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Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

Computers, Materials & Continua 2022年第2期70卷 3653-3666页

作者： Inam Ullah Khan Muhammad Abul Hassan Muhammad Fayaz Jeonghwan Gwak Muhammad Adnan Aziz Department of Electronic Engineering Isra University(SEAS)Islamabad44000Pakistan Department of Computing and Technology Abasyn University Peshawar25000Pakistan Deparment of Computer Engineering Jeju National UniversityJejuKorea Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Most interesting area is the growing demand of flying-IoT mergers with smart ***,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy *** order to communicate effectively,IoT is a key element for smart *** improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end ***,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld *** issue leads to the development of lightweight energyefficient routing in flying-IoT *** paper addresses the energy conservation problem in *** paper presents a novel approach for the internet of flying vehicles using DSDV ***-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale *** shows optimal results in comparison with other contemporary routing *** mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.

关键词： Flying-IoT DSR smart cities UAV’s FANETS

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Generalizability of CNN Architectures for Face Morph Presentation Attack

arXiv

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arXiv 2023年

作者： HmaSalah, Sherko R. Asaad, Aras T. Department of Software Engineering Faculty of Engineering Koya University Danielle Mitterrand Boulevard KOY45 Kurdistan Region Koya Iraq School of computing The University of Buckingham United Kingdom

Automatic border control systems are wide spread in modern airports worldwide. Morphing attacks on face biometrics is a serious threat that undermines the security and reliability of face recognition systems deployed in airports and border controls. Therefore, developing a robust Machine Learning (ML) system is necessary to prevent criminals crossing borders with fake identifications especially since it has been shown that security officers cannot detect morphs better than machines. In this study, we investigate the generalization power of Convolutional Neural Network (CNN) architectures against morphing attacks. The investigation utilizes 5 distinct CNNs namely ShuffleNet, DenseNet201, VGG16, EffecientNet-B0 and InceptionResNet-v2. Each CNN architecture represents a well-known family of CNN models in terms of number of parameters, architectural design and performance across various computer vision applications. To ensure robust evaluation, we employ 4 different datasets (Utrecht, London, Defacto and KurdFace) that contain a diverse range of digital face images which cover variations in ethnicity, gender, age, lighting condition and camera setting. One of the fundamental concepts of ML system design is the ability to generalize effectively to previously unseen data, hence not only we evaluate the performance of CNN models within individual datasets but also explore their performance across combined datasets and investigating each dataset in testing phase only too. Experimental results on more than 8 thousand images (genuine and morph) from the 4 datasets show that InceptionResNet-v2 generalizes better to unseen data and outperforms the other 4 CNN models. © 2023, CC BY.

关键词： Biometrics

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Research on the Current State of Privacy Risks in Children's Mobile Applications 4

Research on the Current State of Privacy Risks in Children's...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Geng, Xinrui Liu, Tao Shang, Chong Chen, Jianxiang Lu, Yueran Zhang, Yan Wang, Lifeng Xi'an Jiaotong-Liverpool University School of AI and Advanced Computing Suzhou China MIIT Key Laboratory of Mobile Application Innovation and Governance Technology Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China Shanxi Agricultural University School of Agricultural Engineering China Tianjin Normal University School of Software Engineering Tianjin China University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China Shandong Yeda Smart City Operation Technology Co. Ltd. Shandong China

ISBN: (纸本)9798350354560

In the digital age, children increasingly access a vast world of information through mobile applications. Despite global regulations and protective measures by app stores, privacy breaches in these apps persist. This study investigates privacy issues and threats in children's mobile applications by comparing APKs and legal provisions across different countries. It examines 70 representative children's apps from China, Europe, the U.S., and Japan through static code analysis and dynamic runtime testing. Using a mixed-methods approach, the research identifies integrated third-party services, data transmission protocols, permission requests, and conducts code audits to assess privacy compliance. It also simulates real usage scenarios to track data flow and detect unauthorized data collection and sharing practices. Furthermore, the study compares national privacy laws, focusing on data minimization, parental consent mechanisms, data encryption, and storage duration regulations. The goal is to provide empirical evidence and recommendations for optimizing global children's online privacy protection policies. © 2024 IEEE.

关键词： Differential privacy

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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An Efficient Ensemble approach for Fake Reviews Detection

An Efficient Ensemble approach for Fake Reviews Detection

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Artificial Intelligence (ICAI), International Conference on

作者： Asif Iqbal Muhammad Arslan Rauf Muhammad Zubair Tanveer Younis Riphah College of Computing Riphah International University Faisalabad Pakistan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

People prefer to buy items and services online to save time in the current age of growing e-commerce. these internet purchases are heavily impacted by the reviews or opinions of people who have already purchased them. customers provide comments to businesses on how to improve product quality, develop, and monitor business strategies in order to boost sales and profits. customers may also use these comments to choose the proper items with less effort and time spent. giving fake review is the practice of fraudulent people who wants to promote or degrade products or services for illegitimate monetary gain. in this research paper, we present an ensemble machine-learning model to identify whether a review is fraudulent or authentic. to achieve this objective, amazon reviews dataset is used. the proposed ensemble model outperformed as compared to other individual classifiers. random forest provides 99% accuracy which is better than other algorithms.

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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Deep Reinforcement Learning with Human Guidance for Autonomous Driving Car Following

Deep Reinforcement Learning with Human Guidance for Autonomo...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wenbo Wang Qingchao Xu Xingfu Yang Yandong Liu School of Software Engineering Dalian University Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

To solve the complex modeling and difficult parameter adjustment of traditional car-following control methods, we propose the car-following framework KgPPO-CF. The KgPPOCF brings expert experience into the interactive process of Deep Reinforcement Learning, which improves the speed and robustness of training policies. We use four different reward functions and compare the effects of policies trained by different designs on car-following control. In the Carla simulator, KgPPO-CF achieves car-following control in a continuous action space. The experimental results show that, compared with the traditional control methods, the control strategy obtained by KgPPO-CF training has better adaptability.

关键词： Training Accuracy Mechatronics Aerospace electronics Deep reinforcement learning Robustness Automobiles Robotics and automation Standards Autonomous vehicles

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Enhancing Anomaly Detection in Electric Vehicle Supply Equipment (EVSE) Networks Using Classical and Ensemble Learning Approaches

Enhancing Anomaly Detection in Electric Vehicle Supply Equip...

引用

Control Instrumentation System Conference (CISCON)

作者： Sandeep R. Hegde Vijayabhaskaramoorthy V. K. R. M. Vijaya Chandrakala Gireesh Kumar T. V. K. Arun Shankar Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Software Testing Engineer Danfoss Industries Private Limited USA

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Electric Vehicle Supply Equipment (EVSE) networks, integral to modern transportation systems, are vulnerable to cyber threats due to integration into smart grids and IoT ecosystems. EVSE network A represents a traditional, centralized EV charging infrastructure, whereas EVSE network B denotes a distributed, decentralized charging network with varying levels of connectivity. This research investigates the security landscape of EVSE networks A and B, focusing on the detection and classification of network attacks. Through meticulous comparative analysis, the research highlights the effectiveness of Ensemble Learning over Classical learning models in improving attack classification precision. This approach enhances the precision of attack detection by leveraging diverse algorithms and data sources to mitigate security risks effectively.

关键词： Biological system modeling Soft sensors Instruments Ecosystems Transportation Smart grids Security Ensemble learning Internet of Things Context modeling

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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