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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Ur Rehman, Ateeq Arshad, Jehangir Sadiq, Muhammad Tariq Rehman, Abdul Ahmad, Munir Hasan, Mohammad Kamrul Al Hamadi, Hussam Faiz, Tauqeer Gc University Department of Electrical Engineering Lahore Pakistan Comsats University Islamabad Lahore Campus Department of Electrical and Computer Engineering Lahore54000 Pakistan School of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom School of Computer Science Ncbae Lahore54000 Pakistan Faculty of Information Science and Technology Selangor Bangi43600 Malaysia College of Engineering and It University of Dubai United Arab Emirates School of It Skyline University College Sharjah United Arab Emirates - Swinburne Australia

ISBN: (纸本)9781665461221

Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology. © 2022 IEEE.

关键词： Microcontrollers

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The Impacts of On-Demand Transit Service on Ridership and its Growth

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SSRN 2024年

作者： Tang, Yili Tasneem, Zaima Zhang, Fangni Department of Civil and Environmental Engineering University of Western Ontario Canada Department of Electrical and Computer Engineering University of Western Ontario Canada Faculty of Engineering and Applied Science University of Regina Canada Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong

On-demand transit service has shown itself to have great prospects in terms of addressing the issues of long-time waiting, connectivity as well as operation efficiency. From the passengers’ perspectives, the service has the potential to improve accessibility with reduced travel times and transfers, whereas transit agencies can benefit from the opportunity of improvement in ridership and operations. As such, this study analyzes the impacts of on-demand transit on ridership and its trend leveraging a combination of transit demand, land use and operation data. The case study is based on a multi-year pilot program of on-demand transit service in the City of Regina, Canada. The impacts of on-demand transit on ridership at stop level as well as the cross-year growth are assessed with difference-in-difference methods for both the overall transit network and different land use zones including residential, commercial and public service areas. Density and distance of various amenities within 500 meters of the transit stops are also considered such as food, school and health facilities. Results prove positive relationships between ridership and on-demand transit services on the entire transit network, while the benefits vary in different land use zones. Results also show increasing impacts of the service on ridership across years which contribute to the ridership growth. The developed method and findings can provide planning and policy guidance for both the existing service evaluation and long-term development for on-demand transit. © 2024, The Authors. All rights reserved.

关键词： Land use

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Zainab Mohammed Hussein Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Science Information System University of Technology Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100–500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19–24 %), energy consumption (32-36 % ), packet loss (16-190/0), and end-to-end latency (21-26 % ), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance.

关键词： Wireless communication Wireless sensor networks Energy consumption Packet loss Routing Throughput Routing protocols Energy efficiency Encryption Security

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Renewable Energy Efficiency of Unmanned Aerial Vehicles Operating with Wireless Sensor Networks and Mobile Ad Hoc Networks

Renewable Energy Efficiency of Unmanned Aerial Vehicles Oper...

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Smart Technologies for Power and Renewable Energy (SPECon), International Conference on

作者： Wakeel Ahmad Sarfaraz Ahmed Adeel Ahmad Shams Tabrez Siddiqui Mohammad Khamruddin Haneef Khan Deptartment of Electrical Engineering Technology College of Applied Industrial Tech Jazan University Jazan Saudi Arabia Dept. of CNET College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Science Faculty of Science Aligarh Muslim University Aligarh U.P India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350383041

ISBN: (纸本)9798350383058

The amalgamation of Unmanned Aerial Vehicles (UAVs) into Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) provides significant prospects to improve network capabilities. This study examines the energy renewable efficiency and performance of UAVs operating in WSNs and MANETs. The assessment considers a variety of elements that influence energy consumption, including the possibility of integrating renewable energy, energy management measures, and the impact on overall network performance. This seeks to provide insights into optimizing energy usage and improving the operating efficiency of UAVs in these dynamic network situations through extensive analysis and evaluation. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The paper explores existing protocols such as LEACH, AODV and OLSR highlighting their benefits and challenges for UAV’s. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability.

关键词： Measurement Wireless sensor networks Renewable energy sources Autonomous aerial vehicles Routing protocols Reliability Vehicle dynamics

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A Comprehensive Review on Big Data Challenges 2

A Comprehensive Review on Big Data Challenges

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bharany, Salil Taleb, Nasser Sadiq, M. Tariq Kanwal, Nayab Abdelhakim, Mohamed Ghazal, Taher M. Pradhan, Manas Rehman, Ateeq Ur Tech Amritsar India CET Department United Kingdom Canadian University Dubai Faculty of Management Dubai United Arab Emirates University of Brighton School of Architecture Engineering United Kingdom NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan University of Dubai College of Engineering and IT United Arab Emirates Skyline University College School of Information Technology Sharjah United Arab Emirates Department of Electrical Engineering GCU Lahore Pakistan

ISBN: (纸本)9798350335644

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data"refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data. © 2023 IEEE.

关键词： Big data

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Matko Glučina Nikola Žigulic Domagoj Frank Ivan Lorencin Dario Matika Department of Engineering Istrian University of Applied Sciences Pula Croatia Faculty of Engineering University of Rijeka Rijeka Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varazdin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996.

关键词： Technological innovation computer vision Weapons Urban areas Artificial neural networks Safety Security Intelligent systems Informatics

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Channel Estimation for Reconfigurable Intelligent Surface-aided 6G NOMA Systems using CNN-based Quantum LSTM Model

Channel Estimation for Reconfigurable Intelligent Surface-ai...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nhien Q. T. Thoong Adnan A. Cheema Saeed R. Khosravirad Octavia A. Dobre Trung Q. Duong Faculty of Engineering and Applied Science Memorial University St. John’s Canada School of Engineering Ulster University Belfast UK Nokia Bell Labs Murray Hill NJ USA School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast UK

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

With the rapid development of communication applications, the integration of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques has emerged as a promising approach to enhance connectivity and data transmission rate in future wireless networks. To successfully deploy RIS-NOMA aided 6G network, an accurate channel estimation is a crucial task. Quantum machine learning (QML) is a novel approach showing potential computational advantages in various problems of 6G wireless communications. However, its application, particularly in channel estimation, remains largely theoretical rather than adopted in practice. We propose a hybrid quantum-classical neural network model based on convolutional neural network (CNN) and quantum long short-term memory (QLSTM) for channel estimation in RIS-aided 6G NOMA system. Our results show that the proposed CNN-QLSTM model has a better channel prediction compared to its classical counterpart with regard to root mean square error (RMSE) and mean absolute error (MAE).

关键词： 6G mobile communication NOMA Computational modeling Channel estimation Machine learning Predictive models Reconfigurable intelligent surfaces Convolutional neural networks Integrated circuit modeling Long short term memory

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Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

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computer Modeling in engineering & sciences 2025年第5期143卷 2237-2259页

作者： Sahbi Boubaker Adel Mellit Nejib Ghazouani Walid Meskine Mohamed Benghanem Habib Kraiem Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia The International Centre for Theoretical Physics Trieste UniversityTrieste34151Italy Faculty of Science and Technology University of JjelJijel18000Algeria Department of Civil Engineering College of EngineeringNorthern Border University Arar1321Saudi Arabia SAMATWAIQ for Drones Company Prince Sultan Roadeddah23621Saudi Arabia Physics Department Faculty of ScienceIslamic University of MadinahMadinah4235Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientifc Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia

Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal *** cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand *** present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling *** using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric *** main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous ***,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and *** found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.

关键词： Microgrid electric vehicles charging station forecasting deep recurrent neural networks energy management system

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Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

Transformer-Based Deep Learning Detector for Dual-Mode Index...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Toan Gian Tien-Hoa Nguyen Trung Tan Nguyen Van-Cuong Pham Thien Van Luong School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi Vietnam Faculty of Radio-Electronics Le Quy Don Technical University Ha Noi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks.

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Flying Welding Path Planning Based on Improved Two-chromosome Genetic Algorithm

Flying Welding Path Planning Based on Improved Two-chromosom...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wenlong Zhang Tianjiang Zheng Kunpeng Yan Hongling Tian Hongyuan Lian Qiping Zhang Faculty of Mechanical Engineering and Mechanics Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Aiming to address the issues of moving speed and overall machining efficiency of a robot arm in path planning for laser flying welding, a method is proposed. This method involves using the non-central point of the trajectory to be welded as the welding position point and is further extended to the generalized traveling salesman problem. Through the analysis of the optimization model, the two-chromosome genetic algorithm is utilized to solve the path planning. Based on this, path intersection elimination and neighbor node replacement are carried out for every iteration in order to enhance the quality of the best solution. Finally, the path planning based on the general genetic algorithm for central welding position points, and the path planning based on the two-chromosome genetic algorithm and the improved two-chromosome genetic algorithm for non-central welding position points are simulated. The results show that the improved two-chromosome genetic algorithm can calculate the optimal solution of higher quality and a more reasonable machining path for non-central point welding positions.

关键词： Industrial electronics Welding Optimization models Machining Traveling salesman problems Manipulators Laser modes

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