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  • Development complexity of Cyber-Physical Systems: theoretical and practical benefits from Pre-Integrated Architectures

    Philippe Gougeon, Etienne Hamelin
    J Smart Environ Green Comput 2023;3:3-17. DOI: 10.20517/jsegc.2022.17
    Abstract
    Aim: The growing complexity of Cyber-Physical Systems (CPS) impacts resources and development time. Hence, numerous ... MORE
    Aim: The growing complexity of Cyber-Physical Systems (CPS) impacts resources and development time. Hence, numerous component-based design approaches have been developed to mitigate complexity and, consequently, the R&D effort. But is complexity even measurable? The aims of this paper are: to contribute to the CPS and Product Line Engineering (PLE) fields of research; to understand and apply the tools estimating CPS complexity; to evaluate the benefits of Pre-Integrated Architectures (PIARCHs) from the point of view of CPS complexity.Methods: Based on prior studies, we found that complexity is measurable. We used a structural complexity metric to calculate the impact of PIARCHs by creating variants of a given system architecture. In a practical application project, we used PIARCHs on two types of use cases: generic ones, like localization and perception, and a highly specific one: Urban Automated Driving.Results: Based on the calculation established by complexity metrics, PIARCHs reduce complexity. This has been revealed in theoretical and practical approaches. Generic use cases like localization and perception of an automated vehicle have more benefits with PIARCHs than the complex Urban Automated Driving use case. This can be explained by the fact that the number of inputs and parameters is smaller, and after the initial investment, the field of applications is wider.Conclusion: Project complexity is measurable, and the impact of PIARCHs mitigating complexity can be assessed. Their impact varies according to the complexity of the use case and the width of the field of applications. A minimum of complexity is required to justify the initial investment. However, an excessive PIARCH complexity reduces the number of applications and the payback of the initial investment. LESS      Full article
    Original Article|Published on: 14 Mar 2023
    Full-Text | PDF-2M

  • Machine Learning perspectives

    Hamido Fujita
    J Smart Environ Green Comput 2023;3:1-2. DOI: 10.20517/jsegc.2023.01
    Editorial|Published on:  8 Feb 2023
    Full-Text | PDF-166K

  • Towards green machine learning: challenges, opportunities, and developments

    Witold Pedrycz
    J Smart Environ Green Comput 2022;2:163-74. DOI: 10.20517/jsegc.2022.16
    Abstract
    Machine Learning has assumed a prominent position in the plethora of design and analysis of ... MORE
    Machine Learning has assumed a prominent position in the plethora of design and analysis of intelligent systems. Learning is the holy grail of Machine Learning, and with rapidly growing complexity and the size of the constructed networks (the trend which is profoundly visible in deep learning architectures), the overwhelming computing is staggering. The return on investment clearly diminishes: even a very limited improvement in performance (commonly expressed as a classification rate or prediction error) does call for intensive computing because of learning a large number of parameters. The recent developments in green Artificial Intelligence (or better to say, green Machine Learning) has identified and emphasized a genuine need for a holistic multicriteria assessment of the design practices of Machine Learning architectures by involving computing overhead, interpretability, robustness, and identifying sound trade-offs present in these problems. We discuss a realization of green Machine Learning and advocate how Granular Computing contributes to the augmentation of the existing technology. In particular, some paradigms that exhibit a sound potential to support the sustainability of Machine Learning such as federated learning and transfer learning, are identified, critically evaluated, and cast into some general perspective. LESS      Full article
    Position Paper|Published on: 31 Dec 2022
    Full-Text | PDF-1.4M

  • UAV assisted communication for ground users using machine learning and optimization

    Prakash Ravi, ... Miao Wang
    J Smart Environ Green Comput 2022;2:175-91. DOI: 10.20517/jsegc.2022.05
    Abstract
    Aim: Among a large number of products that support communication, there is one called space ... MORE
    Aim: Among a large number of products that support communication, there is one called space air ground integrated networks (SAGIN's), which is the most commonly used to support users in rural and emergency situations. Typicaly in emergency situations SAGIN's use unmanned aerial vehicles (UAVs) in their air layer to temporarily support the ground users. Although the cost of UAVs is lower than that of traditional base stations, and their actions are more flexible, but their battery life problems lead to frequent charging of drones, resulting in many resource losses and unable to provide communication support. In order to mitigate the issues, novel optimization algorithms need to be developed to support the ground users.Methods: In this work, we develop a grid-based deep learning method using the LSTM model to estimate the number of ground users as vehicles in each area, and developed an optimization algorithm to minimize the number of UAVs needed to the user's and meanwhile to satisfy quality of service (QoS) requirements. For optimization, we mainly use the Linear Optimization Tools and an objective function has been developed using density predicted and SINR data to achieve acceptable QoS.Results: The simulation results shows that this approach has improved the quality of the communication by 50%.Conclusion: Using unique grid technique and the LSTM machine learning model, the user densities on each partitioned grid is determined. Finally, a linear optimization algorithm is developed based on the user density to determine the lowest number of UAVs to support the users in each grid while maintaining the QoS. LESS      Full article
    Original Article|Published on: 31 Dec 2022
    Full-Text | PDF-580K

  • Leveraging the GQM+ Strategy approach and Industry 4.0 technologies for environmental sustainability in manufacturing

    Giancarlo Nota, Alonso Toro Lazo
    J Smart Environ Green Comput 2022;2:143-62. DOI: 10.20517/jsegc.2022.13
    Abstract
    Aim: In the last years, sustainability has been identified as an enormous problem, with many ... MORE
    Aim: In the last years, sustainability has been identified as an enormous problem, with many facets gaining increasing attention. In this broad scenario, the availability of models for environmental sustainability constitutes a conceptual tool to guide industries towards reducing the environmental impact deriving from production. This work aims to contribute to the research on environmental sustainability in manufacturing by proposing a model that leverages the Goal Question Metrics approach and technologies of Industry 4.0.Methods: The Goal Question Metrics approach and technologies of Industry 4.0 are leveraged by proposing a model that contributes to environmental sustainability in manufacturing.Results: A model is proposed that can be used as a conceptual tool to support improvement programs in environmental sustainability.Conclusion: The application of the Goal Question Metrics+ Strategies to a case study of an automotive industry shows how the approach, combined with the implementation of Industry 4.0 technologies, contributes to the efficient use of natural resources and also reduces the emissions in the atmosphere. LESS      Full article
    Original Article|Published on: 30 Sep 2022
    Full-Text | PDF-1.1M

  • Solar powered UAV charging strategy design by machine learning

    Prakash Ravi, ... Mark J Scott
    J Smart Environ Green Comput 2022;2:126-42. DOI: 10.20517/jsegc.2022.02
    Abstract
    Aim: The rapid growth in the number of ground users over recent years has introduced ... MORE
    Aim: The rapid growth in the number of ground users over recent years has introduced the issues for a base station of providing more reliable connectivity and guaranteeing the reasonable quality of service (QoS). Thanks to the unique features of unmanned aerial vehicles (UAVs), such as flexibility in deployment, large coverage range and lower cost, UAVs can help the base station to provide wireless connectivity to the ground users, e.g., in rural and remote areas. As the energy limitation is the main concern for UAVs, the motivation is to provide uninterrupted connection to ground users in the next generation wireless networks using solar powered UAV-assisted air networks.Methods: The research uses global horizontal irradiance (GHI) data from the National Renewable Energy Laboratory, small cell power ratings for communication, and UAV parameters. In addition, the TensorFlow library and Python programming language were also used to develop machine learning models and simulate the UAV flying time.Results: In this paper, we develop a novel resource management system for UAVs, which consists of an energy harvesting deep learning model to predict the future power harvested from the solar panel and a consumption model which determines user arrival rate. With energy consumption and harvesting predictions, the resource management system adaptively switches the power consumed by a UAV for communication. In addition, based on the future energy availability and user's arrival rate, the resource management system communicates with other UAVs and enables energy coordinating scheduling among multiple UAVs to support user communications. The experiment results demonstrate that by using adaptive energy scheduling among UAVs, the flying time of the UAVs is improved by 40% during nighttime and by 37% when performing energy coordination among multiple UAVs.Conclusion: In this work, the UAV based communications have been researched. To understand more about UAVs and air segments, some literature review has been done based on previous works. Finally, alteration of the transmission power using several methodologies has been accomplished to increase the flying time of the UAV. LESS      Full article
    Original Article|Published on:  1 Sep 2022
    Full-Text | PDF-830K
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About The Journal

  • ISSN

    2767-6595 (Online)

    Publisher

    OAE Publishing Inc.

    Article Processing Charges

    $1200

  • Editor-in-Chief

    Witold Pedrycz

    Publishing Model

    Gold Open Access

    Copyright

    Copyright is retained by author(s)

  • Publication Frequency

    Quarterly

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    Total publications: 33

    Total article views: 89,726

    Total article downloads: 10,139

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