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Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 10840

Special Issue Editors

Dr. Rui Pinto

E-Mail Website1 Website2
Guest Editor
SYSTEC-ARISE, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
Interests: Industry 4.0; cyber–physical systems; artificial immune systems; autonomic computing; IoT
Dr. Pedro M. B. Torres

E-Mail Website
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. SYSTEC—Research Center for Systems and Technologies, ARISE—Advanced Production and Intelligent Systems Associated Laboratory, 4200-465 Porto, Portugal
Interests: electronics; instrumentation; automation; control; robotics; cyber-physical systems; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Volker Lohweg

E-Mail Website1 Website2
Guest Editor
Institute Industrial IT (inIT), Technische Hochschule Ostwestfalen-Lippe (TH OWL), Campusallee 6, D-32657 Lemgo, Germany
Interests: Intelligent automation; digitalization; information fusion; industrial image processing; pattern recognition; cyber–physical (production) systems; machine learning; resource-limited electronics; mobile devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous systems are emerging as game-changers in the realm of Cyber–Physical Systems (CPS) and Smart Industry, revolutionizing how industries operate and interact with the physical world. This Special Issue is dedicated to exploring the integration and impact of autonomous systems within the CPS framework. We invite contributions that delve into the design, development, and deployment of Self-* capabilities in CPS and industrial applications. Topics of interest include autonomous manufacturing, logistics, predictive maintenance, AI (artificial intelligence) and machine learning in industrial processes, and autonomous decision-making processes. We also welcome research on the challenges and opportunities presented by autonomous systems, such as safety, reliability, security, privacy, and ethical considerations. Join us in uncovering the transformative potential of autonomous systems in shaping the future of Smart Industry.

Dr. Rui Pinto
Dr. Pedro M. B. Torres
Prof. Dr. Volker Lohweg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber–physical systems
  • Smart Industry
  • autonomous systems
  • Self-*
  • artificial intelligence (AI)
  • machine learning
  • real-time monitoring
  • predictive maintenance
  • security and privacy in industry
  • ethical considerations in autonomous systems

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Published Papers (7 papers)

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Research

Jump to: Review

28 pages, 45195 KiB  
Article
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
Viewed by 679
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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Figure 1

Figure 1
<p>A generic architecture of the FL framework for IoT scenarios.</p> Full article ">Figure 2
<p>Scenario of heterogeneous FL in an IoT network environment. This study focuses on the heterogeneity in device specifications and the non-Independent and Identically Distributed (non-IID) nature of datasets among individual devices.</p> Full article ">Figure 3
<p>The overall workflow of UA-FedRL for adaptive epoch selection of heterogeneous industrial IoT devices.</p> Full article ">Figure 4
<p>The overall architecture of the PWA which employs weight quality measurement to compute the weighted average of all local models’ weight.</p> Full article ">Figure 5
<p>Hardware specifications of the selected IoT devices used in this study.</p> Full article ">Figure 6
<p>The illustration of the communication between server and client side on Mininet.</p> Full article ">Figure 7
<p>The accumulated rewards of the UA-FedRL for different gamma values when the learning rate was set to 0.1.</p> Full article ">Figure 8
<p>The accumulated rewards of the UA-FedRL for different gamma values when the learning rate was set to 0.5.</p> Full article ">Figure 9
<p>The accumulated rewards of the UA-FedRL for different gamma values when the learning rate was set to 0.9.</p> Full article ">Figure 10
<p>The accuracy comparison between UA-FedRL and different FL methods on the MNIST dataset.</p> Full article ">Figure 11
<p>The accuracy comparison between UA-FedRL, Fed_AVG, and Fed_Prox methods on the MNIST dataset with 90% straggler IoT devices.</p> Full article ">Figure 12
<p>The accuracy comparison between UA-FedRL and different FL methods on the CIFAR-10 dataset.</p> Full article ">Figure 13
<p>The accuracy comparison of UA-FedRL, Fed_AVG, and Fed_Prox methods with 90% straggler IoT devices on the CIFAR-10 dataset.</p> Full article ">Figure 14
<p>Comparative analysis of normalized communication cost across different federated learning methods on MNIST and CIFAR-10 datasets.</p> Full article ">Figure 15
<p>Comparative analysis of normalized energy consumption across different FL methods on MNIST and CIFAR-10 datasets.</p> Full article ">Figure 16
<p>The uncertainty estimation of the UA-FedRL taking each action in terms of reward.</p> Full article ">
23 pages, 2276 KiB  
Article
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 907
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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Figure 1

Figure 1
<p>CA-FoFS architecture.</p> Full article ">Figure 2
<p>Context Engine Architecture.</p> Full article ">Figure 3
<p>Integration and Communication with External Applications.</p> Full article ">Figure 4
<p>Class Diagram of the Experimental Data Model.</p> Full article ">Figure 5
<p>Context Acquisition Execution Example for Tests.</p> Full article ">Figure 6
<p>Search test for a worker who is not of the coordinator type.</p> Full article ">Figure 7
<p>Search test for a worker who is a coordinator.</p> Full article ">Figure 8
<p>Detecting Stoppages and Recording the Sending of Alerts in <span class="html-italic">Context Acquisition</span>.</p> Full article ">Figure 9
<p>Alerts Received Page in the Virtual Assistant [<a href="#B36-applsci-14-03907" class="html-bibr">36</a>].</p> Full article ">Figure 10
<p>Material Replacement Request functionality on the Smartwatch.</p> Full article ">Figure 11
<p>Example of <span class="html-italic">DeviceInfo</span> Service Response to an Information Request.</p> Full article ">Figure 12
<p>Component replacement order functionality.</p> Full article ">Figure 13
<p>Virtual Assistant Report creation functionality [<a href="#B36-applsci-14-03907" class="html-bibr">36</a>].</p> Full article ">
17 pages, 2191 KiB  
Article
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 2508
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Generic architecture for CPS Framework.</p> Full article ">Figure 2
<p>Block diagram of Asset Tracking Platform.</p> Full article ">Figure 3
<p>Generic overview of platform.</p> Full article ">Figure 4
<p>Detail of the list of vehicles.</p> Full article ">Figure 5
<p>Detail of a track recorded by the platform.</p> Full article ">
16 pages, 830 KiB  
Article
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
Viewed by 1072
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Communication process (1 and 2) -&gt; Industrial Sonar algorithm -&gt; decision making. This figure explains the communication process of the Industrial Sonar algorithm: (<b>a</b>) Firstly, Communication 1 is established with work centres of operation 20 to obtain an answer regarding the schedule of the tasks. (<b>b</b>) Simultaneously, Communication 2 is established with work centres of operation 30 to obtain an answer also regarding the schedule of the tasks. (<b>c</b>) With the operation schedules of each work centre obtained, the Industrial Sonar algorithm is applied and a decision is made, selecting and confirming one specific work centre.</p> Full article ">Figure 2
<p>Energy losses calculated for the item and work centre. This figure illustrates an example of the energy losses calculated for one item and work centre, with all the different terms of the calculation detailed: the energy losses based on pieces, energy losses based on OEE (overall equipment effectiveness) and the carbon footprint.</p> Full article ">Figure 3
<p>Historical data on energy loss calculations for the items in the work centre. This figure presents an example of historical data registered on energy loss calculations for the items in the work centre. These historical data are the input for an algorithm to schedule the production operations by energy losses.</p> Full article ">Figure 4
<p>Sequence of production tasks ordered according to requested criteria of energy losses. This figure displays an example of the sequence of production tasks returned by a work centre for the next operation, ordered according to the requested criteria of SPT + energy losses based on OEE). These data are the result of Communication 1.</p> Full article ">Figure 5
<p>Production tasks ordered based on criteria (OEE + transportation time). This figure displays an example of the sequence of production tasks returned by a work centre for the operation following the current operation (the next operation) with a specific order based on the requested criteria (SPT-OEE + transportation time). These data are the result of Communication 2.</p> Full article ">Figure 6
<p>Outcome of the Industrial Sonar algorithm using ’Sonar Industrial’ software developed in Python. This figure displays the outcome of applying the Industrial Sonar algorithm using ’Sonar Industrial’ software developed in Python to test the hypothesis of this research. This is a result of applying the Industrial Sonar algorithm with the data returned from the communications with the work centres in <a href="#applsci-14-01890-f004" class="html-fig">Figure 4</a> and <a href="#applsci-14-01890-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>Graph of the application of the Johnson’s algorithm to optimise Cmax. This figure shows the relationship between Cmax and energy loss objectives in the work centre for all the operations scheduled applying Johnson’s algorithm. In this case, in the application of Johnson’s algorithm, only optimisation of Cmax values is performed.</p> Full article ">Figure 8
<p>Graph of the application of the algorithm to reduce and optimise energy losses. This figure shows the relationship between Cmax and energy loss objectives in the work centre for all the operations scheduled, applying an algorithm to reduce and optimise energy losses.</p> Full article ">Figure 9
<p>Graph of the application of the Industrial Sonar algorithm to reduce and optimise Cmax and energy losses. This figure shows the relationship between Cmax and energy loss objectives in the work centre for all the operations scheduled, applying an Industrial Sonar algorithm to reduce and optimise the Cmax, energy loss and transport time.</p> Full article ">

Review

Jump to: Research

50 pages, 3528 KiB  
Review
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 1046
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Space–time diagram. Blach lines – trajectories of all vehicles, blue line – an average speed of the vehicle, red line—current speed of the vehicle, grey-blue bands—a place and time for speed measurement, <span class="html-italic">x<sub>r</sub></span>—reference point, <span class="html-italic">t<sub>r</sub></span>—reference time, <span class="html-italic">u<sub>a</sub></span>—average vehicle speed, <span class="html-italic">h</span>—vehicle headway, <span class="html-italic">s</span>—vehicle spacing.</p> Full article ">Figure 2
<p>Linear density–speed (<b>a</b>) and parabolic density–flow (<b>b</b>) relations.</p> Full article ">Figure 3
<p>Drake’s fundamental diagram.</p> Full article ">Figure 4
<p>Smulders’ fundamental diagram.</p> Full article ">Figure 5
<p>Daganzo’s fundamental diagram.</p> Full article ">Figure 6
<p>Fundamental diagram with capacity drop.</p> Full article ">Figure 7
<p>Fundamental diagram with clockwise hysteresis.</p> Full article ">Figure 8
<p>Three traffic phases: F—free flow; S—synchronized flow; J—wide moving jams.</p> Full article ">Figure 9
<p>Solution for a merge node with two inlets. Areas: 1—free flow (green), 2 and 3—congestion in the output road and in one of the input roads, 4—congestion at the outlet and both inlets (rose).</p> Full article ">Figure 10
<p>Solution for diverged node with two outlets: (<b>a</b>) input capacity less than the sum of the output capacities; (<b>b</b>) input capacity more than the sum of the output capacities. Areas: 1—free flow (green), 2 and 3—congestion at the inlet and at one of the outlets (rose), 4—congestion at the inlet and both outlets (red).</p> Full article ">
22 pages, 2223 KiB  
Review
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
Viewed by 1023
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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<p>The various levels of cooperation between a human worker and a robot.</p> Full article ">Figure 2
<p>Classical RL loop [<a href="#B15-applsci-14-06345" class="html-bibr">15</a>].</p> Full article ">Figure 3
<p>Percentage increase in publications across topic clusters over time (2012–2023).</p> Full article ">Figure 4
<p>The five different brain waves: Delta, theta, alpha, beta, and gamma.</p> Full article ">Figure 5
<p>Real experimentation using EEG for an assembly task [<a href="#B33-applsci-14-06345" class="html-bibr">33</a>].</p> Full article ">Figure 6
<p>Different bio-sensors and their positions in the human body [<a href="#B51-applsci-14-06345" class="html-bibr">51</a>].</p> Full article ">Figure 7
<p>Conceptual diagram. Intersection between topics displayed in triplets for future research.</p> Full article ">
19 pages, 2010 KiB  
Review
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 2489
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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<p>Integration of automation, robotics, and edge computing in environmental sensing.</p> Full article ">Figure 2
<p>Transfer learning workflow [<a href="#B38-applsci-14-03531" class="html-bibr">38</a>].</p> Full article ">Figure 3
<p>Deep learning workflow [<a href="#B60-applsci-14-03531" class="html-bibr">60</a>].</p> Full article ">Figure 4
<p>Deep learning applications in environment sensing.</p> Full article ">Figure 5
<p>Edge computing workflow [<a href="#B97-applsci-14-03531" class="html-bibr">97</a>].</p> Full article ">
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