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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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A novel transformer-based graph generation model for vectorized road design

A novel transformer-based graph generation model for vectori...

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作者： Zhou, Peichi Li, Chen Zhang, Jian Wang, Changbo Qin, Hong Liu, Long School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science StonyBrook University Stony BrookNY United States

Road network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor-intensive, tedious and time-consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state-of-the-art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly-proposed framework for road design. © 2024 John Wiley & Sons Ltd.

关键词： Roads and streets

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MushroomDetect: An Investigation into Local Mushroom Classification in Bangladesh Using Transfer Learning 15

MushroomDetect: An Investigation into Local Mushroom Classif...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Adhikari, Bornita Hossain, Md. Mikail Akter, Sumaia Ahamed, Md. Sazzadur Daffodil International University Department. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Agricultural University Department. of Agricultural Chemistry Mmensingh Bangladesh Daffodil International University Department. of software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

When it comes to achieving food security, one of the main obstacles is ensuring that the diet includes high-quality foods. The development of scientific technologies has made manual local identification completely impractical because it takes so long and is so imprecise. To address this problem Using a machine vision-based approach to swiftly identify the most popular and farmed mushrooms from photos raises the accuracy level to our desired level. Pictures of our chosen species were carefully clicked to compile the dataset. We carefully selected new circumstances for seven varieties of selected mushrooms, including 'Ear mushroom,' 'Golden Oyster,' 'Milky Mushroom,' 'Oyster mushroom(Po2),' 'Oyster mushroom(Po10),' 'Pink Oyster,' and 'Reishi Mushroom,' to generate a bespoke dataset. We collected the 500 basic photographs and divided them into 7 groups. To get a position where training a dataset will be simple, we sought to increase the amount of raw dataset with augmentation. InceptionV3, InceptionResNetV2, and DenseNet201 were the transfer learning models employed in the initial stage. After putting DenseNet201 into practice, we received the intended outcome. All of these models provide us with a respectable level of accuracy, but after training DenseNet201, we were able to reach an accuracy of 96.72%. Accidental mushroom identification may also be possible in this study. © 2024 IEEE.

关键词： Shellfish

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Automated computer Vision-based Detection of Solar Panel Defects Using a Thermal Camera Mobile Application 10

Automated Computer Vision-based Detection of Solar Panel Def...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Hajjdiab, Hassan Mohamed, Gasm ELbary Alzaabi, Salim Alhalabi, Marah Gad, Abdalla Yaghi, Maha Alkhedher, Mohammad Ghazal, Mohammed Concordia University Department of Computer Science and Software Engineering Montreal Canada Abu Dhabi University College of Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350316353

Solar energy production has risen significantly globally in recent years. Solar Panels and Photovoltaic PV modules are the key components of solar energy production. Therefore, ensuring their health and efficiency and finding technologies to facilitate this became more important. However, the PV inspection costs money, time, and human resources. Thus, many technologies and techniques have been introduced to facilitate PV cell inspection, such as using a drone with a thermal camera. Moreover, as technology evolves every day, so do mobile devices. Nowadays, smartphones have brought many conveniences to our lives. Smartphones have become smarter with applications, and now they can be used as health monitoring devices, projectors, and more. One of smartphones' most revolutionary features and technology is that they can come with a thermal camera or be compatible with thermal cameras. Thermal camera application varies and is diverse, from detecting heat on an object to inspecting photovoltaic PV cells. This paper proposes a novel system consisting of a thermal camera mobile app to detect the defects in PV modules and estimate the defect percentage. The result of this work has shown a potential research and further development area in the field of renewable energy and PV cell inspection. © 2023 IEEE.

关键词： Solar panels

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LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： Classification algorithm human action recognition motion sensors machine learning Masi entropy

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Motorbike Driving Activity Recognition Using Smartphone Motion Sensors 25

Motorbike Driving Activity Recognition Using Smartphone Moti...

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25th International Multi Topic Conference, INMIC 2023

作者： Raheel, Aasim Arsalan, Aamir Noorani, Sadam Hussain Khan, Sheharyar Ehatisham-Ul-Haq, Muhammad Ali, Zohaib University of Engineering & Technology Department of Computer Engineering Taxila Pakistan Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan Air University Department of Creative Technologies Islamabad Pakistan Bahria University Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350317701

Motorbike driving activity recognition plays a crucial role in various domains, including rider safety, vehicle diagnostics, and driver behavior analysis. Traditional methods for activity recognition often rely on dedicated sensors or on-board systems, which can be expensive, cumbersome, or limited in terms of availability. In recent years, the widespread use of smartphones with built-in motion sensors has opened up new possibilities for activity recognition in a more cost-effective and accessible manner. This paper presents a novel approach for motorbike driving activity recognition using smartphone motion sensors. Motorcyclist are inquired to take after a predefined way for recording accelerometer and gyroscope data. Twelve factual features are extricated to classify four driving events i.e., right turn, left turn, U-turn, and a straight path. Four machine learning classifiers i.e., Bayes Net, K-nearest neighbor, support vector machine, and random forest is utilized to classify motorbike driving events. The findings indicate that fusing of a gyroscope and accelerometer can significantly improve the detection of bike driving occurrences, achieving a noteworthy precision rate of 92.13%. © 2023 IEEE.

关键词： Accelerometers

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Yolo Based License Plate Detection of Triple Riders and Violators

Yolo Based License Plate Detection of Triple Riders and Viol...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nandhakumar, R.G. Devi, K. Nirmala Krishnamoorthy, N. Shanthi, S. Pranesh, V.R. Nikhalyaa, S. Kongu Enginering College Perundurai Department of Computer Science and Engineering Tamilnadu Erode India Vellore Institutte of Technology School of Information and Engineering Department of Software Systems and Engineering Vellore India

ISBN: (纸本)9798350348217

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can't continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m∗m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model's detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. © 2023 IEEE.

关键词： Object detection

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Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

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computers, Materials & Continua 2022年第9期72卷 4755-4769页

作者： Shahzad Nazir Muhammad Asif Shahbaz Ahmad Hanan Aljuaid Shahbaz Ahmad Yazeed Ghadi Zubair nawaz Department of Computer Science National Textile UniversityFaisalabadPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia Department of Software Engineering and Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates Department of Data Science University of the PunjabPakistan

Social media provide digitally interactional technologies to facilitate information sharing and exchanging ***,in case of disasters,a massive corpus is placed on platforms such as *** accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is *** approaches have been developed to address this kind of *** recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic ***,these approaches suffer from limitations as they are dataset-dependent and not *** this paper,we proposed a method to identify eyewitnesses from *** experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the *** each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this *** algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words ***,keyword matching for each feature in tweets is *** a feature exists in a tweet,it is termed as 1;otherwise,***,for 13 features,we created a file that reflects features in each *** classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were *** approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest *** results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure *** the same time,the proposed approach gained a 0.88 value of *** results were comparable as the proposed approach is not ***,it can be used for the identification of eyewitness accounts.

关键词： Word dictionary social media eyewitness identification disasters

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