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15 pages, 3745 KiB

Open AccessArticle

Indoor Microclimate Monitoring and Forecasting: Public Sector Building Use Case

by Ruslans Sudniks, Arturs Ziemelis, Agris Nikitenko, Vasco N. G. J. Soares and Andis Supe

Information 2025, 16(2), 121; https://doi.org/10.3390/info16020121 - 8 Feb 2025

Viewed by 585

Abstract

This research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga [...] Read more.

This research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga Technical University’s campus buildings, equipped with around 128 IAQ sensors, is used as a test bed to create a digital shadow including a comparison of five ML-based data prediction tools. We compare the IAQ data prediction loss using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) error metrics based on real sensor data. Gated Recurrent Unit (GRU) and Kolmogorov–Arnold Networks (KAN) prove to be the most accurate models regarding the prediction error. Also, GRU proved to be the most efficient model regarding the required computation time. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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29 pages, 4918 KiB

Open AccessArticle

Implementation of Sensor Input Setup Assistance Service Using Generative AI for SEMAR IoT Application Server Platform

by I Nyoman Darma Kotama, Nobuo Funabiki, Yohanes Yohanie Fridelin Panduman, Komang Candra Brata, Anak Agung Surya Pradhana, Noprianto and I Gusti Made Ngurah Desnanjaya

Information 2025, 16(2), 108; https://doi.org/10.3390/info16020108 - 5 Feb 2025

Viewed by 739

Abstract

For rapid deployments of various IoT application systems, we have developed Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) as an integrated server platform. It is equipped with rich functions for collecting, analyzing, and visualizing various data. Unfortunately, the proper configuration [...] Read more.

For rapid deployments of various IoT application systems, we have developed Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) as an integrated server platform. It is equipped with rich functions for collecting, analyzing, and visualizing various data. Unfortunately, the proper configuration of SEMAR with a variety of IoT devices can be complex and challenging for novice users, since it often requires technical expertise. The assistance of Generative AI can be helpful to solve this drawback. In this paper, we present an implementation of a sensor input setup assistance service for SEMAR using prompt engineering techniques and Generative AI. A user needs to define the requirement specifications and environments of the IoT application system for sensor inputs, and give them to the service. Then, the service provides step-by-step guidance on sensor connections, communicating board configurations, network connections, and communication protocols to the user, which can help the user easily set up the configuration to connect the relevant devices to SEMAR. For evaluations, we applied the proposal to the input sensor setup processes of three practical IoT application systems with SEMAR, namely, a smart light, water heater, and room temperature monitoring system. In addition, we applied it to the setup process of an IoT application system for a course for undergraduate students at the Insitut Bisnis dan Teknologi (INSTIKI), Indonesia. The results demonstrate the effectiveness of the proposed service for SEMAR. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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24 pages, 38325 KiB

Open AccessArticle

A Yoga Pose Difficulty Level Estimation Method Using OpenPose for Self-Practice System to Yoga Beginners

by Cheng-Liang Shih, Jun-You Liu, Irin Tri Anggraini, Yanqi Xiao, Nobuo Funabiki and Chih-Peng Fan

Information 2024, 15(12), 789; https://doi.org/10.3390/info15120789 - 9 Dec 2024

Viewed by 956

Abstract

Yoga is an exercise preferable for various users at different ages to enhance physical and mental health. To help beginner yoga self-practitioners avoid getting injured by selecting difficult yoga poses, the information of the difficulty level of yoga poses is very important to [...] Read more.

Yoga is an exercise preferable for various users at different ages to enhance physical and mental health. To help beginner yoga self-practitioners avoid getting injured by selecting difficult yoga poses, the information of the difficulty level of yoga poses is very important to provide an objective metric to assist yoga self-practitioners in selecting appropriate exercises on the basis of their skill level by using the yoga self-practice system. To enhance the developed yoga self-practice system, the yoga difficulty level estimation function will enable users to clearly understand whether the selected yoga poses are suitable for them. In this paper, the newest difficulty level estimation method of yoga poses is proposed by using and analyzing OpenPose two-dimensional (2D) human body keypoints. The proposed method effectively uses the selected six keypoints areas of the upper and lower body, body support types, center of gravity calculations, and body tilt angles and slopes to produce estimations. Firstly, the method calculates the weighted centers of the upper and lower human body for each pose by using keypoints. Secondly, it refers the slope of the centroid line between the two centers and infers the body’s balance state. Lastly, the system estimates the difficulty level by additionally considering the keypoints of the body to contact the ground. For evaluations of the proposal, more than one hundred yoga poses are collected from the Internet and applied to classify them into five difficulty levels. Through comparisons with subjective levels from one instructor and 10 users, the validity of the estimation results is confirmed, a comparison is performed with existing designs, and it is implemented in embedded systems. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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14 pages, 3163 KiB

Open AccessArticle

QYOLO: Contextual Query-Assisted Object Detection in High-Resolution Images

by Mingyang Gao, Wenrui Wang, Jia Mao, Jun Xiong, Zhenming Wang and Bo Wu

Information 2024, 15(9), 563; https://doi.org/10.3390/info15090563 - 12 Sep 2024

Cited by 1 | Viewed by 1061

Abstract

High-resolution imagery captured by drones can detect critical components on high-voltage transmission towers, providing inspection personnel with essential maintenance insights and improving the efficiency of power line inspections. The high-resolution imagery is particularly effective in enhancing the detection of fine details such as [...] Read more.

High-resolution imagery captured by drones can detect critical components on high-voltage transmission towers, providing inspection personnel with essential maintenance insights and improving the efficiency of power line inspections. The high-resolution imagery is particularly effective in enhancing the detection of fine details such as screws. The QYOLO algorithm, an enhancement of YOLOv8, incorporates context queries into the feature pyramid, effectively capturing long-range dependencies and improving the network’s ability to detect objects. To address the increased network depth and computational load introduced by query extraction, Ghost Separable Convolution (GSConv) is employed, reducing the computational expense by half and further improving the detection performance for small objects such as screws. The experimental validation using the Transmission Line Accessories Dataset (TLAD) developed for this project demonstrates that the proposed improvements increase the average precision (AP) for small objects by 5.5% and the F1-score by 3.5%. The method also enhances detection performance for overall targets, confirming its efficacy in practical applications. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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19 pages, 1196 KiB

Open AccessArticle

AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge

by Dimitrios Giagkos, Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis, Francky Catthoor and Dimitrios Soudris

Information 2024, 15(8), 480; https://doi.org/10.3390/info15080480 - 13 Aug 2024

Viewed by 1844

Abstract

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams in IoT environments. To avoid data communication overheads, a common practice is to have computation close to [...] Read more.

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams in IoT environments. To avoid data communication overheads, a common practice is to have computation close to the data source rather than Cloud offloading. Typically, video analytics are organized as separate tasks, each with different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a promising approach for mapping such types of applications, enabling fine-grained deployment and management in a per-function, and per-device manner. However, there is a tradeoff between QoS adherence and resource efficiency. Performance variability due to function co-location and prevalent resource heterogeneity make maintaining QoS challenging. At the same time, resource efficiency is essential to avoid waste, such as unnecessary power consumption and CPU reservation. In this paper, we present Darly, a QoS-, interference- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments on top of distributed Edge nodes. The proposed framework incorporates a DRL agent that exploits performance counters to identify the levels of interference and the degree of heterogeneity in the underlying Edge infrastructure. It combines this information along with user-defined QoS requirements to improve resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. We evaluate Darly on a typical Edge cluster with a real-world workflow composed of commonly used serverless video analytics functions and show that our approach achieves efficient scheduling of the deployed functions by satisfying multiple QoS requirements for up to 91.6% (Profile-based) of the total requests under dynamic conditions. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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37 pages, 40520 KiB

Open AccessArticle

The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users

by Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, João M. L. P. Caldeira and Vasco N. G. J. Soares

Information 2024, 15(7), 389; https://doi.org/10.3390/info15070389 - 2 Jul 2024

Viewed by 1353

Abstract

Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable [...] Read more.

Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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30 pages, 4934 KiB

Open AccessArticle

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

by Yohanes Yohanie Fridelin Panduman, Nobuo Funabiki, Evianita Dewi Fajrianti, Shihao Fang and Sritrusta Sukaridhoto

Information 2024, 15(3), 153; https://doi.org/10.3390/info15030153 - 9 Mar 2024

Cited by 14 | Viewed by 7581

Abstract

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single [...] Read more.

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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30 pages, 2463 KiB

Open AccessArticle

IoT-Assisted Automatic Driver Drowsiness Detection through Facial Movement Analysis Using Deep Learning and a U-Net-Based Architecture

by Shiplu Das, Sanjoy Pratihar, Buddhadeb Pradhan, Rutvij H. Jhaveri and Francesco Benedetto

Information 2024, 15(1), 30; https://doi.org/10.3390/info15010030 - 2 Jan 2024

Cited by 16 | Viewed by 6054

Abstract

The main purpose of a detection system is to ascertain the state of an individual’s eyes, whether they are open and alert or closed, and then alert them to their level of fatigue. As a result of this, they will refrain from approaching [...] Read more.

The main purpose of a detection system is to ascertain the state of an individual’s eyes, whether they are open and alert or closed, and then alert them to their level of fatigue. As a result of this, they will refrain from approaching an accident site. In addition, it would be advantageous for people to be promptly alerted in real time before the occurrence of any calamitous events affecting multiple people. The implementation of Internet-of-Things (IoT) technology in driver action recognition has become imperative due to the ongoing advancements in Artificial Intelligence (AI) and deep learning (DL) within Advanced Driver Assistance Systems (ADAS), which are significantly transforming the driving encounter. This work presents a deep learning model that utilizes a CNN–Long Short-Term Memory network to detect driver sleepiness. We employ different algorithms on datasets such as EM-CNN, VGG-16, GoogLeNet, AlexNet, ResNet50, and CNN-LSTM. The aforementioned algorithms are used for classification, and it is evident that the CNN-LSTM algorithm exhibits superior accuracy compared to alternative deep learning algorithms. The model is provided with video clips of a certain period, and it distinguishes the clip by analyzing the sequence of motions exhibited by the driver in the video. The key objective of this work is to promote road safety by notifying drivers when they exhibit signs of drowsiness, minimizing the probability of accidents caused by fatigue-related disorders. It would help in developing an ADAS that is capable of detecting and addressing driver tiredness proactively. This work intends to limit the potential dangers associated with drowsy driving, hence promoting enhanced road safety and a decrease in accidents caused by fatigue-related variables. This work aims to achieve high efficacy while maintaining a non-intrusive nature. This work endeavors to offer a non-intrusive solution that may be seamlessly integrated into current automobiles, hence enhancing accessibility to a broader spectrum of drivers through the utilization of facial movement analysis employing CNN-LSTM and a U-Net-based architecture. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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32 pages, 8511 KiB

Open AccessArticle

The PolitiFact-Oslo Corpus: A New Dataset for Fake News Analysis and Detection

by Nele Põldvere, Zia Uddin and Aleena Thomas

Information 2023, 14(12), 627; https://doi.org/10.3390/info14120627 - 23 Nov 2023

Cited by 6 | Viewed by 5848

Abstract

This study presents a new dataset for fake news analysis and detection, namely, the PolitiFact-Oslo Corpus. The corpus contains samples of both fake and real news in English, collected from the fact-checking website PolitiFact.com. It grew out of a need for a more [...] Read more.

This study presents a new dataset for fake news analysis and detection, namely, the PolitiFact-Oslo Corpus. The corpus contains samples of both fake and real news in English, collected from the fact-checking website PolitiFact.com. It grew out of a need for a more controlled and effective dataset for fake news analysis and detection model development based on recent events. Three features make it uniquely placed for this: (i) the texts have been individually labelled for veracity by experts, (ii) they are complete texts that strictly correspond to the claims in question, and (iii) they are accompanied by important metadata such as text type (e.g., social media, news and blog). In relation to this, we present a pipeline for collecting quality data from major fact-checking websites, a procedure which can be replicated in future corpus building efforts. An exploratory analysis based on sentiment and part-of-speech information reveals interesting differences between fake and real news as well as between text types, thus highlighting the importance of adding contextual information to fake news corpora. Since the main application of the PolitiFact-Oslo Corpus is in automatic fake news detection, we critically examine the applicability of the corpus and another PolitiFact dataset built based on less strict criteria for various deep learning-based efficient approaches, such as Bidirectional Long Short-Term Memory (Bi-LSTM), LSTM fine-tuned transformers such as Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa, and XLNet. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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18 pages, 6385 KiB

Open AccessArticle

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

by Pedro C. Santana-Mancilla, Oscar E. Castrejón-Mejía, Silvia B. Fajardo-Flores and Luis E. Anido-Rifón

Information 2023, 14(12), 625; https://doi.org/10.3390/info14120625 - 21 Nov 2023

Cited by 1 | Viewed by 2836

Abstract

Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal [...] Read more.

Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machine learning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machine learning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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23 pages, 5243 KiB

Open AccessEditor’s ChoiceArticle

Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT

by Yibo He, Kah Phooi Seng and Li Minn Ang

Information 2023, 14(10), 575; https://doi.org/10.3390/info14100575 - 19 Oct 2023

Cited by 9 | Viewed by 4854

Abstract

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal [...] Read more.

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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16 pages, 5064 KiB

Open AccessArticle

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

by Asier del Rio, Oscar Barambones, Jokin Uralde, Eneko Artetxe and Isidro Calvo

Information 2023, 14(10), 556; https://doi.org/10.3390/info14100556 - 11 Oct 2023

Cited by 6 | Viewed by 2798

Abstract

Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are [...] Read more.

Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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16 pages, 2179 KiB

Open AccessArticle

A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain

by Habib Ullah Khan, Anwar Hussain, Shah Nazir, Farhad Ali, Muhammad Zubair Khan and Inam Ullah

Information 2023, 14(8), 459; https://doi.org/10.3390/info14080459 - 14 Aug 2023

Cited by 12 | Viewed by 2515

Abstract

The upcoming generation of communications can provide richer mobility, high data rate, reliable security, better quality of services, and supporting mobility requirements in the Internet of Things (IoT) environment. Integrating modern communication with IoT demands more secure, scalable, and resource-efficient mobility solutions for [...] Read more.

The upcoming generation of communications can provide richer mobility, high data rate, reliable security, better quality of services, and supporting mobility requirements in the Internet of Things (IoT) environment. Integrating modern communication with IoT demands more secure, scalable, and resource-efficient mobility solutions for better business opportunities. In a massive 6G-enabled IoT environment, modern mobility solutions such as proxy mobile IPv6 (PMIPv6) have the potential to provide enhanced mobility and resource efficiency. For supporting richer mobility, a cost-effective and resource-efficient mobility solution is required in a massive 6G-enabled IoT environment. The main objective of the presented study is to provide a resource-friendly mobility solution for supporting the effective integration of future communication in the massive IoT domain. In that context, a location-based, resource-efficient PMIPv6 extension protocol is proposed to provide resource efficiency in terms of required signaling, packet loss, and handover latency. To compare and analyze the proposed model’s effectiveness, mathematical equations are derived for the existing as well as for the proposed solution, and such equations are implemented. Based on the comparison among existing and proposed solutions, the results show that the proposed location-based service-oriented proxy mobile IPv6 extension is resource efficient for supporting mobility in 6G-enabled IoT. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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24 pages, 3759 KiB

Open AccessArticle

A Novel Approach of Resource Allocation for Distributed Digital Twin Shop-Floor

by Haijun Zhang, Qiong Yan, Yan Qin, Shengwei Chen and Guohui Zhang

Information 2023, 14(8), 458; https://doi.org/10.3390/info14080458 - 13 Aug 2023

Viewed by 2493

Abstract

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin [...] Read more.

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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17 pages, 4899 KiB

Open AccessArticle

Combining Classifiers for Deep Learning Mask Face Recognition

by Wen-Chang Cheng, Hung-Chou Hsiao, Yung-Fa Huang and Li-Hua Li

Information 2023, 14(7), 421; https://doi.org/10.3390/info14070421 - 21 Jul 2023

Cited by 3 | Viewed by 2200

Abstract

This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV2. The models are augmented by connecting an otherwise fully connected network [...] Read more.

This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV2. The models are augmented by connecting an otherwise fully connected network with a SoftMax output layer. We combine triplet loss and categorical cross-entropy loss to optimize the training process. In addition, the learning rate of the optimizer is dynamically updated using the cosine annealing mechanism, which improves the convergence of the model during training. Mask face recognition (MFR) experimental results on a custom MASK600 dataset show that proposed InceptionResNetV2 and InceptionV3 use only 20 training epochs, and MobileNetV2 uses only 50 training epochs, but to achieve more than 93% accuracy than the previous works of MFR with annealing. In addition to reaching a practical level, it saves time for training models and effectively reduces energy costs. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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15 pages, 6561 KiB

Open AccessArticle

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

by Hossein Shahverdi, Mohammad Nabati, Parisa Fard Moshiri, Reza Asvadi and Seyed Ali Ghorashi

Information 2023, 14(7), 404; https://doi.org/10.3390/info14070404 - 14 Jul 2023

Cited by 13 | Viewed by 3222

Abstract

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications [...] Read more.

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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Review

Jump to: Research

41 pages, 4010 KiB

Open AccessReview

AI and Related Technologies in the Fields of Smart Agriculture: A Review

by Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, Konstantinos Kotis and Dimitris Spiliotopoulos

Information 2025, 16(2), 100; https://doi.org/10.3390/info16020100 - 2 Feb 2025

Cited by 1 | Viewed by 4277

Abstract

The integration of cutting-edge technologies—such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and [...] Read more.

The integration of cutting-edge technologies—such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies. The areas examined in this study are open field smart farming, vertical and indoor farming, zero waste agriculture, precision livestock farming, smart greenhouses, and regenerative agriculture. This paper links current research, technological innovations, and case studies to present a comprehensive review of these emerging technologies being developed in the context of smart agriculture, for the benefit of farmers and consumers in general. By exploring practical applications and future perspectives, this work aims to provide valuable insights to address global food security challenges, minimize environmental impacts, and support sustainable development goals through the application of new technologies. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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Open AccessReview

Bibliometric Analysis of IoT Lightweight Cryptography

by Zenith Dewamuni, Bharanidharan Shanmugam, Sami Azam and Suresh Thennadil

Information 2023, 14(12), 635; https://doi.org/10.3390/info14120635 - 28 Nov 2023

Cited by 6 | Viewed by 3296

Abstract

In the rapidly developing world of the Internet of Things (IoT), data security has become increasingly important since massive personal data are collected. IoT devices have resource constraints, which makes traditional cryptographic algorithms ineffective for securing IoT devices. To overcome resource limitations, lightweight [...] Read more.

In the rapidly developing world of the Internet of Things (IoT), data security has become increasingly important since massive personal data are collected. IoT devices have resource constraints, which makes traditional cryptographic algorithms ineffective for securing IoT devices. To overcome resource limitations, lightweight cryptographic algorithms are needed. To identify research trends and patterns in IoT security, it is crucial to analyze existing works, keywords, authors, journals, and citations. We conducted a bibliometric analysis using performance mapping, science mapping, and enrichment techniques to collect the necessary information. Our analysis included 979 Scopus articles, 214 WOS articles, and 144 IEEE Xplore articles published during 2015–2023, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 4.3.1, VOSviewer version 1.6.19, and the bibliometrix library. We discovered that India is the leading country for this type of research. Archarya and Bansod are the most relevant authors; lightweight cryptography and cryptography are the most relevant terms; and IEEE Access is the most significant journal. Research on lightweight cryptographic algorithms for IoT devices (Raspberry Pi) has been identified as an important area for future research. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

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