Search

In the IIoT, billions of devices continually provide information that is extremely diverse, variable, and large-scale and presents significant hurdles for interpretation and analysis. Additionally, issues about data transmission, scaling, computation, and storage can result in data anomalies that significantly affect IIoT applications. This work presents a novel anomaly detection framework for the IIoT in the context of the challenges posed by vast, heterogeneous, and complex data streams. This paper proposes a two-staged multi-variate approach employing a composition of long short-term memory (LSTM) and a random forest (RF) Classifier. Our approach leverages the LSTM's superior temporal pattern recognition capabilities in multi-variate time-series data and the exceptional classification accuracy of the RF model. By integrating the strengths of LSTM and RF models, our method provides not only precise predictions but also effectively discriminates between anomalies and normal occurrences, even in imbalanced datasets. We evaluated our model on two real-world datasets comprising periodic and non-periodic, short-term, and long-term temporal dependencies. Comparative studies indicate that our proposed method outperforms well-established alternatives in anomaly detection, highlighting its potential application in the IIoT environment. [ABSTRACT FROM AUTHOR]

In an era where Information and Communication Technologies (ICT) redefine the boundaries of competition and collaboration, the concept of coopetition — simultaneous competition and cooperation — emerges as a strategic imperative for small and medium-sized enterprises (SMEs). This study presents the design and development of an Industrial Internet of Things (IIoT) artefact designed to enable competition among SMEs. Bringing the Service-Dominant Logic (S-D logic) foundations to IIoT, a Design Science Research (DSR) approach was employed. This approach facilitated the integration of design theory with practical problem-solving, creating the Coopetition IIoT-based System. The primary goal of this solution is to augment the dynamics of coopetition networks, with a particular focus on SMEs. The practical evaluation of the Coopetition IIoT-based System is assessed through a prototype evaluation by experts representing twenty-four manufacturing stone SMEs, a crucial sector in the Portuguese economy. The feedback received was highly positive, indicating a positive evaluation rate of 78.9%. This favourable response highlights the Coopetition IIoT-based System's proficiency in fostering simultaneous competition and cooperation throughout the lifecycle of business opportunities in SME networks, thereby underscoring its potential as a facilitator of effective competition. Theoretically, this research enriches the application of S-D logic in coopetition networks and advances state-of-the-art IIoT systems. Practically, the Coopetition IIoT-based System demonstrates significant potential in boosting the competitiveness of SMEs in developed economies. Nonetheless, the ultimate efficacy of such IIoT systems will be best determined through real-world application and evaluation. Future research should concentrate on the real-case deployment and assessment of Coopetition IIoT-based Systems within coopetition networks. [ABSTRACT FROM AUTHOR]

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies. [ABSTRACT FROM AUTHOR]

Due to its outstanding privacy-related characteristics, Federated Learning (FL) has recently become a popular solution for the IIoT's data privacy and scalability issues. However, more research is needed to determine how unique streaming data in IIoT Settings affects FL-enabled IIoT architectures, with unique streaming data affecting accuracy and reducing convergence performance. To achieve this goal, this paper explains the streaming data learning problem in an IIoT framework enabled by FL. Afterward, it outlines two unique issues relevant to this situation: convergence and the catastrophic forgetting that occurs throughout training. This article presents FedStream, a practical FL framework for IIoT streaming data applications, considering these challenges. In particular, we develop a straightforward and effective pairwise similarity-based streaming data replacement training method that systematically replaces original data samples with ones that show high similarity during the iterative training process. This not only improves the accuracy but also reduces the convergence process and catastrophic forgetting problem. Comprehensive case studies support the effectiveness of the proposed method. Finally, the article recommends potential research areas, encouraging academics and industry professionals to explore these emerging topics further. [ABSTRACT FROM AUTHOR]

The communication of Industrial Internet of Things (IIoT) devices faces important security and privacy challenges. With the rapid increase in the number of devices, it is difficult for traditional security mechanisms to balance performance and security. Although schemes based on encryption and authentication exist, there are still difficulties in achieving lightweight security. In this paper, an authentication and key exchange scheme combining hardware security features and modern encryption technology is proposed for the MQTT-SN protocol, which is not considered security. The scheme uses Physical Unclonable Functions (PUFs) to generate unpredictable responses, and combines random numbers, time stamps, and shared keys to achieve two-way authentication and secure communication between devices and broker, effectively preventing network threats such as replay and man-in-the-middle attacks. Through verification, the proposed scheme has proved effective in terms of security and robustness, has computational and communication cost advantages compared with recent schemes, and provides higher availability. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative hybrid wireless power transfer (H-WPT) scheme tailored for IIoT networks employing multiple relay nodes. The scheme allows relay nodes to dynamically select their power source for energy harvesting based on real-time channel conditions. Our analysis evaluates outage probability within decode-and-forward (DF) relaying and adaptive power splitting (APS) frameworks, while also considering the energy used by relay nodes for ACK signaling. A notable feature of the H-WPT scheme is its decentralized operation, enabling relay nodes to independently choose the optimal relay and power source using instantaneous channel gain. This approach conserves significant energy otherwise wasted in centralized control methods, where extensive information exchange is required. This conservation is particularly beneficial for energy-constrained sensor networks, significantly extending their operational lifetime. Numerical results demonstrate that the proposed hybrid approach significantly outperforms the traditional distance-based power source selection approach, without additional energy consumption or increased system complexity. The scheme's efficient power management capabilities underscore its potential for practical applications in IIoT environments, where resource optimization is crucial. [ABSTRACT FROM AUTHOR]

In the past few years, latency‐sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self‐driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across‐domain business task computing on distributed edge servers. With this observation, a trade‐off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low‐latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low‐latency edge server. The experiments and comparison analysis verify the availability of our method. [ABSTRACT FROM AUTHOR]

Abstract In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Due to its outstanding privacy-related characteristics, Federated Learning (FL) has recently become a popular solution for the IIoT’s data privacy and scalability issues. However, more research is needed to determine how unique streaming data in IIoT Settings affects FL-enabled IIoT architectures, with unique streaming data affecting accuracy and reducing convergence performance. To achieve this goal, this paper explains the streaming data learning problem in an IIoT framework enabled by FL. Afterward, it outlines two unique issues relevant to this situation: convergence and the catastrophic forgetting that occurs throughout training. This article presents FedStream, a practical FL framework for IIoT streaming data applications, considering these challenges. In particular, we develop a straightforward and effective pairwise similarity-based streaming data replacement training method that systematically replaces original data samples with ones that show high similarity during the iterative training process. This not only improves the accuracy but also reduces the convergence process and catastrophic forgetting problem. Comprehensive case studies support the effectiveness of the proposed method. Finally, the article recommends potential research areas, encouraging academics and industry professionals to explore these emerging topics further.

Abstract Traditional security detection methods face challenges in identifying zero-day attacks in critical infrastructures (CIs) integrated with the industrial internet of things (IIoT). These attacks exploit unknown vulnerabilities and are difficult to detect due to their connection to physical systems. The integration of legacy ICS networks with modern computing and networking technologies has significantly expanded the attack surface, making these systems more susceptible to cyber-attacks. Despite existing security measures, attackers continually find ways to breach these operating networks. Anomaly detection systems are critical in protecting these CIs from current cyber threats. This study investigates the effectiveness of unsupervised anomaly detection models in detecting operational anomalies that could lead to cyber-attacks, thereby disrupting and negatively impacting quality of life. We preprocess the data with a focus on cybersecurity and chose the SWAT dataset because it accurately represents the types of attack vectors that critical infrastructures commonly encounter. We evaluated the performance of isolation forest (IF), local outlier factor (LOF), one-class SVM (OCSVM), and Autoencoder algorithms—trained exclusively on normal data—in enhancing cybersecurity within IIoT environments. Our comprehensive analysis includes an assessment of each model’s detection capabilities. The findings highlight the VAE-LSTM model’s potential to identify cyber-attacks within seconds in a high-frequency dataset, suggesting near real-time detection capability. The final model combines the reconstruction ability of the variational autoencoder (VAE) with regularization using the Kullback–Leibler divergence, reflecting the non-Gaussian nature of industrial system data. Our model successfully detected 23 out of 26 attack scenarios in the SWAT dataset, demonstrating its effectiveness in improving the security of IIoT-based CIs.

In many industrial settings, there are several problems that can arise during the production process. These include machine breakdowns, quality issues, and unexpected delays, which can impact productivity, reduce overall efficiency, and result in lower quality output. In addition, without an effective monitoring system, it can be difficult to identify the root causes of these problems and take appropriate corrective actions.To address these challenges, the implementation of a smart andon system can be highly beneficial. This system enables real-time monitoring of the production process, allowing operators and management to quickly identify and respond to any issues as they arise. By providing instant notifications and alerts, the smart andon system can help reduce downtime, increase productivity, and improve product quality. It also enables more accurate and comprehensive data collection, facilitating better analysis and decision-making by management. Overall, the smart andon system can play a critical role in improving operational efficiency, reducing costs, and enhancing overall competitiveness in today's highly competitive industrial landscape.the implementation of a smart andon system has been shown to improve production efficiency, reduce downtime, and increase overall equipment effectiveness (OEE). The system allows for real-time monitoring of the production process, early detection of problems, and quick resolution of issues through timely alerts and notifications. This can result in significant cost savings for the industry, improved product quality, and increased customer satisfaction.

Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era.

O interesse em dispositivos IoT (Internet of Things) é cada dia maior por parte de várias verticais da Indústria, tais como automotiva, agrícola, elétrica, manufatura, saúde e cidades inteligentes, devido à flexibilidade e à facilidade que eles oferecem no monitoramento e na gestão da cadeia produtiva. Contudo, utilizar essa tecnologia habilitadora em ambientes industriais se mostrou um grande desafio, devido à heterogeneidade na diversidade tanto dos dispositivos quanto das aplicações e dos serviços disponibilizados. Portanto, gerenciá-los de forma eficiente não é uma tarefa trivial. Nesse contexto, com o crescimento da popularidade do conceito de Indústria 4.0, o número de artigos científicos que englobam o tema de gerenciamento de redes e serviços em redes IoT industriais (IIoT) vem crescendo. Várias soluções têm sido propostas nas diferentes subáreas possíveis da gerência de redes. Este trabalho tem o propósito de mapear as pesquisas relacionadas ao gerenciamento de recursos e aplicações IoT industriais, com o intuito de investigar tais soluções e identificar os direcionamentos de pesquisa, além das possíveis lacunas a serem exploradas.

Connectivity and data exchange are key features of Industry 4.0. In this paradigm, (Industrial) Internet of Things ((I)IoT) devices are a vital component facilitating the collection and transmission of environmental data from the physical system to the central station for processing and analysis (digital twin [DT]). However, although (I)IoT devices play a critical role in this process, they are not inherently equipped to run strong encryption mechanisms to secure the data they transmit over wired or wireless channels. This research aims to explore the potential of DTs in securing Industry 4.0 applications and the security mechanism employed to ensure confidentiality, integrity, and authentication of data communicated between (I)IoT and DT through a systematic literature review (SLR). This SLR, based on the analysis of 67 papers published between 2018 and 2023, underscores the evolving significance of DT technology, particularly within the ambit of Industry 4.0. The findings illuminate the pervasive influence of DT technology across multiple industrial sectors. The result SLR revealed that DT is growing and being widely adopted as a security tool particularly in Industry 4.0 using enabling technology like machine learning, data analytics, blockchain, and 5G networks to provide security solutions such as intrusion detection, vulnerability assessment, cyber range, and threat intelligence. [ABSTRACT FROM AUTHOR]

The oil and gas industry remains critical to the global economy, as it contributes to the provision of energy and raw materials. Nonetheless, this sector continued to face clear challenges in operational effectiveness, risk and security. Regular tracking methods are limited to latency issues; they are not secure, and data may face integrity issues. To this end, this paper lays out an efficient fog-enabled private blockchain-based identity authentication approach for oil and gas field monitoring. By integrating IoT devices to blockchain,, decentralized control systems are created that enhance security, transparency, and efficient execution of transactions. In this scheme, by making full use of the decentralized structure of blockchain technology and applying the computational power of fog nodes, a secure and efficient identity authentication framework is designed. Fog nodes are an intermediary between IoT devices and blockchain technology, providing lower latency in communication, and therefore more efficient. The main contributions of this paper include: developing a decentralized authentication system based on private blockchains and fog nodes to overcome the drawbacks of centralized models. Create a network model using a private blockchain that dramatically improves feasibility by incorporating strict admission and authorization procedures. Hence, this leads to simultaneous registrations with minimal network time consensus Authentication that incorporating fuzzy extractor to connect the privacy-centric approach and to improve the security analysis and performance evaluation proving that the proposed solution provides better. According to the previous security analysis, it is clear that the scheme conflicts with different types of threats including DoS, MITM attacks, replay, Sybil, and message substitution attacks. The performance evaluation also shows low computational and communication costs, high compatibility, and real-time operation, which indicates that the proposed scheme is effective and can be implemented as a real-time oil and gas field monitoring system. [ABSTRACT FROM AUTHOR]

Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON's efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) consists of massive devices in different management domains, and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange. To address the above challenges, a viable solution that combines Certificateless Public Key Cryptography (CL-PKC) with blockchain technology can be utilized. However, as many existing schemes rely on a single Key Generation Center (KGC), they are prone to problems such as single points of failure and high computational overhead. In this case, this paper proposes a novel blockchain-based certificateless cross-domain authentication scheme, that integrates the threshold secret sharing mechanism without a trusted center, meanwhile, adopts blockchain technology to enable cross-domain entities to authenticate with each other and to negotiate session keys securely. This scheme also supports the dynamic joining and removing of multiple KGCs, ensuring secure and efficient cross-domain authentication and key negotiation. Comparative analysis with other protocols demonstrates that the proposed cross-domain authentication protocol can achieve high security with relatively low computational overhead. Moreover, this paper evaluates the scheme based on Hyperledger Fabric blockchain environment and simulates the performance of the certificateless scheme under different threshold parameters, and the simulation results show that the scheme has high performance. [ABSTRACT FROM AUTHOR]

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework. [ABSTRACT FROM AUTHOR]

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model's performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. [ABSTRACT FROM AUTHOR]

Purpose: Lean production has been proved to be a cost-effective and efficient means of production that reduces non-valve added activities. Industry 4.0 (I4.0) is a technology-driven platform that allows machines to interact with other systems through artificial intelligence, machine learning, industrial Internet of Things (IoT), etc. that improve the production system with flexibility, quality and customization throughout the whole value chain. New approaches to digitization of lean production have recently been emerged and they are transforming the industry and increasing productivity throughout the value chain. Through this article, an effort has been made to review the research published in this field. Design/methodology/approach: This paper reviews the literature published in various journals, the databases Web of science (WoS), ScienceDirect, Scopus, Emerald etc. were referred with a focus on lean concepts and tools and I4.0 technologies; it has been noticed that the integration of the lean tools with I4.0 technologies is a very effective tool for the industry. Findings: It has been found in the literature published earlier in various journals that lean manufacturing (LM) is commonly acknowledged and considered a best practice to improve the productivity. It is concerned with the tight integration of people into the industrial process through continuous improvement which leads to value addition throughout the whole value chain by eliminating non vale added activities. The findings show that organizations can improve their productivity and flexibility with speed and accuracy by integrating I4.0 technologies with LM, which is foremost need of any industry across the world. Originality/value: This article accentuates the connections between the principles and tools developed under the umbrella of I4.0 and those developed by the LM techniques, with a specific emphasis on how some of the principles and tools of I4.0 improve the implementation of lean principles dependent on the competence levels of the technology. Very few articles have been published in this area, and this paper is an original piece of research covering a review of extant research published in various journals. [ABSTRACT FROM AUTHOR]

Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel Gradient Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of Gradient Descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://doi.org/10.1016/j.jestch.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030–1038, https://doi.org/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722–62750, 2022, https://doi.org/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method "GDS-SRFFL" has overall accuracy of 89.42% as compared to other existing methods. [ABSTRACT FROM AUTHOR]

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring. [ABSTRACT FROM AUTHOR]

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) envisions enhanced surveillance and control for industrial applications through diverse IoT devices. However, the increasing heterogeneity of deployed end devices poses challenges to current practices, hampering overall performance as device numbers escalate. To tackle this issue, we introduce an innovative distributed power control algorithm leveraging the wireless channel's nature to approximate the centralized maximum-weight scheduling algorithm. Employing ubiquitous multi-protocol mobile devices as intermediaries, we propose a concurrent dual-hop/multi-hop backhauling strategy, improving interoperability and facilitating data relay, translation, and forwarding from end IoT devices. Our focus is directed towards addressing large-scale network stability and queue management challenges. We formulate a long-term time-averaged optimization problem, incorporating considerations of end-to-end rate control, routing, link scheduling, and resource allocation to guarantee essential network-wide throughput. Furthermore, we present a real-time decomposition-based approximation algorithm that ensures adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with the highest energy efficiency. Comprehensive numerical results verify significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. This work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications. [ABSTRACT FROM AUTHOR]

IoT devices produce a gigantic amount of data and it has grown exponentially in previous years. To get insights from this multi-property data, machine learning has proved its worth across the industry. The present paper provides an overview of the variety of data collected through IoT devices. The conflux of machine learning with IoT is also explained using the bibliometric analysis technique. This paper presents a systematic literature review using bibliometric analysis of the data collected from Scopus and WoS. Academic literature for the last six years is used to explore research insights, patterns, and trends in the field of IoT using machine learning. This study analyses and assesses research for the last six years using machine learning in seven IoT domains like Healthcare, Smart City, Energy systems, Industrial IoT, Security, Climate, and Agriculture. The author’s and country-wise citation analysis is also presented in this study. VOSviewer version 1.6.18 is used to provide a graphical representation of author citation analysis. This study may be quite helpful for researchers and practitioners to develop a blueprint of machine learning techniques in various IoT domains. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) service providers have become increasingly important in the manufacturing industry due to their ability to gather and process vast amounts of data from connected devices, enabling manufacturers to improve operational efficiency, reduce costs, and enhance product quality. These platforms provide manufacturers with real-time visibility into their production processes and supply chains, allowing them to optimize operations and make informed decisions. In addition, IIoT service providers can help manufacturers create new revenue streams through the development of innovative products and services and enable them to leverage the benefits of emerging technologies such as Artificial Intelligence (AI) and machine learning. Overall, the implementation of IIoT platforms in the manufacturing industry is crucial for companies seeking to remain competitive and meet the ever-increasing demands of customers in the digital age. In this study, the evaluation criteria to be considered in the selection of IIoT service provider in small and medium-sized (SME) manufacturing enterprises will be determined and IIoT service providers alternatives will be evaluated using the technique for order preference by similarity to an ideal solution (TOPSIS) method based on circular intuitionistic fuzzy sets. Based on the assessments conducted in accordance with the literature review and expert consultations, a set of 8 selection criteria has been established. These criteria encompass industry expertise, customer support, flexibility and scalability, security, cost-effectiveness, reliability, data analytics, as well as compatibility and usability. Upon evaluating these criteria, it was observed that the security criterion holds the highest significance, succeeded by cost-effectiveness, data analytics, flexibility and scalability, reliability, and customer support criteria, in descending order of importance. Following the evaluation of seven distinct alternatives against these criteria, it was deduced that the A6 alternative, a German service provider, emerged as the most favorable option. The identical issue was addressed utilizing sensitivity analysis alongside various multi-criteria decision-making (MCDM) methods, and after comprehensive evaluation, the outcomes were assessed. Spearman's correlation coefficient was computed to ascertain the association between the rankings derived from solving the problem using diverse MCDM methods. [ABSTRACT FROM AUTHOR]