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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT communications

computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network 38

Human Activity Recognition in Logistics Using Wearable Senso...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on Information Technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical Conference on Circuits/systems, computers, and communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

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computers, Materials & Continua 2021年第12期69卷 2967-2981页

作者： Arun Kumar Sivabalan Ambigapathy Mehedi Masud Emad Sami Jaha Sumit Chakravarty Kanchan Sengar Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Center for System Design Chennai Institute of TechnologyIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA

The article introduces Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC),known as hybrid waveform(NOMAFBMC),as two of the most deserving contenders for fifth-generation(5G)*** spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid *** compare the spectral efficiency of Orthogonal Frequency Division Multiplexing(OFDM),FBMC,NOMA,and *** is seen that the hybrid waveform outperforms the existing *** to Average Power Ratio(PAPR)is regarded as a significant issue in multicarrier *** combination of Selective Mapping-Partial Transmit Sequence(SLM-PTS)is an effective way to minimize large peak power *** SLM,PTS,and SLM-PTS procedures are applied to the NOMA-FBMC *** hybrid structure is applied to the existing ***,the correlated factors like Bit Error Rate(BER)and Computational Overhead(CO)are studied and computed for these *** outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems.

关键词： 5G NOMA-FBMC SLM-PTS PAPR BER OFDM

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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A Review of Satellite Image Analysis Tools 4th

A Review of Satellite Image Analysis Tools

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

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IET communicationS 2022年第5期16卷 400-420页

作者： Elfatih, Nada M. Hasan, Mohammad Kamrul Kamal, Zeinab Gupta, Deepa Saeed, Rashid A. Ali, Elmustafa Sayed Hosain, Md. Sarwar Department of Electrical and Electronics Engineering Red Sea University Port Sudan Sudan Faculty of Information Science and Technology The National University of Malaysia (UKM) Bangi Selangor Malaysia Amity Institute of Information Technology Amity University Uttar Pradesh Delhi NCR India Department of computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh

The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

关键词： Internet of Things Vehicle to Everything artificial intelligence techniques resource allocation IoT technology IoV radio access networks vehicle resource management V2X 5G mobile communication Internet of Vehicles communication infrastructure artificial intelligence vehicular ad hoc networks 5G RAN networks 5G new radio high-speed vehicular environment 5G networks resource management telecommunication computing

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Real-time Animal Identification and Alert system using IoT and Deep Learning

Real-time Animal Identification and Alert System using IoT a...

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International Conference on Advances in Information Technology (ICAIT)

作者： Manujakshi B C Shashidhar T M Ravikiran H N Asha R Rajeev Bilagi Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Bengaluru Karnataka India Department of Computer Science and Engineering Acharya Institute of Technology Bengaluru Karnataka India Department of Electronics and Communication Engineering Adichunchanagiri University Nagamangala Mandya Karnataka India Department of Electronics and Communication Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The amount of traffic accidents that result in fatalities and injuries is one of the most urgent problems facing the world today. Many tragic incidents are caused by collisions between humans and animals. Worldwide, there are startlingly many automobile accidents—one every few seconds. Our primary objective is to decrease the probability of traffic accidents caused by animals by identifying them and informing cars of their presence. We suggest using IoT technology to create a detection and warning system that can significantly lower animal-related incidents. The purpose of this warning system is to alert cars on the roadway as soon as a detection event takes place by means of a signal. Our solution is based on Internet of Things technology, with the Raspberry Pi3 Model B acting as the main component for car warning and animal identification. Real-time photographs and videos of animal movements are captured by the Pi camera, which is equipped with a Raspberry Pi, through the use of sensors to identify impediments. After that, it alerts any vehicles and pedestrians using the woodland road. Additionally, it marks particular distances at night with lights. In the end, the document's primary goal is to safeguard numerous people from possible harm and to preserve animals from mishaps. In order to do this, the YOLO (You Only Look Once) algorithm was created. It processes approximately 45 frames per second and examines the complete picture in order to determine if objects are present or not.

关键词： Deep learning YOLO Machine learning algorithms Animals Roads Wildlife Forestry

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