https://publications.eai.eu/index.php/inis/issue/feed EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025-03-12T15:37:47+00:00 EAI Publications Department publications@eai.eu Open Journal Systems <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). This journal is co-organized, and managed by Duy Tan University, Vietnam.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> https://publications.eai.eu/index.php/inis/article/view/6780 Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach 2024-12-06T07:36:06+00:00 Thi-Tuyet-Hai Nguyen tuyethai@ptithcm.edu.vn Tran Cong-Hung tranconghung@siu.edu.vn Nguyen Hong-Son ngson@ptithcm.edu.vn Tan Hanh tanhanh@ptithcm.edu.vn Tran Trung Duy trantrungduy@ptithcm.edu.vn Lam-Thanh Tu lamthanh0@gmail.com <p>The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.</p> 2024-12-05T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/7612 Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection 2025-03-11T13:31:37+00:00 Amrutha Annadurai triloknath.pandey@vit.ac.in Manas Ranjan Prusty triloknath.pandey@vit.ac.in Trilok Nath Pandey triloknath.pandey@vit.ac.in Subhra Rani Patra triloknath.pandey@vit.ac.in <p class="ICST-abstracttext"><span lang="EN-GB">INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. </span></p> <p class="ICST-abstracttext"><span lang="EN-GB">OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. </span></p> <p class="ICST-abstracttext"><span lang="EN-GB">METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network.</span></p> <p class="ICST-abstracttext"><span lang="EN-GB">RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. </span></p> <p class="ICST-abstracttext"><span lang="EN-GB">CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model.</span></p> 2025-03-11T00:00:00+00:00 Copyright (c) 2024 Amrutha Annadurai, Manas Ranjan Prusty, Trilok Nath Pandey, Subhra Rani Patra https://publications.eai.eu/index.php/inis/article/view/7616 A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark 2025-01-09T07:08:19+00:00 Muhammed Onur Kaya muhammedonurkya@gmail.com Mehmet Ozdem mehmet.ozdem@telekom.com.tr Resul Das resuldas@gmail.com <p>This paper presents a novel approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, globally's most widely utilized network analysis tool. As the complexity and volume of network data continue to grow, effective anomaly detection has become essential for maintaining network performance and enhancing security. Our method leverages Wireshark’s robust data collection and analysis capabilities to identify anomalies swiftly and accurately. In addition to detection, we introduce innovative visualization techniques that facilitate the intuitive representation of detected anomalies, allowing network administrators to comprehend network conditions and make informed decisions quickly. The results of our study demonstrate significant improvements in both the efficacy of anomaly detection and the practical applicability of visualization tools in real-time scenarios. This research contributes valuable insights into network security and management, highlighting the importance of integrating advanced analytical methods with effective visualization strategies to enhance the overall management of dynamic networks.</p> 2025-01-07T00:00:00+00:00 Copyright (c) 2024 Muhammet Onur Kaya, Mehmet Ozdem, Resul Das https://publications.eai.eu/index.php/inis/article/view/7859 Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm 2025-01-09T07:09:45+00:00 Trang Hoang hoangtrang@hcmut.edu.vn <p><span class="fontstyle0">This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper<br />facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the e</span><span class="fontstyle2">ffi</span><span class="fontstyle0">ciency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation.</span></p> 2025-01-08T00:00:00+00:00 Copyright (c) 2024 Trang Hoang https://publications.eai.eu/index.php/inis/article/view/8600 Integrated Cloud-Twin Synchronization for Supply Chain 5.0 2025-03-12T15:35:37+00:00 Divya Sasi Latha divyasasilatha@gmail.com Tartat Mokkhamakkul tartat@cbs.chula.ac.th <p>The digital twin is thus emerging means of improving real-world performance from virtual spaces, especially relatedto Supply Chain 5.0 in Industry 5.0. This framework employs the integration of cloud computing and digital twin technologies to secure data storage, trusted tracking, and high reliability, is architectural for the integration of supply-chain sustainable enterprises. In this work, we introduce a high level architecture of cloud-based digital twin model for supply chain 5.0 , which was created to align the system of supply chain through real-time observation as well as real-timesupply chain 5.0 decision-making and control. This study introduces a cloud-based twin optimization model for Supply Chain 5.0, validated through genetic algorithm (GA) simulations. The model determines optimal weights to balance objectives, achieving an optimal objective function value that reflects trade-offs among operational efficiency, cost, and sustainability. A convergence plot illustrates the model’s iterative solution improvements, demonstrating its dynamic adaptability. Lastly, the proposed model defines and test a supply chain performance analysis through dynamic simulations.</p> 2025-03-12T00:00:00+00:00 Copyright (c) 2024 Divya Sasi Latha, Tartat Mokkhamakkul