Smart water consumption measurement system for houses using IoT and cloud computing

Abstract Presently, in several parts of the world, water consumption is not measured or visualized in real time, in addition, water leaks are not detected in time and with high precision, generating unnecessary waste of water. That is why this article presents the implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, in addition, a leak detection algorithm is proposed based on rules, historical context, and user location that manages to cover 10 possible water consumption scenarios between normal and anomalous consumption. The system allows data to be collected by a smart meter, which is preprocessed by a local server (Gateway) and sent to the Cloud from time to time to be analyzed by the leak detection algorithm and, simultaneously, be viewed on a web interface. The results show that the algorithm has 100% Accuracy, Recall, Precision, and F1 score to detect leaks, far better than other procedures, and a margin of error of 4.63% recorded by the amount of water consumed.

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Non-standard situation detection in smart water metering

Open Computer Science ◽

10.1515/comp-2020-0190 ◽

2020 ◽

Vol 11 (1) ◽

pp. 12-21

Author(s):

O. Kainz ◽

E. Karpiel ◽

R. Petija ◽

M. Michalko ◽

F. Jakab

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Real Time ◽

Water Consumption ◽

Detection Algorithm ◽

Smart Water ◽

Real Time Detection ◽

Nonstandard Situation ◽

Standard Situation ◽

Water Metering

AbstractIn this paper an algorithm for detection of nonstandard situations in smart water metering based on machine learning is designed. The main categories for nonstandard situation or anomaly detection and two common methods for anomaly detection are analyzed. The proposed solution needs to fit the requirements for correct, efficient and real-time detection of non-standard situations in actual water consumption with minimal required consumer intervention to its operation. Moreover, a proposal to extend the original hardware solution is described and implemented to accommodate the needs of the detection algorithm. The final implemented and tested solution evaluates anomalies in water consumption for a given time in specific day and month using machine learning with a semi-supervised approach.

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SMART WATER NETWORK MONITORING: A CASE STUDY AT UNIVERSITI TEKNOLOGI MALAYSIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w3-2021-3-2022 ◽

2022 ◽

Vol XLVI-4/W3-2021 ◽

pp. 3-7

Author(s):

N. A. A. Abdul Aziz ◽

T. A. Musa ◽

I. A. Musliman ◽

A. H. Omar ◽

W. A. Wan Aris

Keyword(s):

Real Time ◽

Water Flow ◽

Water Pressure ◽

Water Flow Rate ◽

Water Monitoring ◽

Geographical Information ◽

Smart Water ◽

Cloud Computing Service ◽

Water Measurement ◽

Water Uses

Abstract. Water uses need to be measured, which is critical for evaluating water stress. The Industry 4.0 via the Internet of Things (IoT) and usage of water measurement sensor can provide real-time information on the water flow rate and water pressure, that is crucial for water monitoring and analysis. There is a need for online smart water monitoring that gives out more efficient and sustainable water uses at Universiti Teknologi Malaysia (UTM) campus. A prototype of an online smart water monitoring for UTM, which was developed based on the integration of IoT and Geographical Information System (GIS), consist of four layers; (1) physical layer; (2) network layer; (3) processing layer and, (4) application layer. The findings show that when the water flow increases, the water pressure decreases. When there is no water flow, the lowest value is 52.214 Psi, and the highest value is 60.916 Psi. The latest technology integrating the IoT-GIS for smart water monitoring has shown a very efficient way of providing real-time water parameters information, cost and time effective, and allowing for continuous water consumption analysis via the cloud computing service.

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Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features

Sensors ◽

10.3390/s21020367 ◽

2021 ◽

Vol 21 (2) ◽

pp. 367

Author(s):

Thang Bui Quy ◽

Jong-Myon Kim

Keyword(s):

Embedded System ◽

Real Time ◽

Leak Detection ◽

Detection Algorithm ◽

Time Characteristic ◽

Gas Pipeline ◽

Occurrence Rate ◽

Hybrid Features ◽

The Real ◽

Ae Signals

This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.

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A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

Journal of Advanced Transportation ◽

10.1155/2020/8851485 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Feng You ◽

Yunbo Gong ◽

Haiqing Tu ◽

Jianzhong Liang ◽

Haiwei Wang

Keyword(s):

Real Time ◽

Traffic Safety ◽

Information Entropy ◽

Detection Algorithm ◽

Facial Motion ◽

Facial Information ◽

Video Frames ◽

Fatigue Driving ◽

Fatigue Detection ◽

Better Than

Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in information entropy. Particularly, this algorithm relies on the analysis of sufficient consecutive video frames. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, we construct a geometric area called Face Feature Triangle (FFT) based on the application of the Dlib toolkit as well as the landmarks and the coordinates of the facial regions; then we create a Face Feature Vector (FFV), which contains all the information of the area and centroid of each FFT. We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. Comparative experiments show that our algorithm performs better than the current ones on both accuracy and real-time performance. In simulated driving applications, the proposed algorithm detects the fatigue state at a speed of over 20 fps with an accuracy of 94.32%.

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An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

Remote Sensing ◽

10.3390/rs13122259 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2259

Author(s):

Ruicheng Zhang ◽

Chengfa Gao ◽

Qing Zhao ◽

Zihan Peng ◽

Rui Shang

Keyword(s):

Real Time ◽

Sampling Rate ◽

Threshold Value ◽

Deformation Monitoring ◽

Success Rates ◽

Multipath Mitigation ◽

Mode Decomposition ◽

Observation Noise ◽

Original Observation ◽

Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

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Real-time 2D–3D door detection and state classification on a low-power device

SN Applied Sciences ◽

10.1007/s42452-021-04588-3 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

João Gaspar Ramôa ◽

Vasco Lopes ◽

Luís A. Alexandre ◽

S. Mogo

Keyword(s):

Low Power ◽

Real Time ◽

Object Classification ◽

Semantic Segmentation ◽

Detection Algorithm ◽

Power Device ◽

Indoor Environments ◽

State Classification ◽

Segmentation Algorithms ◽

Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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