Applied Sciences

Editorial

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6 pages, 9114 KiB

Open AccessEditorial

Closing Editorial: Advances and Future Directions in Autonomous Systems for Cyber-Physical Systems and Smart Industry

by Rui Pinto, Pedro M. B. Torres and Volker Lohweg

Appl. Sci. 2024, 14(22), 10673; https://doi.org/10.3390/app142210673 - 19 Nov 2024

Viewed by 399

Abstract

The rapid evolution of autonomous systems and their integration into cyber–physical Systems (CPS) and the Industrial Internet of Things (IIoT) has been a critical driver of the fourth industrial revolution, also known as Industry 4 [...] Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

Research

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28 pages, 45195 KiB

Open AccessArticle

Uncertainty-Aware Federated Reinforcement Learning for Optimizing Accuracy and Energy in Heterogeneous Industrial IoT

by A. S. M. Sharifuzzaman Sagar, Muhammad Zubair Islam, Amir Haider and Hyung-Seok Kim

Appl. Sci. 2024, 14(18), 8299; https://doi.org/10.3390/app14188299 - 14 Sep 2024

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Abstract

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent [...] Read more.

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent and Identically Distributed (non-IID) datasets and straggler IoT devices, ensuring computation and communication efficiency, and addressing weight aggregation issues. In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients to effectively manage heterogeneous industrial IoT devices and improve accuracy, computation, and communication efficiency. Additionally, we introduce the Predictive Weighted Average Aggregation (PWA) method to tackle weight aggregation issues in heterogeneous industrial IoT scenarios by adjusting the weights of individual models based on their quality. The UA-FedRL addresses the inherent complexities and challenges of implementing FL in heterogeneous industrial IoT environments. Extensive simulations in complex IoT environments demonstrate the superior performance of UA-FedRL on both MNIST and CIFAR-10 datasets compared to other existing approaches in terms of accuracy, communication efficiency, and computation efficiency. The UA-FedRL algorithm attain an accuracy of 96.83% on the MNIST dataset and 62.75% on the CIFAR-10 dataset, despite the presence of 90% straggler IoT devices, attesting to its robust performance and adaptability in different datasets. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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23 pages, 2276 KiB

Open AccessArticle

Context-Aware System for Information Flow Management in Factories of the Future

by Pedro Monteiro, Rodrigo Pereira, Ricardo Nunes, Arsénio Reis and Tiago Pinto

Appl. Sci. 2024, 14(9), 3907; https://doi.org/10.3390/app14093907 - 3 May 2024

Cited by 1 | Viewed by 1014

Abstract

The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized [...] Read more.

The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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17 pages, 2191 KiB

Open AccessArticle

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

by Carlos Serôdio, Pedro Mestre, Jorge Cabral, Monica Gomes and Frederico Branco

Appl. Sci. 2024, 14(5), 2160; https://doi.org/10.3390/app14052160 - 5 Mar 2024

Cited by 3 | Viewed by 3038

Abstract

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral [...] Read more.

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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16 pages, 830 KiB

Open AccessArticle

Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots

by Francisco Burillo, María-Pilar Lambán, Jesús-Antonio Royo, Paula Morella and Juan-Carlos Sánchez

Appl. Sci. 2024, 14(5), 1890; https://doi.org/10.3390/app14051890 - 25 Feb 2024

Cited by 1 | Viewed by 1228

Abstract

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are [...] Read more.

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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Review

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50 pages, 3528 KiB

Open AccessReview

Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles

by Łukasz Łach and Dmytro Svyetlichnyy

Appl. Sci. 2024, 14(18), 8456; https://doi.org/10.3390/app14188456 - 19 Sep 2024

Viewed by 1794

Abstract

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review [...] Read more.

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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22 pages, 2223 KiB

Open AccessReview

Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

by Asier Gonzalez-Santocildes, Juan-Ignacio Vazquez and Andoni Eguiluz

Appl. Sci. 2024, 14(14), 6345; https://doi.org/10.3390/app14146345 - 20 Jul 2024

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Abstract

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline [...] Read more.

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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19 pages, 2010 KiB

Open AccessReview

Emerging Technologies for Automation in Environmental Sensing: Review

by Shekhar Suman Borah, Aaditya Khanal and Prabha Sundaravadivel

Appl. Sci. 2024, 14(8), 3531; https://doi.org/10.3390/app14083531 - 22 Apr 2024

Cited by 1 | Viewed by 3354

Abstract

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements [...] Read more.

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

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