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Vehicle Identity Anonymity Framework with Accountability for VANET Environment

Journal of Internet technology 2024年第2期25卷 267-280页

作者： Lo, Nai-Wei Chuang, Chi-Ying Luo, Jia-Ning Yang, Chong-Long Department of Information Management National Taiwan University of Science and Technology Taiwan Department of Computer Science and Information Engineering National Defense University Taiwan

In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collision warning systems. Simultaneously, car manufacturers and technology companies are actively exploring technologies in the realm of autonomous driving. To facilitate such applications, vehicles are required to communicate with each other, exchanging vital information such as position, speed, and acceleration. However, this exchange of information poses a potential risk of drivers’ personal data being compromised. For safety purposes, vehicles must undergo appropriate authentication before engaging in communication with other vehicles. Ensuring this authentication process maintains the anonymity of the vehicles is crucial. Yet, striking a balance between protecting vehicle anonymity and enabling vehicle identification when necessary remains a challenging issue. This paper introduces a multi-tier Vehicular Ad-Hoc Network (VANET) framework designed to uphold the conditional anonymity and traceability of vehicles. The implementation of a group signature mechanism facilitates anonymous authentication, thereby enabling the realization of conditional anonymity and traceability. Moreover, comprehensive simulations and security analyses were conducted to validate the effectiveness of the proposed framework, demonstrating its efficiency while incorporating robust safety considerations. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Vehicular ad hoc networks

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal 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 Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia 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 Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

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Sensors and Materials 2024年第3期36卷 1003-1018页

作者： Lin, Cheng-Jian Lin, Pei-Jung Shih, Chi-Huang Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%;(2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle;and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Robotic arms

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Multisource Data Fusion Using MLP for Human Activity Recognition

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computers, Materials & Continua 2025年第2期82卷 2109-2136页

作者： Sujittra Sarakon Wansuree Massagram Kreangsak Tamee Department of Computer Science and Information Technology Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence for Innovation and Technology for Detection and Advanced Materials(ITDAM) Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand Center of Excellence in Nonlinear Analysis and Optimizations Faculty of ScienceNaresuan UniversityPhitsanulok65000Thailand

This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.

关键词： Multisource data fusion human activity recognition multi-layer perceptron(MLP) artificial intelligent

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Enhancing Drone Security Through Multi-Sensor Anomaly Detection and Machine Learning

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SN computer science 2024年第5期5卷 651页

作者： Alzahrani, Mohammed Y. Department of Information Technology Faculty of Computer Science & amp Information Technology AlBaha University AlBaha Saudi Arabia

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have determined numerous applications across industries, ranging from aerial surveillance to package shipping. As drones are used in vital operations, ensuring cyberattacks and anomalies protect them from attackers is now a big challenge. This research study presents a complete approach to enhancing drone safety by integrating multi-sensor anomaly detection and superior machine learning techniques. The proposed methodology capitalizes on the rich sensor suite embedded in present-day drones, encompassing GPS receivers, accelerometers, gyroscopes, cameras, communication modules, and more. Leveraging an array of sensors in drones, our technique detects abnormal drone behavior indicative of unauthorized access, GPS spoofing, communication jamming, and malicious activities. By extracting features from sensor records, we develop a robust anomaly detection framework using the “uav attack dataset” able to identify deviations from normal flight patterns, communication signals, and environmental interactions. Central to our methodology is the utilization of machine learning algorithms. These algorithms are skilled on labeled datasets containing numerous flight eventualities, each normal and hostile, together with the ones discovered inside the “uav attack dataset”. The obtained results are eventually evaluated using rigorous performance metrics to quantify their effectiveness in distinguishing genuine anomalies from benign variations. The findings of our study underscore the capacity of multi-sensor anomaly detection for drones. By harnessing the power of machine learning and sensor fusion, we exhibit the ability to hit upon attacks at an early level, mitigating capability harm and permitting rapid responses. This study contributes now not only to the field of drone safety but also to the broader panorama of self-sustaining systems protection, highlighting the importance of adaptive and proactive protection mech

关键词： Anomaly Detection Drone Security Machine Learning for Drones Multi-Sensor Anomaly Detection Sensor Fusion

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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Wireless Sensor Security Issues on Data Link Layer:A Survey

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computers, Materials & Continua 2023年第5期75卷 4065-4084页

作者： Muhammad Zulkifl Hasan Zurina Mohd Hanapi Muhammad Zunnurain Hussain Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangSelangor43400Malaysia

A computer network can be defined as many computing devices connected via a communication medium like the *** network development has proposed how humans and devices communicate *** networks have improved,facilitated,and made conventional forms of communication ***,it has also led to uptick in-network threats and *** 2022,the global market for information technology is expected to reach$170.4 ***,in contrast,95%of cyber security threats globally are caused by human *** networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental *** proposed literature review presents a wide range of information on Wireless Social Networks(WSNs)and Internet of Things(IoT)*** aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend *** second is to review the novel work in the domain and find its *** goal is to identify the area’s primary gray field or current research divide to enable others to address the ***,we concluded that *** Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)*** research divides our future research into solutions and newly developed techniques that can assist in completing the lacking *** this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

关键词： Wireless sensor networks(WSN) internet of things(IoT) industrial revolution 4.0(IR4.0) computer networks network security

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Empowering agricultural security with IoT and deep learning driven real-time alert systems

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International Journal of information technology (Singapore) 2024年第7期16卷 4627-4635页

作者： Singh, Priya Krishnamurthi, Rajalakshmi Department of Computer Science Jaypee Institute of Information Technology

In the realm of smart agriculture, our primary objective is to enhance security in the agriculture field by combining IoT and computer vision technologies. By leveraging these advanced tools, we strive to protect field crops from unauthorized access, aligning with the core principles of smart agriculture that prioritize the safeguarding of agricultural assets. Various governmental initiatives have been innovated to tackle the issue of ensuring food security. However, the majority of farmers face limitation due to insufficient knowledge and financial means, to effectively manage issues such as field monitoring and object detection. Hence, we aim to create an energy-efficient real time system that intelligently detects objects and sends alerts to farmers specifically designed for agricultural purposes. By integrating ultrasonic sensors, Raspberry Pi edge, and real-time trained object detectors like Embed-YOLOv3 and EmbedT-YOLOv3, we achieve superior accuracy, faster processing, and enhanced speed. This system efficiently identifies and categorizes objects, instantly notifying farmers through a real time android application. Identification of objects followed by immediate action can preserve yields, significantly reducing crop damage and strengthening the country’s economy. This proactive approach can alleviate distress among numerous farmers. Achieving an exceptional precision rate of 97%, recall rate of 96%, and accuracy of 95.86%, our system harnesses low-power IoT devices coupled with deep learning to deliver robust crop protection in the agricultural field. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Internet of things (IoT) Object detection Raspberry pi Ultrasonic sensors YOLOv3

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Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals 46

Deep Learning-based Open-set Person Identification using Rad...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Zelin Bouazizi, Mondher Ohtsuki, Tomoaki Keio University Faculty of Science and Technology Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798350371499

Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted vital signs. While current studies mainly focus on closeset conditions with consistent training and testing categories, real-world scenarios often involve open-set circumstances, in which there are more data categories in the testing data. The algorithm involves extracting heart pulse signals from Doppler radar echoes, training two Convolutional Neural Network (CNN)-based models using transfer learning, and utilizing a distribution model for calibration. By combining the models' outputs through a strategic decision-making process, we achieve superior person identification results. Experimental results on a public radar vital signs dataset demonstrate an identification accuracy of 99.61% in close-set conditions and 94.35% in openset conditions, surpassing existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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