Application of IoT on Manufacturing, Communication and Engineering

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Coatings coatings	2.9	5.0	2011	14.5 Days	CHF 2600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Symmetry symmetry	2.2	5.4	2009	17.3 Days	CHF 2400	Submit
Applied System Innovation asi	3.8	7.9	2018	31.4 Days	CHF 1400	Submit

25 pages, 5404 KiB

Open AccessArticle

STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent

by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju and Rajagopal Maheswar

Sensors 2025, 25(6), 1852; https://doi.org/10.3390/s25061852 - 17 Mar 2025

Viewed by 398

Abstract

The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them [...] Read more.

The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. The STID-Net is also experimented with the MBGD and SGD optimizers, and we are satisfied with the performance of the SGD optimizer in both the IoMT and IIoT datasets. The SGD optimized model provides a faster convergence and better weight adjustments for handling noisy datasets, making it robust and scalable for diverse IoT applications. This experimental work demonstrates an accuracy of 97.14% and 97.85% with the MBGD optimizer in the IoMT and IIoT datasets, while it attained 98.58% and 99.15% with SGD optimization, respectively. The proposed methodology also outperforms the standalone CNN and LSTM models incorporated with both optimizers, and the result indicates the robustness and scalability of STID-Net in medical and industrial applications. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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14 pages, 7666 KiB

Open AccessArticle

Analysis of the Influence of Patch Antenna Shapes for Wireless Passive Temperature Sensor Applications

by Trisa Azahra, Ying-Ting Liao, Yi-Chien Chen and Cheng-Chien Kuo

Appl. Sci. 2025, 15(6), 3136; https://doi.org/10.3390/app15063136 - 13 Mar 2025

Viewed by 433

Abstract

Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, [...] Read more.

Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, which relies on the wireless interaction between the interrogator antenna and the sensor antenna’s resonant frequency, implemented in the far-field region to support long communication distances. To evaluate the impact of antenna shape on sensor performance, three microstrip patch antenna shapes—rectangular, circular, and equilateral triangular—were designed to operate in the fundamental mode at 2.4 GHz. These designs were simulated using HFSS in Ansys Electromagnetic Suite^® 2023 R1 (Ansys Inc., Canonsburg, PA, USA), fabricated on alumina substrates, and assessed for performance metrics, including communication distance and sensitivity. Results indicated that the equilateral triangular patch outperformed the others, achieving a maximum communication distance of 16.5 cm, a sensitivity of 0.129 MHz/°C over a temperature range of 25 °C to 500 °C, and a simulated gain of 5.84 dBi. These findings underscore the importance of antenna shape selection and optimization for robust, wireless temperature sensing in demanding environments. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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23 pages, 5227 KiB

Open AccessArticle

Lightweight Leather Surface Defect Inspection Model Design for Fast Classification and Segmentation

by Chin-Feng Lee, Yu-Chuan Chen, Jau-Ji Shen and Anis Ur Rehman

Symmetry 2025, 17(3), 358; https://doi.org/10.3390/sym17030358 - 26 Feb 2025

Viewed by 423

Abstract

Automated inspection of leather surface defects is critical in evaluating product quality, yet manual inspection is still time-consuming and error-prone. Conventional automated methods, on the other hand, exhibit high computational complexities, are rigid in dealing with varied defects, and often require extensive manual [...] Read more.

Automated inspection of leather surface defects is critical in evaluating product quality, yet manual inspection is still time-consuming and error-prone. Conventional automated methods, on the other hand, exhibit high computational complexities, are rigid in dealing with varied defects, and often require extensive manual parameter tuning. To counter these challenges, we propose a lightweight model integrated with symmetry for efficient defect classification and segmentation. The model consists of a streamlined semantic segmentation network that uses depthwise separable convolution and symmetric padding to preserve edge features while eliminating deconvolution layers, thus considerably reducing computational overhead. Moreover, a discrimination network automates defect detection without requiring manual thresholds, and a segmentation suggestion stage refines defect masks for practical cutting recommendations. Experimental results demonstrate a 96.75% detection accuracy and 89.41% mean pixel accuracy, achieving performance comparable to state-of-the-art models (e.g., KMDNet, U-Net) while reducing training time by 40% and model size by 60%. The symmetry-driven architecture enhances computational efficiency (0.05 s/img) and robustness across multiple defect types. Furthermore, the modular design enables independent updates for new defect types without requiring full retraining, addressing a major limitation of prior methods. These findings highlight the potential of symmetry-based architectures in industrial quality control, offering a scalable and efficient solution for automated defect detection. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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16 pages, 7035 KiB

Open AccessArticle

An Explainable Scheme for Memorization of Noisy Instances by Downstream Evaluation

by Chun-Yi Tsai, Ping-Hsun Tsai and Yu-Wei Chung

Appl. Sci. 2025, 15(5), 2392; https://doi.org/10.3390/app15052392 - 24 Feb 2025

Viewed by 367

Abstract

Deep learning models are often perceived as black boxes, making it challenging to analyze the causal relationships between inputs and outputs. For this reason, the explainability of model learning has garnered increasing attention in recent years. Some previous studies proposed influence functions, which [...] Read more.

Deep learning models are often perceived as black boxes, making it challenging to analyze the causal relationships between inputs and outputs. For this reason, the explainability of model learning has garnered increasing attention in recent years. Some previous studies proposed influence functions, which evaluate how the weighting of data impacts the model by mathematical analysis, thereby explaining how it realizes the data. This inspires us to suggest that when data in an upstream task is affected by varying levels of noise interference, it is practical to set up a downstream model to apply Taylor expansion in conjunction with the Hessian matrix to estimate perturbations that each data point cause in the model. Additionally, utilizing Integrated Gradients to compute the loss difference between the original data instances and a baseline instance which does not affect the model is powerful to yield a memorization matrix that allows researchers to observe the changes in model reasoning before and after noise interference, helping to analyze the causes of erroneous inference. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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22 pages, 4392 KiB

Open AccessArticle

Verification of a Probabilistic Model and Optimization in Long-Range Networks

by José Luis Romero Vázquez, Abel García-Barrientos, José Alberto Del-Puerto-Flores, Francisco R. Castillo Soria, Roilhi F. Ibarra-Hernández, Ulises Pineda Rico and Ernesto Zambrano-Serrano

Appl. Sci. 2025, 15(4), 1873; https://doi.org/10.3390/app15041873 - 11 Feb 2025

Viewed by 966

Abstract

This paper presents a comprehensive probabilistic analysis of packet loss in long-range (LoRa) networks, a vital aspect of low-power, wide-area communication systems increasingly employed in IoT applications. The proposed model integrates multiple critical factors, including packet arrival rates, transmission power levels, and the [...] Read more.

This paper presents a comprehensive probabilistic analysis of packet loss in long-range (LoRa) networks, a vital aspect of low-power, wide-area communication systems increasingly employed in IoT applications. The proposed model integrates multiple critical factors, including packet arrival rates, transmission power levels, and the distance between transmitting nodes and the gateway. By incorporating these variables into a unified probabilistic framework, the model not only predicts packet loss and interference patterns but also provides insights into optimizing network parameters. Specifically, it focuses on determining the optimal transmission power required to balance energy efficiency and communication reliability. A distinctive feature of the analysis is its ability to adapt dynamically to varying network conditions, ensuring sustained performance even in environments with high node density or fluctuating traffic loads. The study also explores the interplay between transmission power and interference, demonstrating how careful calibration of power settings can significantly reduce packet collisions while conserving energy resources. The proposed framework not only advances theoretical understanding, but also offers actionable guidelines for network designers seeking to achieve high performance in resource-constrained environments. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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15 pages, 2511 KiB

Open AccessArticle

Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces

by Pin-Chia Huang, I-Cheng Li, Ching-Yi Wang, Cheng-Hsiung Shih, Masimukku Srinivaas, Wan-Ting Yang, Chin-Fang Kao and Te-Jen Su

Appl. Sci. 2025, 15(2), 562; https://doi.org/10.3390/app15020562 - 8 Jan 2025

Viewed by 3059

Abstract

This study aims to explore the application of artificial intelligence (AI) technology in the preservation and exhibition of artworks, with the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System”, developed by Cheng Shiu University, as case studies. In [...] Read more.

This study aims to explore the application of artificial intelligence (AI) technology in the preservation and exhibition of artworks, with the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System”, developed by Cheng Shiu University, as case studies. In recent years, AI technology has made significant advancements in image recognition, machine learning, and data analysis, which provide new opportunities for art management. However, due to high costs and implementation challenges, as well as a lack of qualified personnel to use these tools and systems, small art galleries and museums have not yet had the opportunity to acquire such systems. Therefore, this study observes the practical application of the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System” in the fields of art preservation and exhibition. The study employs case study and observation methods, with participatory observation as the primary data collection approach. The results indicate that AI technology significantly enhances the preservation conditions of artworks and the interactivity of exhibitions. The paper suggests that future efforts should focus on long-term planning relating to technology costs and professional talent development to fully realize the potential of AI in art management and exhibition. Additionally, the application of these technologies can be extended to other fields. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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10 pages, 2034 KiB

Open AccessArticle

A Study on the Development of Real-Time Chamber Contamination Diagnosis Sensors

by Junyeob Lee and Kyongnam Kim

Sensors 2025, 25(1), 20; https://doi.org/10.3390/s25010020 - 24 Dec 2024

Viewed by 742

Abstract

Plasma processes are critical for achieving precise device fabrication in semiconductor manufacturing. However, polymer accumulation during processes like plasma etching can cause chamber contamination, adversely affecting plasma characteristics and process stability. This study focused on developing a real-time sensor system for diagnosing chamber [...] Read more.

Plasma processes are critical for achieving precise device fabrication in semiconductor manufacturing. However, polymer accumulation during processes like plasma etching can cause chamber contamination, adversely affecting plasma characteristics and process stability. This study focused on developing a real-time sensor system for diagnosing chamber contamination by quantitatively monitoring polymer accumulation. A quartz crystal sensor integrated with flexible printed circuit boards was designed to measure the frequency shifts corresponding to polymer thickness changes. An impedance probe was also employed to monitor variations in the plasma discharge characteristics. The sensor demonstrated high reliability with a measurement scatter of 2.5% despite repeated plasma exposure. The experimental results revealed that polymer accumulation significantly influenced the plasma impedance, and this correlation was validated through real-time monitoring and scanning electron microscopy (SEM). The study further showed that the sensor could detect the transition point of the plasma state changes under varying process gas conditions, enabling the early detection of potential process anomalies. These findings suggest that the developed sensor system can be crucial for diagnosing plasma and chamber conditions, providing valuable data for optimizing preventive maintenance schedules. This advancement offers a pathway for improving process reliability and extending the operational lifetime of semiconductor manufacturing equipment. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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11 pages, 4454 KiB

Open AccessArticle

Effect of Tungsten Doping on the Properties of Titanium Dioxide Dye-Sensitized Solar Cells

by Ming-Cheng Kao, Jun-Hong Weng, Chih-Hung Chiang, Kai-Huang Chen, Der-Yuh Lin and Tsung-Kuei Kang

Energies 2024, 17(20), 5118; https://doi.org/10.3390/en17205118 - 15 Oct 2024

Viewed by 1107

Abstract

Tungsten-doped TiO₂ thin films were prepared by sol–gel method on fluorine-doped tin oxide-coated substrates as working electrodes of dye-sensitized solar cells. The influences of different W doping (0, 2, 4, 6, and 8 at%) on the microstructure, optical, and photovoltaic properties of [...] Read more.

Tungsten-doped TiO₂ thin films were prepared by sol–gel method on fluorine-doped tin oxide-coated substrates as working electrodes of dye-sensitized solar cells. The influences of different W doping (0, 2, 4, 6, and 8 at%) on the microstructure, optical, and photovoltaic properties of the W-TiO₂ thin-film DSSCs were studied by the measurement of X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) analysis, and electrochemical impedance spectroscopy (EIS). An optimal DSSCs performance was observed with a 6 at% W-doped TiO₂ thin film, resulting in a Voc of 0.68 V, a Jsc of 20.2 mA/cm², an FF of 68.6%, and an efficiency (η) of 9.42%. The efficiency of DSSCs with 6 at% W-doped TiO₂ photoanode improved by 75%. This is because the 6 at% W-doped TiO₂ thin film increases the specific surface area and electron transfer rate. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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23 pages, 4249 KiB

Open AccessArticle

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

by Lei Yang, Yibo Jiang, Kang Zeng and Tao Peng

Sensors 2024, 24(10), 2992; https://doi.org/10.3390/s24102992 - 8 May 2024

Cited by 3 | Viewed by 1918

Abstract

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, [...] Read more.

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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11 pages, 5282 KiB

Open AccessArticle

Effect of Photoanode Process Sequence on Efficiency of Dye-Sensitized Solar Cells

by Tian-Chiuan Wu, Wei-Ming Huang, Jenn-Kai Tsai, Cheng-En Chang and Teen-Hang Meen

Coatings 2024, 14(3), 304; https://doi.org/10.3390/coatings14030304 - 29 Feb 2024

Cited by 1 | Viewed by 1749

Abstract

Owing to its contribution to carbon emission reduction, green energy has received widespread attention. Among green energy sources, solar energy is regarded as the most important. In solar energy production, dye-sensitized solar cells (DSSCs) have been favored owing to their characteristics of simple [...] Read more.

Owing to its contribution to carbon emission reduction, green energy has received widespread attention. Among green energy sources, solar energy is regarded as the most important. In solar energy production, dye-sensitized solar cells (DSSCs) have been favored owing to their characteristics of simple manufacturing and high efficiency as a third-generation solar cell technology. DSSCs are prospective candidates for powering indoor Internet of Things (IoT) devices. In this study, to find a method to enhance DSSCs’ efficiency, the advantages and disadvantages of the screen printing method and the mechanical pressing and annealing method were analyzed. Using an improved method, a TiO₂ photoanode was processed and annealed, and the DSSCs with the photoanode showed an efficiency increase from 1.10 to 4.78%. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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36 pages, 2639 KiB

Open AccessReview

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

by Raihan Uddin and Insoo Koo

Appl. Sci. 2024, 14(5), 1876; https://doi.org/10.3390/app14051876 - 25 Feb 2024

Cited by 19 | Viewed by 16255

Abstract

This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple [...] Read more.

This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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21 pages, 9944 KiB

Open AccessArticle

Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation

by Eugene Kim, Myeonghwan Hwang, Taeyoon Lim, Chanyeong Jeong, Seungha Yoon and Hyunrok Cha

Sensors 2024, 24(4), 1241; https://doi.org/10.3390/s24041241 - 15 Feb 2024

Cited by 2 | Viewed by 1464

Abstract

There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement [...] Read more.

There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement the uncertain behavior of autonomous vehicles, teleoperation of the vehicle has been introduced to deal with the disengagement of autonomous driving. However, teleoperation can lead the vehicle to unforeseen and hazardous situations from the viewpoint of wireless communication stability. In particular, communication delay outliers that severely deviate from the passive communication delay should be highlighted because they could hamper the cognition of the circumstances monitored by the teleoperator, or the control signal could be contaminated regardless of the teleoperator’s intention. In this study, communication delay outliers were detected and classified based on the stochastic approach (passive delays and outliers were estimated as 98.67% and 1.33%, respectively). Results indicate that communication delay outliers can be automatically detected, independently of the real-time quality of wireless communication stability. Moreover, the proposed framework demonstrates resilience against outliers, thereby mitigating potential performance degradation. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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13 pages, 3978 KiB

Open AccessArticle

An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture

by Rita Wiryasaputra, Chin-Yin Huang, Yu-Ju Lin and Chao-Tung Yang

Sensors 2024, 24(4), 1180; https://doi.org/10.3390/s24041180 - 11 Feb 2024

Cited by 17 | Viewed by 6586

Abstract

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may [...] Read more.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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18 pages, 10065 KiB

Open AccessArticle

Chatter Stability Prediction for Deep-Cavity Turning of a Bent-Blade Cutter

by Xiaojuan Wang, Qinghua Song and Zhanqiang Liu

Sensors 2024, 24(2), 606; https://doi.org/10.3390/s24020606 - 18 Jan 2024

Viewed by 1143

Abstract

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the [...] Read more.

The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending–torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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20 pages, 10946 KiB

Open AccessArticle

The Implementation of a Gesture Recognition System with a Millimeter Wave and Thermal Imager

by Yi-Lin Cheng, Wen-Hsiang Yeh and Yu-Ping Liao

Sensors 2024, 24(2), 581; https://doi.org/10.3390/s24020581 - 17 Jan 2024

Cited by 1 | Viewed by 2034

Abstract

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method [...] Read more.

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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12 pages, 4793 KiB

Open AccessArticle

Research on Low-Density Parity-Check Decoding Algorithm for Reliable Transmission in Satellite Communications

by Yuanzhi He and Yaoling Wang

Appl. Sci. 2024, 14(2), 746; https://doi.org/10.3390/app14020746 - 15 Jan 2024

Cited by 2 | Viewed by 2023

Abstract

Satellite communications face difficulties such as intensified environmental attenuation, dynamic time-varying links, and diverse business scenarios, which usually require channel coding schemes with high coding gain and high throughput. Low-density parity-check (LDPC) codes are dominant in satellite communication coding schemes due to their [...] Read more.

Satellite communications face difficulties such as intensified environmental attenuation, dynamic time-varying links, and diverse business scenarios, which usually require channel coding schemes with high coding gain and high throughput. Low-density parity-check (LDPC) codes are dominant in satellite communication coding schemes due to their excellent performance in approaching the Shannon limit and the characteristics of parallel computing. The traditional weighted-Algorithm B decoding algorithm ignores the channel received information and involves frequent multiplication operations and iteration, which introduces the channel received information for hard-decision and constellation mapping processing. Meanwhile, we design the correlated reliability between the extrinsic information and the mapping processing information to improve the correctness of decoding. The multiplication operation in the iterative process can be replaced by the simple sum of the Hamming distance coefficient, the correlated reliability between the extrinsic information and the mapping processing information, and the extrinsic information frequency, thereby reducing the complexity and storage load of the system. The simulation results show that the presented MRAI-LDPC algorithm can obtain about 0.4 dB performance gain, and the average number of iterations is reduced by 68% under a low SNR. The algorithm can achieve better error-correcting performance and higher throughput, providing strong support for reliable transmission of satellite communications. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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13 pages, 5625 KiB

Open AccessArticle

Investigation of Laser Ablation Quality Based on Data Science and Machine Learning XGBoost Classifier

by Chien-Chung Tsai and Tung-Hon Yiu

Appl. Sci. 2024, 14(1), 326; https://doi.org/10.3390/app14010326 - 29 Dec 2023

Cited by 4 | Viewed by 1964

Abstract

This work proposes a matching data science approach for the laser ablation quality, r_eb, the study of Si₃N₄ film based on supervised machine learning classifiers in the CMOS-MEMS process. The study demonstrates that there exists an energy threshold, [...] Read more.

This work proposes a matching data science approach for the laser ablation quality, r_eb, the study of Si₃N₄ film based on supervised machine learning classifiers in the CMOS-MEMS process. The study demonstrates that there exists an energy threshold, E_th, for laser ablation. If the laser energy surpasses this threshold, increasing the interval time will not contribute significantly to the recovery of pulse laser energy. Thus, r_eb enhancement is limited. When the energy is greater than 0.258 mJ, there exists a critical value of interval time at which the r_eb value is relatively low for each energy level, respectively. In addition, the variation of r_eb, Δr_eb, is independent of the interval time at the invariant point of energy between 0.32 mJ and 0.36 mJ. Energy and interval time exhibit a Pearson correlation of 0.82 and 0.53 with r_eb, respectively. To maintain Δr_eb below 0.15, green laser ablation of Si₃N₄ at operating energies of 0.258–0.378 mJ can adopt a baseline interval time of the initial baseline multiplied by 1/∜2. Additionally, for operating energies of 0.288–0.378 mJ during Si₃N₄ laser ablation, Δr_eb can be kept below 0.1. With the forced partition methods, namely, the k-means method and percentile method, the XGBoost (v 2.0.3) classifier maintains a competitive accuracy across test sizes of 0.20–0.40, outperforming the machine learning algorithms Random Forest and Logistic Regression, with the highest accuracy of 0.78 at a test size of 0.20. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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14 pages, 6939 KiB

Open AccessArticle

Ionic Storage Materials for Anodic Discoloration in Electrochromic Devices

by Po-Wen Chen, Chen-Te Chang and Po-Hsiu Kuo

Energies 2023, 16(24), 8119; https://doi.org/10.3390/en16248119 - 17 Dec 2023

Cited by 1 | Viewed by 1907

Abstract

The ion storage layer in electrochromic devices (ECDs) stores protons or lithium ions to provide electrochemical stability and extend cycle durability. This paper reports on the performance and stability of ECDs paired with various ion storage layers (NiO, V₂O₅, [...] Read more.

The ion storage layer in electrochromic devices (ECDs) stores protons or lithium ions to provide electrochemical stability and extend cycle durability. This paper reports on the performance and stability of ECDs paired with various ion storage layers (NiO, V₂O₅, and IrO₂ films). The complementary ECD using a V₂O₅ ion storage layer presented the fastest response time, but the lowest optical contrast. In addition, the ECD using an IrO₂ ion storage layer proved the most effective as an ion storage layer, due to its high optical modulation ability capability and long-term stability. Chronoamperometry analysis revealed that IrO₂-based ECD (glass/IZTO/WO₃/liquid electrolyte/IrO₂/IZTO/glass) can be highly effective in modulating optical transmittance, as indicated by T = 61.5% (from T_bleaching (69.6%) to T_coloring (8.1%)) and switching times of 5.3 s for coloring and 7.3 s for bleaching at 633 nm. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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23 pages, 7499 KiB

Open AccessFeature PaperArticle

Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries

by Shun-Chung Wang and Zhi-Yao Zhang

Energies 2023, 16(22), 7641; https://doi.org/10.3390/en16227641 - 17 Nov 2023

Cited by 5 | Viewed by 1654

Abstract

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. [...] Read more.

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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16 pages, 9569 KiB

Open AccessArticle

Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

by Ming-You Ma, Shang-En Shen and Yi-Cheng Huang

Sensors 2023, 23(21), 8999; https://doi.org/10.3390/s23218999 - 6 Nov 2023

Cited by 13 | Viewed by 3035

Abstract

A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding [...] Read more.

A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding the emergent landing platform and for future reconnaissance area missions. This paper proposes an approach to enhance the visual capabilities of this system by using the You Only Look Once (YOLO)-based object detection (OD) with Tensor RT^TM acceleration technique, an automated visual tracking gimbal camera control system, and multithread programing for image transmission to the ground station. With lightweight edge computing (EC), the mean average precision (mAP) was satisfied and we achieved a higher frame per second (FPS) rate via YOLO accelerated with TensorRT for an onboard UAV. The OD compares four YOLO models to recognize objects of interest for landing spots at the home university first. Then, the trained dataset with YOLOv4-tiny was successfully applied to another field with a distance of more than 100 km. The system’s capability to accurately recognize a different landing point in new and unknown environments is demonstrated successfully. The proposed approach substantially reduces the data transmission and processing time to ground stations with automated visual tracking gimbal control, and results in rapid OD and the feasibility of using NVIDIA Jetson^TM Xavier NX by deploying YOLOs with more than 35 FPS for the UAV. The enhanced visual landing and future reconnaissance mission capabilities of real-time UAVs were demonstrated. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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14 pages, 4034 KiB

Open AccessArticle

Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS

by Eric Wai and C. K. M. Lee

Appl. Sci. 2023, 13(21), 12008; https://doi.org/10.3390/app132112008 - 3 Nov 2023

Cited by 9 | Viewed by 4211

Abstract

The increased connectivity and automation capabilities of Industry 4.0 cyber-physical production systems (CPPS) create significant cyber-security vulnerabilities in supervisory control and data acquisition (SCADA) environments if robust protections are not properly implemented. Legacy industrial control systems and new IP-enabled sensors, instruments, controllers, and [...] Read more.

The increased connectivity and automation capabilities of Industry 4.0 cyber-physical production systems (CPPS) create significant cyber-security vulnerabilities in supervisory control and data acquisition (SCADA) environments if robust protections are not properly implemented. Legacy industrial control systems and new IP-enabled sensors, instruments, controllers, and appliances often lack basic safeguards like encryption, rigorous access controls, and endpoint security. This exposes manufacturers to substantial risks of cyberattacks that could manipulate, disrupt, or disable critical physical assets and processes related to their production lines and facilities. This study presents a multilayered cybersecurity framework to address these challenges and harden SCADA environments by implementing granular access controls, network micro-segmentation, anomaly detection, encrypted communications, and legacy system upgrades. The multilayered defense-in-depth (DID) approach combines policies, processes, and technologies to counter emerging vulnerabilities. The methodology was implemented in an electronics manufacturing facility across access control, zoning, monitoring, and encryption scenarios. Results show security improvements, including 57.4% fewer unauthorized access events, 41.2% faster threat containment, and 79.2% fewer hacking attempts. The quantified metrics highlight the CPPS resilience and threat mitigation capabilities enabled by the securely designed SCADA architecture, which allows manufacturers to confidently pursue Industry 4.0 integration and digital transformation with minimized disruption. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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29 pages, 2761 KiB

Open AccessReview

A Systematic Mapping: Exploring Internet of Everything Technologies and Innovations

by Fazlina Mohd Ali, Nur Arzilawati Md Yunus, Nur Nabila Mohamed, Marizuana Mat Daud and Elankovan A. Sundararajan

Symmetry 2023, 15(11), 1964; https://doi.org/10.3390/sym15111964 - 24 Oct 2023

Cited by 5 | Viewed by 3052

Abstract

The Internet of Everything (IoE) represents a paradigm shift in the world of connectivity. While the Internet of Things (IoT) initiated the era of interconnected devices, the IoE takes this concept to new heights by interlinking objects, individuals, data, and processes. Symmetry in [...] Read more.

The Internet of Everything (IoE) represents a paradigm shift in the world of connectivity. While the Internet of Things (IoT) initiated the era of interconnected devices, the IoE takes this concept to new heights by interlinking objects, individuals, data, and processes. Symmetry in IoE innovation and technology is essential for creating a harmonious and efficient ecosystem to ensure that the benefits are accessible to a broad spectrum of society while minimizing potential drawbacks. This comprehensive review paper explores the multifaceted landscape of the IoE, delving into its core concepts, enabling technologies, real-world applications, and the intricate web of challenges it presents. A focal point of this review is the diverse array of real-world applications spanning healthcare, smart cities, industry 4.0, agriculture, and sustainability. Previous works and examples illustrate how the IoE reshapes these domains, leading to greater efficiency, sustainability, and improved decision making. However, the transformative power of the IoE is accompanied by a host of challenges, including security and privacy concerns, interoperability issues, and the ethical implications of ubiquitous connectivity. These challenges are dissected in order to comprehensively understand the obstacles and potential solutions in the IoE landscape. As we stand on the cusp of an IoE-driven future, this review paper serves as a valuable resource for researchers, policy makers, and industry professionals seeking to navigate the complexities of this emerging paradigm. By illuminating the intricacies of the IoE, this review fosters a deeper appreciation for the transformative potential and the multifaceted challenges that lie ahead in the Internet of Everything era. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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13 pages, 833 KiB

Open AccessArticle

Development of an Industrial Safety System Based on Voice Assistant

by Jaime Paúl Ayala Taco, Oswaldo Alexander Ibarra Jácome, Jaime Luciano Ayala Pico and Brian Andrés López Castro

Appl. Sci. 2023, 13(21), 11624; https://doi.org/10.3390/app132111624 - 24 Oct 2023

Cited by 1 | Viewed by 1551

Abstract

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users’ cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based [...] Read more.

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users’ cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based on a voice assistant, Alexa, which promotes more natural, intuitive, direct, and understandable communication. The purpose of this work is the development of an industrial safety system for a controlled electric motor based on Alexa voice assistant, which allows the monitoring of its operating parameters, such as phase current, housing temperature, and rotor vibration, as well as making it possible to control ignition and shut down and change the rotation of the motor with a prior password, as a safety measure. Commercial smart devices and Arduino-compatible modules were used to achieve this, providing them with the Internet of Things (IoT) feature. In addition, several software platforms, such as Blynk, Tuya Smart, Node Red, and Voiceflow, are used to perform data transmission, device management, and programming of the Alexa skill, oriented to the execution of the security and run system. This shows the potential capacity of voice assistants in the industry to deliver information more naturally to humans and obtain optimal notifications. However, problems were evidenced, such as the influence of noise in the environment when communicating with the assistant, the vocalization of words, low voice tones, and accents typical of the language, that will increase the security level of the system and prevent potential identity theft. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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15 pages, 7893 KiB

Open AccessArticle

Technical Solution for Monitoring Climatically Active Gases Using the Turbulent Pulsation Method

by Ekaterina Kulakova and Elena Muravyova

Sensors 2023, 23(20), 8645; https://doi.org/10.3390/s23208645 - 23 Oct 2023

Cited by 1 | Viewed by 1772

Abstract

This article introduces a technical solution for investigating the movement of gases in the atmosphere through the turbulent pulsation method. A comprehensive control system was developed to measure and record the concentrations of carbon dioxide and methane, temperature, humidity, atmospheric air pressure, wind [...] Read more.

This article introduces a technical solution for investigating the movement of gases in the atmosphere through the turbulent pulsation method. A comprehensive control system was developed to measure and record the concentrations of carbon dioxide and methane, temperature, humidity, atmospheric air pressure, wind direction, and speed in the vertical plane. The selection and validation of sensor types and brands for each parameter, along with the system for data collection, registration, and device monitoring, were meticulously executed. The AHT21 + ENS160 sensor was chosen for temperature measurement, the BME680 was identified as the optimal sensor for humidity and atmospheric pressure control, Eu-M-CH4-OD was designated for methane gas analysis, and CM1107N for carbon dioxide measurements. Wind direction and speed are best measured with the SM5386V anemometer. The control system utilizes the Arduino controller, and software was developed for the multicomponent gas analyzer. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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22 pages, 2186 KiB

Open AccessArticle

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

by Armaghan Darbandi and Myung-Kyun Kim

Energies 2023, 16(20), 7039; https://doi.org/10.3390/en16207039 - 11 Oct 2023

Cited by 1 | Viewed by 1258

Abstract

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery [...] Read more.

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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19 pages, 4108 KiB

Open AccessArticle

Analytical Technique Leveraging Processing Gain for Evaluating the Anti-Jamming Potential of Underwater Acoustic Direct Sequence Spread Spectrum Communication Systems

by Xiaowei Wang and Qidou Zhou

Symmetry 2023, 15(9), 1710; https://doi.org/10.3390/sym15091710 - 6 Sep 2023

Cited by 2 | Viewed by 1725

Abstract

This study proposes an analytical technique underpinned by processing gain to evaluate the anti-jamming potential of an underwater acoustic direct-sequence spread-spectrum (DSSS) communication system that employs a short-period pseudo-noise (PN) sequence. The processing gain comes from the symmetry of the coding, which provides [...] Read more.

This study proposes an analytical technique underpinned by processing gain to evaluate the anti-jamming potential of an underwater acoustic direct-sequence spread-spectrum (DSSS) communication system that employs a short-period pseudo-noise (PN) sequence. The processing gain comes from the symmetry of the coding, which provides a mechanism for separating desired signals from unwanted ones, and the apparent randomness of the coding, which suppresses interference and noise in the system. The robustness of such a system against wideband interference, partial-band jamming, and single-frequency interference is emulated. Outcomes suggest that, in comparison to a standard binary phase shift keying (BPSK) system, the DSSS system’s ability to resist wideband interference is limited, with only a marginal increase in immunity performance of approximately 0.5 dB. Contrarily, it suppresses partial-band jamming effectively, with the suppression level dependent on the interference bandwidth and its relative position concerning the signal carrier frequency. The influence of single-frequency interference on system performance depends similarly on its relative location relative to the signal carrier frequency. In all situations where the interference frequency offset is an integer multiple of the bit bandwidth, the system exhibits the worst performance when the frequency offset equals the bit bandwidth. Upon comparing resistance levels to identical power interferences targeted at the signal carrier frequency, our system demonstrates optimal resilience to single-frequency interference. In concordance with the empirical findings, the simulated results substantiate both the effectiveness and practicability of the proposed analytical method based on processing gain. Subsequently, this study contributes a novel perspective for evaluating the anti-jamming potential of DSSS systems. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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14 pages, 3883 KiB

Open AccessArticle

Unveiling LoRa’s Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island

by João Pinelo, André Dionísio Rocha, Miguel Arvana, João Gonçalves, Nuno Cota and Pedro Silva

Sensors 2023, 23(17), 7394; https://doi.org/10.3390/s23177394 - 24 Aug 2023

Cited by 4 | Viewed by 3195

Abstract

In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime [...] Read more.

In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime sensing data. Specifically, we conduct an experimental assessment of the Azorean Long Range Wide Area Network (LoRaWAN) coverage. Our tests involve positioning the gateway at the island’s highest point and installing end nodes on medium-sized fishing vessels. Through measurements of received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and lines of sight (LOS), we showcase the potential of LoRaWAN transmissions to achieve communication distances exceeding 130 km in a LOS-free scenario over the ocean. These findings highlight the promising capabilities of LoRaWAN for reliable and long-range maritime communication of sensing data. Full article

(This article belongs to the Topic Application of IoT on Manufacturing, Communication and Engineering)

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