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arXiv 2022年

作者： Kalogiratos, Athanasios Kantzavelou, Ioanna Dept. of Informatics and Computer Engineering University of West Attica Egaleo Athens12243 Greece

Bluetooth is a communication technology used to wirelessly exchange data between devices. In the last few years there have been found a great number of security vulnerabilities, and adversaries are taking advantage of them causing harm and significant loss. Numerous system security updates have been approved and installed in order to sort out security holes and bugs, and prevent attacks that could expose personal or other valuable information. But those updates are not sufficient and appropriate and new bugs keep showing up. In Bluetooth technology, pairing is identified as the step where most bugs are found and most attacks target this particular process part of Bluetooth. A new technology that has been proved bulletproof when it comes to security and the exchange of sensitive information is Blockchain. Blockchain technology is promising to be incorporated well in a network of smart devices, and secure an Internet of Things (IoT), where Bluetooth technology is being extensively used. This work presents a vulnerability discovered in Bluetooth pairing process, and proposes a Blockchain solution approach to secure pairing and mitigate this vulnerability. The paper first introduces the Bluetooth technology and delves into how Blockchain technology can be a solution to certain security problems. Then a solution approach shows how Blockchain can be integrated and implemented to ensure the required level of security. Certain attack incidents on Bluetooth vulnerable points are examined and discussion and conclusions give the extension of the security related problems. © 2022, CC BY-NC-SA.

关键词： Bluetooth

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Federated Digital Twin

Federated Digital Twin

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos School of Computer Science University of Birmingham Birmingham UK Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

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Towards Personalized Fitness with Multi-modal Wearable Heart Rate Monitors: A Proposal

Towards Personalized Fitness with Multi-modal Wearable Heart...

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Vocational Education and Electrical engineering, International Conference on

作者： Alfian Dwi Nugraha Pradini Puspitaningayu Rahmat Hidayatullah Eko Sakti Pramukantoro Parama Diptya Widayaka Naim Rochmawati Dept. of Informatics Engineering State University of Surabaya Surabaya Indonesia Dept. of Electrical Engineering State University of Surabaya Surabaya Indonesia Faculty of Computer Science Brawijaya University Malang Indonesia

Nowadays, the trend for home workouts is getting more and more popular. The growth of wearable fitness trackers from basic accelerometers, pedometers, pulse detectors, and ECGs also supports the advancement of personal fitness monitoring. Everyone has a distinct level of fitness, in theory. The heart rate, which is correlated with physical activity, such as sports, demonstrates how this organ functions. As the muscles work harder at a high intensity, the heart rate will also rise. This paper presents the design and implementation of a multimodal wearable heart rate monitoring system. The software is Android-based and is designed to be able to receive heart rate data sent by two different wearable heart rate monitors simultaneously using Bluetooth low-energy protocol. One uses an optical heart monitor and the other uses an electrocardiogram or ECG. The implementation also includes IoT for real-time data acquisition and a web-based application for wider accessibility. The system is evaluated by collecting packets from two devices for 60 minutes. The results underscore the feasibility of the system in collecting users’ heart rate data either using different devices. Furthermore, it serves as a foundation for future studies aiming to expand the personalization capabilities of wearable devices in healthcare and fitness.

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Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data 20

Vehicle Classification in Intelligent Transportation Systems...

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20th IEEE International Conference on Intelligence and Security informatics, ISI 2023

作者： Hashima, Sherief Saad, Mohamed H. Hatano, Kohei Rizk, Hamada Computational Learning Theory Team RIKEN-AIP Fukuoka819-0395 Japan Egyptian Atomic Energy Authority Engineering Dept Cairo13759 Egypt Egyptian Atomic Energy Authority Ncrrt Radiation Engineering Dept Cairo Egypt Kyushu University Department of Informatics Fukuoka819-0395 Japan Graduate School of Information Science and Technology Osaka University Suita565-0871 Japan Tanta University Computer and Control Engineering Department Tanta31733 Egypt

ISBN: (纸本)9798350337730

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques. © 2023 IEEE.

关键词： Vehicles

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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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International Conf on Electrical and Electronic engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of Things Data mining

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Atherosclerotic Carotid Plaque AM-FM Texture Feature Variability in Ultrasound Video

Atherosclerotic Carotid Plaque AM-FM Texture Feature Variabi...

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IEEE International Symposium on Biomedical Imaging

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Christos P. Loizou Efthyvoulos Kyriacou Andreas Panayides Andrew N. Nicolaides Constantinos S. Pattichis Marios S. Pattichis Department of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical Computer Engineering and Informatics Cyprus University of Technology Cyprus CYENS Centre of Excellence Nicosia Cyprus Cyprus Cardiovascular Disease Educational Research Trust Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal here was to identify AM-FM features that are associated with increased risk of stroke. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract plaque histogram features. A small dataset of 5 asymptomatic and 3 symptomatic videos were analyzed. Selected AM-FM plaque histogram texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between asymptomatic and symptomatic videos. However, further evaluation with more subjects needs to be carried out to exploit the usefulness of the proposed analysis in the clinical context.

关键词： Histograms Ultrasonic imaging Frequency modulation Stability criteria Atherosclerosis Feature extraction Object recognition

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset

Generalization Evaluation of Seq2seq based Fog Computing App...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Michael S. M. Pakpahan Lukito Edi Nugroho Widyawan Widyawan Ajie Kusuma Wardhana Muhammad Ardian R.A Rangga Satria Astagenta Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. Informatics Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network’s edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model’s performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?; 2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios.

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Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Media

Unconventional Computing based on Four Wave Mixing in Highly...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Kostas Sozos Stavros Deligiannidis Charis Mesaritakis Adonis Bogris Dept. of Informatics and Computer Engineering. Aghiou Spiridonos University of West Attica Egaleo Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Karlovassi Samos Greece

ISBN: (纸本)9798350345995

Nowadays, photonic analog computing in the form of neuromorphic photonics or photonic neural networks has resulted in powerful hardware platforms that accelerate difficult tasks with marginal power consumption. Although linear transformations are efficiently carried out in the optical domain, nonlinear transformations are also of paramount importance. Here, we propose four wave mixing (FWM) process as an elegant solution for the generation of diverse nonlinear higher order products of an original signal. On one hand, these products can act as activation functions in different types of neural networks, either recurrent neural networks (RNNs) and reservoir computers or in any other type of brain-inspired frameworks. On the other hand, the same products can form the basis for polynomial regression which is a very powerful technique used in machine learning in different tasks i.e. for the prediction of COVID-19 transmission [1].

关键词： Four wave mixing Machine learning Neural networks Nonlinear wave mixing Optical computing Quantum computation

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On the Way to Realize the 5thIndustrial Revolution: Achievements, Challenges and Research Areas 36

On the Way to Realize the 5thIndustrial Revolution: Achievem...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Dimirakopoulos, George Gutt, Thomas Ehm, Hans Hoess, Alfred Karathanasopoulou, Konstantina Lackner, Anna Schneider, Germar Harokopio Univesity of Athens Dept. of Informatics and Telematics Athens Greece Infineon Technologies A.G. Munich Germany University of Applied Sciences Amberg-Weiden Dept. of Electrical Engineering Media and Computer Science Amberg Germany Eesy-innovation GmbH Unterhaching Germany Infineon Technologies Dresden GmbH&Co.KG Dresden Germany

ISBN: (纸本)9781665477161

The industrial world is rapidly moving towards its 5th industrial revolution (a.k.a. 'Industry5.0'). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing A I-enabled hardware, as well as A I tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty. © 2023 IEEE.

关键词： Sustainable development

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