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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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A Wideband Rotary Antenna Based on Gap Wavegudie Technology for mmWave Applications 19

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Farahbakhsh, Ali Zarifi, Davood Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk 80-233 Poland Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (纸本)9788831299107

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems. © 2025 European Association on Antennas and Propagation.

关键词： beem-steering gap waveguide technology rotary antenna

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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CrazyKhoreia: A Robotic Perception System for MAV Path Planning From Digital Images

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SN computer Science 2025年第5期6卷 1-20页

作者： Restrepo-Garcia, Santiago Romero-Cano, Victor Faculty of Engineering Universidad Autónoma de Occidente Cll 25 115-85 Valle del Cauca Cali 760030 Colombia School of Computer Science and Informatics Cardiff University Wales Cardiff CF24 4 AG United Kingdom

Micro Air Vehicles (MAVs) can be used for a wide range of applications, such as drone-based light shows for cultural or marketing purposes, or as a replacement for fireworks. However, more widespread usage of this technology is precluded by the usability gap between choreography design and deployment on target drones. Seamless deployment of MAV choreographies requires the building of computational interfaces that can obtain drone trajectories from conventional media such as digital images. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to MAV choreography design. CrazyKhoreia is a robotic perception system that obtains a safe, traversable, and accurate waypoint matrix from a digital image. We validate our trajectory generation system through two distinct modes of operation: light painting, where an MAV flies through all waypoints, and multi-drone formation, in which multiple MAVs are arranged to emulate a given image. The utility of each mode is evaluated differently, using the full resolution CrazyKhoreia output as a reference for comparison. For the light painting mode, we logged the MAV’s pose at a frequency of 10 Hz and compared it with the reference for different levels of detail in the reproduced path. The Root Mean Squared Error (RMSE) ranges from 0.1287 to 0.3190 m. For multi-drone formation, where the swarm remains stationary, we computed the RMSE by comparing observed positions with the reference across multiple tests, resulting in a mean RMSE value of 0.1360 m. © The Author(s) 2025.

关键词： Boundary tracing computer vision Drone swarm MAV Path planning Robotic perception

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Cybersecurity of Sensors on Smart Vehicles: Review of Threats and Solutions 6

Cybersecurity of Sensors on Smart Vehicles: Review of Threat...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Putro, Prasetyo Adi Wibowo Amelia, Fetty Pidanic, Jan Suhartanto, Heru Rahardjo, Imam Arif Imandeka, Ejo Universitas Indonesia Faculty of Computer Science Depok Indonesia Cryptographic Hardware Engineering Politeknik Siber Dan Sandi Negara Bogor Indonesia University of Pardubice Faculty of Electrical Engineering and Informatics Pardubice Czech Republic Universitas Negeri Jakarta Department of Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9798350345162

The use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn't discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory. © 2023 IEEE.

关键词： cybersecurity Kitchenham sensor SLR smart vehicles solution threat

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WildGaussians: 3D Gaussian Splatting in the Wild 38

WildGaussians: 3D Gaussian Splatting in the Wild

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kulhanek, Jonas Peng, Songyou Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science ETH Zurich Switzerland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient Machine Learning for Prediction of Malicious URLs under Neutrosophic Uncertainty Framework

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Neutrosophic Sets and Systems 2025年 83卷 456-468页

作者： Eassa, Mohamed Abdelhafeez, Ahmed Metwaly, Ahmed A. Salama, Ahmed S. Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

With more than 5.44 billion users, the Internet is an essential component of everyday life, facilitating e-commerce, interaction, learning, and more. But with the proliferation of harmful Uniform Resource Locators (URLs), this widespread Internet access also raises questions about online security and privacy. Due to their significant advantages of lowering model variance, increasing predictive performance, raising prediction accuracy, and exhibiting strong generalization potential, traditional ensemble models have recently drawn interest. However, there is still work to be done on how to use it to combat rogue URLs. These URLs are dangerous to people and organizations because they frequently lurk behind static links in emails or web pages. Many malicious websites avoid detection despite blacklisting services because they are either newly created or not closely monitored. Hence, we use different machine learning (ML) models such as decision tree, AdaBoosting, Naïve Bayes, random forest, gradient boosting, and XGBoosting. Then these models are evaluated under the neutrosophic framework to deal with uncertainty. The WASPAS method is used to select the best ML model from different models. The results show that the random forest is the best ML model. © 2025, University of New Mexico. All rights reserved.

关键词： Learning systems

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