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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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An Improved Weighted Ensemble Clustering Based on Two-Tier Uncertainty Measurement

SSRN

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SSRN 2022年

作者： Gu, Qinghua Wang, Peipei Li, Xuexian Chen, Lu Xiong, Neal N. School of Management Xi’an University of Architecture and Technology Xi’an China Xi’ an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi'an University of Architecture and Technology Xi’an China Department of Mathematics and Computer Science Northeastern State University OK United States

Existing local weighted ensemble clustering algorithms strive to weight on each cluster, which take into account the differences among all clusters. However, the base clustering label on each cluster is ignored. The purpose of this paper is to combine the influence of cluster level and base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts global weighting strategy into a locally ensemble cluster learning framework. First, the cluster uncertainty based an entropy criterion is refined by considering the base clustering labels for each cluster. Then, two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. The effectiveness of the proposed framework TWEC is verified against fourteen real-world datasets and ten comparison algorithms. The results demonstrate that the TWEC framework can improve the robustness and stability of the clustering. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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Recurrent Neural Networks and Efficiency in High-Dimensional EEG Classification 1st

Recurrent Neural Networks and Efficiency in High-Dimensional...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： León, Javier Escobar, Juan José González, Jesús Ortega, Julio Arrabal-Campos, Francisco Manuel Ortiz, Andrés Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Communications Engineering University of Málaga Málaga Spain

ISBN: (纸本)9783030881627

The performance of neural networks has granted deep learning a place at the forefront of machine learning in the last decade. Although these models are computationally intensive, their advantage is recognized in a wide array of applications. Nonetheless, the large amount of learnable parameters in neural networks can be a disadvantage for small and high-dimensional datasets. In this context, the relationship between solution quality and model complexity gains significance due to overfitting concerns. This is precisely the case of Electroencephalography (EEG), where the data consists in relatively few patterns obtained from multiple electrode readings. In this paper, we take a look at two widely-known types of Recurrent Neural Networks (RNNs), namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), which are evaluated on three EEG Motor Imagery (MI) datasets by measuring not only classification accuracy, but also time and energy consumption. Although the results are not entirely competitive with the state-of-the-art in the datasets considered, the interaction that occurs between the size of the networks and their accuracy provides us with valuable insight about network structure design in the context of the curse of dimensionality. © 2021, Springer Nature Switzerland AG.

关键词： Electroencephalography

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Co-designing a technology Probe with Experienced Designers 33

Co-designing a Technology Probe with Experienced Designers

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33rd Australian computer-Human Interaction Conference, OzCHI 2021

作者： Gough, Phillip Kocaballi, A. Baki Naqshbandi, Khushnood Z. Cochrane, Karen Mah, Kristina Pillai, Ajit G. Yorulmaz, Yeliz Deny, Ainnoun Kornita Ahmadpour, Naseem Affective Interactions Lab School of Architecture Design and Planning The University of Sydney Australia School of Computer Science University of Technology Sydney Australia School of It Carleton University Canada Independent Artist Australia

ISBN: (纸本)9781450395984

technology probes are low-fidelity devices that can be used to understand research participant's lived experiences, but they are not usually subject to iterative design. There are opportunities in human-computer interaction to develop technology probes through co-design, by including diverse perspectives during probe development. To explore this opportunity, five design researchers with different disciplinary and cultural backgrounds engaged with a technology probe to support daily reflections, discussed new directions in a co-design workshop and developed narratives to negotiate possibilities of the probe. This paper presents observations described by each of the researchers using the probe, and insights from the process we followed. We discuss how the the designers' postitionalities are reflected in the processes, and how they brought value by shaping narratives of the different roles a technology probe might take. We also discuss how we may use co-design of technology probes as a generative method, highlight the importance of open-endedness in the process, and reflect on lessons learned. © 2021 ACM.

关键词： Iterative methods

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Deep Learning Techniques for Overcoming Interoperability Challenges in Smart Grid Systems 2

Deep Learning Techniques for Overcoming Interoperability Cha...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Devi, M. Kumar Rai, Anjani Reddy, Avala Raji Neelima, Gogineni Obaid, Saif Chidambaranathan, Senthilnathan New Prince Shri Bhavani College of Engineering and Technology Department of EEE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India Department of Mechanical Engineering CMR Technical Campus Telangana Hyderabad India GRIET Bachupally Department of CSE Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq Virtusa Corporation Department of Architecture and Design New Jersey United States

ISBN: (纸本)9798350368109

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as;predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature;Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency. © 2024 IEEE.

关键词： Smart power grids

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Edge Computing in Smart Cities: Enhancing Real-Time Data Processing 2

Edge Computing in Smart Cities: Enhancing Real-Time Data Pro...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Rajakumari, S. Natarajan, C. Alone, Vinod N. Senthilkumar, P. Gayathri, R. John, Godwin J. Department of Mathematics R.M.D. Engineering College Chennai India Department of CSE P. S. R Engineering College Tamilnadu Sivakasi India Computer Engineering Department VPPCOE &VA Sion Maharashtra Mumbai India School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331541217

One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on DenseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply DenseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of- the-art. © 2024 IEEE.

关键词： Deep Learning DenseNet Smart Cities Traffic Management

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Run Time Memory Error Recovery Process in Networking System 7

Run Time Memory Error Recovery Process in Networking System

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7th International Conference on System Reliability and Safety, ICSRS 2023

作者： Vitucci, Carlo Sundmark, Daniel Danielsson, Jakob Jägemar, Marcus Larsson, Alf Nolte, Thomas Technology Management Ericsson Ab Stockholm Sweden Computer Science and Software Enigineering Mälardalen University Västerås Sweden Sys Architecture Ericsson Ab Stockholm Sweden Sys Compute Dimensioning Ericsson Ab Stockholm Sweden Senior Specialist Observability Ericsson Ab Stockholm Sweden Mälardalen University Division of Networked and Embedded System Västerås Sweden

ISBN: (纸本)9798350306057

System memory errors have always been problematic;today, they cause more than forty percent of confirmed hardware errors in repair centers for both data centers and telecommunications network nodes. Therefore, it is somewhat expected that, in recent years, device manufacturers improved the hardware features to support hardware-assisted fault management implementation. For example, the new standard, DDR5, includes both data redundancy, the so-called Error Correcting Code (ECC), and physical redundancy, the post-package repair (PPR), as mandatory features. Production and repair centers mainly use physical redundancy to replace faulty memory rows. In contrast, field use still needs to be improved, mainly due to a need for integrated system solutions for network nodes. This paper aims to compensate for this shortcoming and presents a system solution for handling memory errors. It is a multi-technology proposition (mixed use of ECC and PPR) based on multi-layer (hardware, firmware, and software) error information exchange. © 2023 IEEE.

关键词： Redundancy

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Gerber File Parsing and the Implementation Method of its Conversion to Bitmap Image

Gerber File Parsing and the Implementation Method of its Con...

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2021 International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation technology, MEMAT 2021

作者： Fan, Yong Wu, Shuqin Wu, Xie Yang, Jianbo School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin Guangxi China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin Guangxi China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China

In order to obtain the standard image based on the Gerber file in the AOI (Automatic Optical Inspection), an implementation method is proposed by analysing and converting the Gerber file into a bitmap image. According to the command format rules of Gerber file, the approach is designed to analyse Gerber files by constructing the command regular expression of Gerber files. In order to achieve convenient access to the drawing data, the drawing data processing is performed by defining a class, then the drawing data is stored in a dynamic array and the adjustment factors are added in the drawing data at the same time to meet the further post-processing needs of bitmap image. This method can quickly analyse Gerber files, adjust drawing data and generate bitmap images. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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