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Uncertainty propagation in the internet of things

Discover Internet of Things 2024年第1期4卷 1-20页

作者： Pal, Shantanu Khalifa, Sara Miller, Dimity Dedeoglu, Volkan Dorri, Ali Ramachandran, Gowri Moghadam, Peyman Kusy, Brano Jurdak, Raja The School of Information Technology Faculty of Science Engineering and Built Environment Deakin University MelbourneVIC 3125 Australia The School of Information Systems Faculty of Science Queensland University of Technology BrisbaneQLD4000 Australia The School of Electrical Engineering and Robotics Faculty of Engineering Queensland University of Technology BrisbaneQLD4000 Australia The Distributed Sensing Systems Group Data61 CSIRO PullenvaleQLD 4069 Australia The School of Computer Science Faculty of Science Queensland University of Technology BrisbaneQLD 4000 Australia The CSIRO Robotics Data61 CSIRO PullenvaleQLD 4069 Australia

The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline can propagate within and across nodes, involving complex interactions with security, privacy, and trust that remain largely unexplored. This paper conducts an in-depth analysis of the types of uncertainties in IoT and how they propagate within IoT nodes and networks. We consider adversarial uncertainty in the context of distributed IoT networks to capture perturbations due to malicious actors in the network that can influence IoT security, privacy and trust. We examine the propagation of adversarial uncertainty and well-known uncertainty types, namely aleatoric and epistemic uncertainty, within the five distinct stages of sensing, communication, storage, processing and decision-making in an IoT pipeline, across network layer boundaries, and across nodes within an IoT network. Using this mapping, we analyse the interactions between the uncertainty types and their propagation, and security, privacy, and trust in IoT. Based on this analysis, we discuss guidelines and considerations for mitigating uncertainty in IoT through a smart grid use case study. © The Author(s) 2024.

关键词： Differential privacy

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Proposed intelligence systems based on digital Forensics: Review paper

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Materials Today: Proceedings 2023年 80卷 2647-2651页

作者： Ismael Sahib, Hussein Qahatan AlSudani, Mustafa Hasan Ali, Mohammed Qassim Abbas, Haydar Moorthy, Kohbalan Mundher Adnan, Myasar Faculty of Computer Science and Information Technology UniversitiTun Hussein Onn Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future. © 2021

关键词： Digital forensics

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Digital Learning Ecosystem: Online Concept for Engineering Education 65

Digital Learning Ecosystem: Online Concept for Engineering E...

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65th IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2024

作者： Zabasta, Anatolijs Kazymyr, Volodymyr Kunicina, Nadezhda Velihorskyi, Oleksandr Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia Chernihiv Polytechnic National University Information and Computer Systems Department Chernihiv Ukraine Chernihiv Polytechnic National University Radio Engineering and Embedded Systems Department Chernihiv Ukraine

ISBN: (纸本)9798350365771

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and Simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the Technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts. © 2024 IEEE.

关键词： Open source software

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Applying Apache Spark on Streaming Big Data for Health Status Prediction

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computers, Materials & Continua 2022年第2期70卷 3511-3527页

作者： Ahmed Ismail Ebada Ibrahim Elhenawy Chang-Won Jeong Yunyoung Nam Hazem Elbakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computers and InformationEl-Zagazig UniversityZagazigSharqiyah44519Egypt Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Big data applications in healthcare have provided a variety of solutions to reduce costs,errors,and *** work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health *** the proposed scalable system,medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning *** this way,healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare *** proposed work also aims to provide an effective recommendation system by using streaming medical data,historical data on a user’s profile,and a knowledge database to make themost appropriate real-time recommendations and alerts based on the sensor’s *** proposed scalable system works by tweeting the health status attributes of *** cloud profile receives the streaming healthcare data in real time by extracting the health attributes via a machine learning prediction algorithm to predict the users’health ***,their status can be sent on demand to healthcare ***,machine learning algorithms can be applied to stream health care data from wearables and provide users with insights into their health *** algorithms can help healthcare providers and individuals focus on health risks and health status changes and consequently improve the quality of life.

关键词： Big data streaming processing healthcare data machine learning IoT data processing Apache Spark

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Analyzing video footage from security cameras using the Amped FIVE professional tool specifically for the identification of vehicle license plates 4

Analyzing video footage from security cameras using the Ampe...

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4th International Conference on Computing, Networking, Telecommunications and Engineering sciences Applications, CoNTESA 2023

作者： Gvozdanovic, Misel Delija, Damir Tulicic, Domagoj Sirovatka, Goran Zagar, Marinko Bilic, Krunoslav Zagreb University of Applied Science Department of Informatics and Computing Zagreb Croatia University of Applied Sciences in Information Technology Department of Computer Systems and Networks Zagreb Croatia

ISBN: (纸本)9798350382754

This paper describes problems of improving security cameras' video footage for forensic investigation. When processing records, one encounters problems with low resolution, poor lighting, or excessive distance of a key object in an image or video. Using the Amped FIVE Professional tool, forensic scientists create videos and analyze and process the record itself using various filters. Record quality demands a choice of video filters. The use and selection of filters show the improvement of the record itself to the level to which useful information can be obtained in this case of vehicle license plates. The presented roadmap can be used as the base for further video forensic research. © 2023 IEEE.

关键词： Image enhancement

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ZeitGeist: A Generic Tool Supporting the Dissemination of Time Series Data Following FAIR Principles 15

ZeitGeist: A Generic Tool Supporting the Dissemination of Ti...

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15th International Conference on Knowledge Management and information systems, KMIS 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Schmidt, Andreas Koubaa, Mohamad Anis Schweikert, Jan Stucky, Karl-Uwe Süß, Wolfgang Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science and Business Information Systems University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9789897586712

An important point for the widespread dissemination of FAIR-data is the lowest possible entry barrier for preparing and providing data to other scientists according to the FAIR criteria. If scientists have to manually extract, transform and annotate the data according to the FAIR criteria and then export it to make it available to the public, this requires a significant investment of time that does not primarily reward the scientist who prepares and provides the data. The Energy Lab at KIT is running a large cluster of an Influx database management system with energy related time series data being stored in a variety of individual databases over periods of up to 15 years. In order to increase the willingness to make data available to the scientific public, we develop a tool that greatly supports and automates the publication and annotation process of time series data stored in Influx databases. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Database systems

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A Proposed Virtual Learning Model Based on Statistical Analysis of Educational Data of Egypt 11th

A Proposed Virtual Learning Model Based on Statistical Analy...

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11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022

作者： Abdella, Aliaa Kamal Elnady, Mai A. Aladel, Lamia Sabry, Heba M. Department of Computers and Information Systems Sadat Academy Cairo Egypt Department of Computer Science Faculty of Computers and Information Sadat Academy Cairo Egypt

ISBN: (纸本)9783031336133

Current political and research agendas all around the world place artificial intelligence (AI) high on the list. Although the foundations for AI have existed for a number of decades, recent advanced directions are accelerating what AI could do. One of these directions is education;those in charge of reforming education must consider the effects of an AI-driven future on education. However, various risks and issues are unavoidably brought on by these quick technological advancements;they have so far outrun policy discussions and regulatory structures. This paper seeks to identify the impact of AI on teaching, learning, and education, by analyzing educational data in Egypt. The data set was downloaded from the ministry of education website. It contained information such as the number of schools, the number of classes, and the number of students per class of the general education and al-Azhar education for primary, preparatory, and secondary stages. In addition, this data set contained information regarding the correlation between different factors. The finding of this research can inform the future design and implementation of the virtual learning model. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Artificial intelligence

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Time Sensitive Networking via 5G - An Overview and Recent Advances 28

Time Sensitive Networking via 5G - An Overview and Recent Ad...

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28. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 28th VDE ITG Conference on Mobile Communication: Technologies and Applications

作者： Kahmann, Felix Muslim, Anas Bin Tönjes, Ralf Islam, Syed Tasnimul Bauschert, Thomas Faculty of Engineering and Computer Science Hochschule Osnabrück - University of Applied Sciences Osnabrück Germany Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany

ISBN: (纸本)9783800763832

Time Sensitive Networking (TSN) is a promising and widely used Ethernet based technology for precise time-aware networking. It enables low latency communication, prioritization of traffic, and replicated and precise machine to machine communication. While Industry 4.0 have exactly these requirements, wide coverage is also needed, which can be difficult with the Ethernet based TSN. Thus, the integration of the 5G and TSN emerges as a solution for Industry 4.0’s ultra reliable low latency and strictly timed communication requirements. 5G’s Release 16 and 17 aims to provide TSN functionality through 5G networks as a service. This paper investigates various approaches regarding how 5G and TSN can be integrated to preserve ultra reliability, low latency, and precisely timed TSN data streams as they navigate through the 5G network. It also focuses on major TSN components, how 5G plans to handle each components’ requirements, and what are the challenges to reach the final TSN-5G integrated solution. Results of private 5G network testings are also presented for easier understanding of the challenges faced in the TSN-5G integration. © 2024 Mobilkommunikation: Technologien und Anwendungen - 28. VDE ITG-Fachtagung. All rights reserved.

关键词： Ethernet

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Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

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computers, Materials & Continua 2022年第5期71卷 3019-3033页

作者： Tahir Alyas Iqra Javed Abdallah Namoun Ali Tufail Sami Alshmrany Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan

Efforts were exerted to enhance the live virtual machines(VMs)migration,including performance improvements of the live migration of services to the *** VMs empower the cloud users to store relevant data and ***,the utilization of servers has increased significantly because of the virtualization of computer systems,leading to a rise in power consumption and storage requirements by data centers,and thereby the running *** center migration technologies are used to reduce risk,minimize downtime,and streamline and accelerate the data center move ***,several parameters,such as non-network overheads and downtime adjustment,may impact the live migration time and server downtime to a large *** virtualizing the network resources,the infrastructure as a service(IaaS)can be used dynamically to allocate the bandwidth to services and monitor the network flow *** to the large amount of filthy retransmission,existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center *** study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold,thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement(SLA).In this research,we analyzed and evaluated the findings observed through simulating different parameters,like availability,downtime,and outage of VMs in data center *** new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

关键词： Cloud computing IaaS data centre storage performance analysis live migration

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