EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2023-10-02T08:33:36+00:00 EAI Publications Department 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). Authors are not charged for article submission and processing. This journal is co-organised, and managed by Duy Tan University, Vietnam.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> Availability of Free-Space Laser Communication Link with the Presence of Clouds in Tropical Regions 2023-08-23T13:33:14+00:00 Thang Nguyen Hoa Le Hien Pham Ngoc Dang <p>Free-space laser communication (lasercom), a great application of using free-space optics (FSO) for satellite communication, has been gaining significant attraction. However, despite of great potential of lasercom, its performance is limited by the adverse effects of atmospheric turbulence and cloud attenuation, which directly affect the quality and availability of lasercom links. The paper, therefore, concentrates on evaluating the cloud attenuation in the FSO downlinks between satellite and ground stations in tropical regions. The meteorological ERA-Interim database provided by the European Center for Medium-Range Weather Forecast (ECMWF) from 2015 to 2020 is used to get the cloud database in several areas in tropical regions. This study proposed a novel probability density function of cloud attenuation, which is validated by using a well-known curve-fitting method. Moreover, we derive a closed-form of satellite-based FSO link availability by applying the site diversity technique to improve the system performance. Numerical results, which demonstrate the urgency of the paper, reveal that the impact of clouds on tropical regions is more severe than in temperate regions.</p> 2023-08-23T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Attention ConvMixer Model and Application for Fish Species Classification 2023-09-06T08:10:16+00:00 Thanh Viet Le Hoang-Minh-Quang Le Van Yem Vu Thi-Thao Tran Van-Truong Pham <p>Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.</p> 2023-09-06T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems SHELF: Combination of Shape Fitting and Heatmap Regression for Landmark Detection in Human Face 2023-09-26T10:11:01+00:00 Ngo Thi Ngoc Quyen Tran Duy Linh Vu Hong Phuc Nguyen Van Nam <p><span dir="ltr" role="presentation">Today, facial emotion recognition is widely adopted in many intelligent applications including the driver </span><span dir="ltr" role="presentation">monitoring system, the smart customer care as well as the e-learning system. In fact, the human emotions </span><span dir="ltr" role="presentation">can be well represented by facial landmarks which are hard to be detected from images, due to the high </span><span dir="ltr" role="presentation">number of discrete landmarks, the variation of shapes and poses of the human face in real world. Over </span><span dir="ltr" role="presentation">decades, many methods have been proposed for facial landmark detection including the shape fitting, the </span><span dir="ltr" role="presentation">coordinate regression such as ASMNet and AnchorFace. However, their performance is still limited for real-</span><span dir="ltr" role="presentation">time applications in terms of both accuracy and e</span><span dir="ltr" role="presentation">ffi</span><span dir="ltr" role="presentation">ciency. In this paper, we propose a novel method called </span><span dir="ltr" role="presentation">SHELF which is the first to combine the shape fitting and heatmap regression approaches for landmark </span><span dir="ltr" role="presentation">detection in human face. The heatmap model aims to generate the landmarks that fit to the common shapes. </span><span dir="ltr" role="presentation">The method has been evaluated on three datasets 300W-Challenging, WFLW, 300VW-E with 31557 images and </span><span dir="ltr" role="presentation">achieved a normalized mean error (NME) of 6.67% , 7.34%, 12.55% correspondingly, which overcomes most </span><span dir="ltr" role="presentation">existing methods. For the first two datasets, the method is also comparable to the state of the art AnchorFace </span><span dir="ltr" role="presentation">with a NME of 6.19%, 4.62%, respectively.</span></p> 2023-09-26T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems A Multi-objective optimization model for sustainable production planning in textile MSMEs 2023-09-27T11:52:58+00:00 Pablo Flores-Siguenza Jose Antonio Marmolejo-Saucedo Rodrigo Guamán <p class="ICST-abstracttext"><span lang="EN-GB">Textile MSMEs are characterized by their high influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment, in recent years the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. It is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, safe for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant conclusions of the study is the contribution to the textile industry with a sustainable decision support tool, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.</span></p> 2023-09-27T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Context-Aware Device Classification and Clustering for Smarter and Secure Connectivity in Internet of Things 2023-10-02T08:31:16+00:00 Priyanka More Sachin Sakhare <p>With the increasing prevalence of the Internet of Things (IoT), there is a growing need for effective access control methods to secure IoT systems and data. Traditional access control models often prove inadequate when dealing with the specific challenges presented by IoT, characterized by a variety of heterogeneous devices, ever-changing network structures, and diverse contextual elements. Managing IoT devices effectively is a complex task in maintaining network security.</p><p>This study introduces a context-driven approach for IoT Device Classification and Clustering, aiming to address the unique characteristics of IoT systems and the limitations of existing access control methods. The proposed context-based model utilizes contextual information such as device attributes, location, time, and communication patterns to dynamically establish clusters and cluster leaders. By incorporating contextual factors, the model provides a more accurate and adaptable clustering mechanism that aligns with the dynamic nature of IoT systems. Consequently, network administrators can configure dynamic access policies for these clusters.</p> 2023-10-02T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems