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Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles

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Agronomy Journal 2023年第1期115卷 1-20页

作者： Selvaraj R. Kuthadi V.M. Baskar S. Dep. of CS & IS Botswana International Univ. of Science and Technology (BIUST) Palapye Botswana Dep. of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India

Farming and agricultural production account for a substantial part of the global economic system, and most people rely on them for their living. In this perspective, real-time agricultural field monitoring and smart irrigation using modern technologies are now important for effective farming in green homes, smart cities, and rural areas. Water is an essential resource to be conserved using the newest technology. The Internet of Things (IoT) and Industry 4.0 enable smart farming, including using Quadrotor unmanned aerial vehicles (Q-UAV) with computer vision. The IoT-based smart irrigation management systems with real-time sensors and Q-UAVs have contributed to the optimum use of water resources in precision farming. The research presented an intelligent irrigation and field surveillance system using atmospheric and soil data such as temperature, humidity, salinity, wind speed, as well as photographs of the field using UAVs. The parameters mentioned above are available on the smartphone of the farmers using IoT and are hosted without any delay in the Firebase console. In addition to this, a user can control the water pump on various fields via Firebase Cloud Message platform. The intelligence and smartness of the proposed system are implemented with a powerful and low-cost platform Raspberry Pi 4B system on chip computer with Industry 4.0 standard dedicated for IoT, real-time embedded protocol interfacing, and computer vision applications. © 2022 The Authors. Agronomy Journal © 2022 American Society of Agronomy.

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Fuzzy Logic-Based Visualization and Interpretation of NDVI Maps 14

Fuzzy Logic-Based Visualization and Interpretation of NDVI M...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Racoviteanu, Andrei Kazak, Artur Secrieru, Nicolae Ivanovici, Mihai Transilvania University of Brasov Electronics and Computers Dep. Romania Politehnica National University of Science and Technology Applied Electronics and Information Engineering Dep. Bucharest Romania Space Technology Center Technical University of Moldova Chisinau Moldova

ISBN: (纸本)9798331513139

Normalized Difference Vegetation Index (NDVI) is widely-used for the monitoring of agriculture crops. Its interpretation is based on thresholds and the colormaps used for the visualization of NDVI maps are discrete. We propose a fuzzy logic approach for both the visualization and the interpretation of the NDVI maps. The rationale is that the NDVI values of agricultural crops exhibit a certain spread, while the interpretation based on thresholds is prone to a certain degree of incertitude. We define several membership functions for modeling three sub intervals of the NDVI values in the [0, 1] range, useful for the interpretation of the vegetation status for agricultural crops. The three sub intervals are corresponding to three categories of vegetation status: dried-out vegetation, moderately healthy and strongly developed. For a Sentinel-2 time series, we compare the visualization using the crisp logic, the Sentinel-2 color palette, and the color palettes resulting from the proposed membership functions implementing the fuzzy logic. We show that our colormap has a smoother color gradient, which can facilitate a more visually-appealing and interpretable transition of colors. We also show an example of inference for the interpretation of the NDVI values for an operational usage of the proposed fuzzy logic approach. © 2024 IEEE.

关键词： Visualization

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning 2

Deep Feature Extraction Framework Based on DNN for Enhancing...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Gharaibeh, Hasan Aljaidi, Mohammad Nasayreh, Ahmad Al-Na'amneh, Qais Jaradat, Ameera S. Samara, Ghassan Emhamed Al Mamlook, Rabia Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Dep. of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya

ISBN: (纸本)9798350373363

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%. © 2023 IEEE.

关键词： Deep neural networks

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Genetic Optimization Techniques for Enhancing Web Attacks Classification in Machine Learning

Genetic Optimization Techniques for Enhancing Web Attacks Cl...

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2023 IEEE International Conference on dep.ndable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jaradat, Ameera S. Nasayreh, Ahmad Al-Na'amneh, Qais Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Yarmouk University Dep. of Computer Science Irbid Jordan Yarmouk University Dep. of Computer Science Irbid211633 Jordan Applied Science Private University Dep. of Cyber Security and Cloud Computing Amman Jordan Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya

ISBN: (纸本)9798350304602

Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work dep.oyed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively. © 2023 IEEE.

关键词： Genetic algorithms

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluating the Impact of Injected Mobility Data on Measuring Data Coverage in CrowdSensing Scenarios

Evaluating the Impact of Injected Mobility Data on Measuring...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Kocian, Alexander Girolami, Michele Capoccia, Stefano Foschini, Luca Chessa, Stefano University of Bologna Department of Computer Science and Engineering Bologna Italy ISTI-CNR Italian National Council of Research Pisa Italy University of Pisa Dep. of Computer Science Italy

ISBN: (纸本)9798350351255

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker's profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision. © 2024 IEEE.

关键词： Differential privacy

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications 59

Performance Analysis of Intelligent Reflecting Surfaces for ...

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2024 Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024

作者： Khan, Muhammad Farhan Raza, Ali Iqbal, Adeel Rashid, Adnan Jamshed, Muhammad Ali Pesch, Dirk School of Computer Science and Information Technology University College Cork Cork Ireland Comsats University Islamabad Dep. of Electrical and Computer Engineering Pakistan Yeungnam University Dep. of Info. & Com. Engineering Gyeongsan-si38541 Korea Republic of Politecnico di Bari Dep. of Electrical & Information Engineering Bari Italy James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350304053

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Packet Too Big Detection and its Integration into QUIC 16

Packet Too Big Detection and its Integration into QUIC

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Völker, Timo Tuxen, Michael FH Münster University of Applied Sciences Dep. of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798350333510

A communication over an Internet Protocol (IP) based network fails if an endpoint sends packets that are too big to reach their destination and if the sender is unable to detect that. The node on the path that drops these packets should respond with a Packet Too Big (PTB) message. However, multiple scenarios exist in which the sender will not receive a PTB message. Even if it does, it refrains from using the information in case it suspects that a potential attacker forged the message. In particular, we are not aware of any implementation of the secure transport protocol QUIC (e.g., used by HTTP/3) that processes PTB messages. In this paper, we present a novel parameterizable PTB detection algorithm for reliable transport protocols that does not dep.nd on PTB messages. We further describe how to integrate our algorithm into QUIC, present results from an evaluation using the algorithm within a QUIC simulation model and, based on these results, suggest concrete parameter values. © 2023 IEEE.

关键词： HTTP

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Studying Risks of Hybrid Reengineering on Air-Transportation Systems Management 'A Case Study on Jordanian Civil Aviation Regulatory Commission (CARC)' 24

Studying Risks of Hybrid Reengineering on Air-Transportation...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Refai Mohammed, N. Taani, M.K. Otoom, Ahmed Ali Samara, Ghassan Hanandeh, S.E. Khafajeh, Hayel Zarqa University Software Engineering Dep. Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Dep. Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Artificial Intelligence Dep. Zarqa Jordan

ISBN: (纸本)9798350384307

This study examines decision-making in software re-engineering within businesses, focusing on the challenges and considerations involved. Financial constraints, time, and staff efforts are critical factors in these decisions. The study identifies difficulties faced by top-level management with legacy systems, existing systems, underused systems, and packaged software due to user resistance and customer dissatisfaction. The primary goal of the study is to assess the importance of software quality factors, especially from the users' perspective, in making re-engineering decisions. It uses the ISO 9126 Model to analyze quality elements in the case study software, aiming to provide guidance for deciding whether to proceed with re-engineering. The study explores functionality, reliability, usability, efficiency, maintainability, and portability as factors influencing re-engineering decisions, based on the ISO 9126 model. To understand user interactions and program performance, the study used unstructured participant observation in the case study software, highlighting the challenges faced by top management due to user resistance. These findings were further confirmed through face-to-face interviews with software users and dep.rtment employees, offering insights into their perspectives on software quality elements. The results reveal that, despite some flaws, the system has valuable features and capabilities. Most employees express a desire to undergo re-engineering to enhance service quality, align with ISO 9126 standards, and leverage the expertise of competent staff for re-engineering activities. © 2023 IEEE.

关键词： customer dissatisfaction ISO 9126 model decision-making software quality factors software re-engineering User acceptance

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Enhanced Patch Antenna Characteristics Analysis and Implementation with DGS and SMS

Enhanced Patch Antenna Characteristics Analysis and Implemen...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Miligy, Ahmed F. Elfeky, Assem H. Amar, Ahmed S.I Abdel-Aleim M., M. Eid, Alla M. Abo Sree, Mohamed Fathy Air Defence College Alexandria Univerisity Electronics and Communication Dep. Alexandria Egypt Ahram Candian University Faculty of Engineering Department of Electronics and Communications Engineering Cairo Egypt Air Defence College Ain Shams University Electronics and Communication Dep. Cairo Egypt Arab Academy for Science Technology and Maritime Transport Department of Electronics and Communications Engineering Cairo Egypt

ISBN: (纸本)9798350351408

The development of Defected Ground Structures (DGS) and Slab microstrip Structures (SMS) techniques improved the properties of numerous microwave devices. While the DGS and SMS are advantageous in the design of microwave filters and microwave oscillators, they are also utilised in the design of microstrip antennas for various purposes for enhanced patch antenna characteristics, including reducing antenna size, suppressing harmonics, and improving matching. In this paper, the combination of the two techniques helped in enhancing patch antenna characteristics such as return loss, bandwidth, gain, and radiation efficiency. The proposed structures are analyzed, simulated, and implemented using a Roger substrate with ϵr = 3.4 and tan = 0.004 using HFSS. The proposed structure introduces improvement of the antenna parameters such as RL = -58 dB, BW = 290 MHz, G = 6 dB, and 90% radiation efficiency at 9 GHz for wireless applications in the x-band. © 2024 IEEE.

关键词： Microwave oscillators

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