Phytoremediation is a technique to remove pollutants from the wastewater using the plants. A conventional method to monitor the phytoremediation performance is by taking the samples of the contaminants from a site and measuring them at the laboratory. This method needs many works for data preparation and analysis. A recent development on the IoT technology may eliminate such tasks by a real-time monitoring system. In the proposed realtime monitoring system, several phytoremediation models are implemented on embedded hardware and connected to the Thingspeak IoT platform. The proposed system aims to provide a real-time monitoring system to better model the phytoremediation by examining the monitoring data time interval and fitting techniques. From the experiments, the proposed monitoring system achieves a data transfer reliability of 81.4% when the period of the data transmission is one minute, which is suitable for one-day interval real-time monitoring system. The proposed monitoring system can build a phytoremediation model using a polynomial fitting with a higher fit than the existing methods using an exponential fitting. Further, the proposed method promises a better solution in terms of the best model, the low cost, and the acceptable accuracy.

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