Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System

Habib, Sami J.; Alyahya, Saleh; Islam, Muhammad; Alnajim, Abdullah M.; Alabdulatif, Abdulatif; Alabdulatif, Abdullah

doi:10.3390/electronics12010028

Cited by 15 publications

(11 citation statements)

References 25 publications

(26 reference statements)

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“…The proposed architecture outperformed the recently spotted techniques by applying challenging anomaly datasets. We have used ShanghaiTech dataset and compared the propsoed model performance with various methods such as predictions of normal frames based on anomaly detection techniques with unsupervised learning [ 9 , 17 ], feature patterns based on unsupervised learning [ 60 , 61 ], and skeleton patterns based on unsupervised learning [ 62 , 63 ]. As a result, unsupervised techniques achieved lower siperfromance which was compared with supervised ones, since abnormal videos aren’t given in the training data, thus, the performance of these methods are lower than supervised techniques.…”

Section: Resultsmentioning

confidence: 99%

An IoT Enable Anomaly Detection System for Smart City Surveillance

Islam¹,

Dukyil²,

Alyahya³

et al. 2023

Sensors

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Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.

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Section: Resultsmentioning

confidence: 99%

An IoT Enable Anomaly Detection System for Smart City Surveillance

Islam¹,

Dukyil²,

Alyahya³

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…At this point in time, the configuration of the microcontroller is possible only from other clients, such as Android, iOS, or Windows client. The architecture of the system can be considered a generic one and is similar to what other researchers are building in their IoT systems [12,13]. The backend is written in Python, and it is responsible for all the communication between the main board, sensors, other microcontrollers, and client applications.…”

Section: Custom Smart-home Software Overviewmentioning

confidence: 99%

“…At this point in time, the configuration of the microcontroller is possible only from other clients, such as Android, iOS, or Windows client. The architecture of the system can be considered a generic one and is similar to what other researchers are building in their IoT systems [12,13]. On the left side of the diagram, in the green boxes, we have the frontend, which consists of a web interface launched directly when the SBC starts.…”

Section: Custom Smart-home Software Overviewmentioning

confidence: 99%

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Software Testing Techniques for Improving the Quality of Smart-Home IoT Systems

Vadan

Miclea

2023

Electronics

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Software is present in any modern device and is one of the most important components of a new product. IoT systems for smart homes have become more popular in recent years, and testing these systems using advanced methods is very important because it should improve software quality from the beginning of development, resulting in a faster product development overall and a better user experience for the client. In this paper, we describe methods of how to build fast quality assurance software for automation testing in comparison with current trends. Those methods are applicable to teams that are using custom test automation frameworks and working in big projects. The methods have already been applied with success in testing infotainment systems in the automotive industry and our custom-made smart-home IoT system. We will present the system and testing techniques used for testing web interfaces, mobile applications, cross-platform mobile applications, and backend using a new design pattern, called ‘Locate, Execute, Expect’. We compare this new design pattern against Page Object Model and will guide you on how to integrate it in an existing project or how to use it with Gherkin. In conclusion, we will see the main advantages of using this technique and how much faster it is in a real-life scenario, we will learn how it can replace Gherkin, and we will also see the main disadvantages.

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“…These techniques are broadly categorized into statistical (ST), artificial intelligence (AI), and hybrid methods (HM) [ 11 ]. In ST-based methods, several algorithms are developed, including auto-regressive [ 12 ], Bayesian [ 13 ], Kalman [ 14 ], grey models [ 15 , 16 ], and the Markov chain model [ 17 ]. Additionally, MaatAllah et al [ 18 ] and Reikard et al [ 19 ] developed ST-based models for renewable power prediction.…”

Section: Introductionmentioning

confidence: 99%

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

Alharkan

Habib

Islam³

2023

Sensors

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The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods.

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Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System

Cited by 15 publications

References 25 publications

An IoT Enable Anomaly Detection System for Smart City Surveillance

An IoT Enable Anomaly Detection System for Smart City Surveillance

Software Testing Techniques for Improving the Quality of Smart-Home IoT Systems

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

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