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Productivity is a key factor in the success of a company, and real-time monitoring systems are necessary to achieve this goal. Manual data collection is time-consuming and exhausting. Industrial Internet of Things (IIoT) technology has been rapidly advancing in monitoring and optimizing industrial processes. Production processes can be disrupted due to machine problems; hence, the need to analyze machine efficiency using the overall equipment effectiveness (OEE) method. This study implements a system that uses Industrial Internet of Things technology based on mobile application to monitor production processes, report production results, assess machine performance using the OEE method and provide maintenance notifications based on time-based maintenance. Research findings indicate that the production monitoring system implemented on a prototype press machine based on an interactive mobile application interface is capable of monitoring production processes and reporting production results. The system can also assess machine performance using the OEE method, with a calculation accuracy of 99.95% and maintenance notifications with a delay time of 1.04 seconds.

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium.

The rapid growth of Information Communication Technologies (ICT) has impacted many fields. In this context, the supply chain has also quickly evolved toward the digital supply chain where digital and electronic technologies have been integrated into every aspect of its end-to-end process. This evolution provides numerous benefits such as profit maximization, loss reduction, and the optimization of supply chain lead times. However, the use of such technologies has also considerably opened up various security threats and risks which have widened the attack surface on the entire end-to-end supply chain. We present a holistic survey on supply chain security. We discuss the different security issues and attacks that target the different supply chain technologies. Then, we discuss various countermeasures and security solutions proposed by academic and industry researchers to mitigate the identified threats. Finally, we provide some recommendations and best practices that can be adopted to achieve a secure supply chain. [ABSTRACT FROM AUTHOR]

The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchainbased scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

In the ever-evolving construction industry, the incorporation of the Industrial Internet of Things (IIoT) through Low-power Wireless Area Networks (LPWAN), such as LoRaWAN, has emerged as a practical solution for addressing the challenges posed by the limited 5G cellular coverage found in solutions like NB-IoT and LTE-M, especially when deployed in remote locations. Open-source LPWAN platforms like The Things Network (TTN) and ChirpStack have played a pivotal role in fostering the adoption of LoRa technology by providing a mature and cost-effective ecosystem that facilitates efficient device resource management. Within this context, maintaining continuous surveillance of LPWAN network gateways becomes critically important, requiring a meticulous examination of status indicators and an evaluation of the communication quality. This paper introduces a structured approach for extracting data from TTN to create a comprehensive gateway monitoring system. The methodology encompasses various aspects, including ensuring seamless server connectivity, specifically focusing on efficient information management and integration of real-world construction data. This foundational work sets the stage for a more in-depth exploration of the diverse management components within the network ecosystem. [ABSTRACT FROM AUTHOR]

With the improvement of network transmission level and hardware storage and computing capacity, the Internet of Things (IoT) has been rapidly developed, and its application has attracted extensive attention. As an innovative application of Internet of Things technology in the field of industrial automation production, the Industrial Internet of Things includes technologies such as edge computing, big data analysis and intelligent sensing, which greatly improves production efficiency and reduces management costs. At present, Industrial Internet of Things (IIoT) has become the key competitiveness of the country. However, the heterogeneity of equipment, the limitation of resources and the low security of data have greatly hindered the development of IIoT. Therefore, in terms of ensuring efficient energy utilization, a trusted relay routing mechanism for optimizing chain performance based on multi-attribute comprehensive evaluation for the IIoT is proposed. It is an improvement of the low-energy adaptive clustering hierarchy (LEACH) algorithm (a cluster routing protocol that is mainly used in WSN) to improve the network's data security transmission, energy efficiency and load balancing characteristics. [ABSTRACT FROM AUTHOR]

This article addresses the issue of information security in the Industrial Internet of Things (IIoT) environment. Information security risk assessment in the IIoT is complicated by several factors: the complexity and heterogeneity of the system, the dynamic nature of the system, the distributed network infrastructure, the lack of standards and guidelines, and the increased consequences of security breaches. Given these factors, information security risk assessment in the IIoT requires a comprehensive approach adapted to the peculiarities and requirements of a particular system and industry. It is necessary to use specialized risk assessment methods and to take into account the context and peculiarities of the system. The method of information security risk assessment in the IIoT, based on the mathematical apparatus of fuzzy set theory, is proposed. This paper analyzes information security threats for IIoT systems, from which the most significant criteria are selected. The rules, based on which decisions are made, are formulated in the form of logical formulas containing input parameters. Three fuzzy inference systems are used: one to estimate the probability of threat realization, another to estimate the probable damage, and a final one to estimate the information security risk for the IIoT system. Based on the proposed method, examples of calculating the information security risk assessment in the IIoT environment are provided. The proposed scientific approach can serve as a foundation for creating expert decision support systems for designing IIoT systems. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker's bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings. [ABSTRACT FROM AUTHOR]

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach's effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance. [ABSTRACT FROM AUTHOR]

As the importance of smart factories has recently been emphasized in industrial settings, the use of industrial networks is increasing. Accordingly, research was conducted on Edge Gateway, which collects data generated from equipment at industrial sites and transmits it to a cloud server. However, Edge Gateway studies require additional I/O to be installed in the industrial network to receive status information generated from manufacturing devices. This may result in increased network communication cycles and modification of PLC programs, which can result in budget and time consumption. Accordingly, in this paper, the solution is to add I/O to the Edge Gateway device to minimize PLC program modification without installing additional I/O in the industrial network. According to the experimental results, adding distributed I/O to the network resulted in an increase in cycle time of up to 0.24ms in the communication cycle, and showed an increase in cycle time of up to 8.41%. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) was developed based on the technology and applications of the Internet of Things (IoT) in an industrial environment. As it is a sub-set of the IoT, it requires higher levels of safety and security. While increased productivity, better management and high operational efficiency are its main goals, they involve managing many risks, such as conflicting criteria and uncertain information, that need to be assessed and ranked. Therefore, in this paper, the Multi-criteria Decision-making (MCDM) method is used to deal with these criteria and a neutrosophic environment to overcome the uncertainty. Also, the Analytical Hierarchical Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are proposed. The former is used to obtain the weights of the criteria and the latter to rank the management of risk in the IIOT system. Numerical examples are provided and a sensitivity analysis conducted to test the reliability of this model.

In recent years, statistics have indicated that the number of patients with malignant brain tumors has increased sharply. However, most surgeons still perform surgical training using the traditional autopsy and prosthesis model, which encounters many problems, such as insufficient corpse resources, low efficiency, and high cost. With the advent of the 5G era, a wide range of Industrial Internet of Things (IIOT) applications have been developed. Virtual Reality (VR) and Augmented Reality (AR) technologies that emerged with 5G are developing rapidly for intelligent medical training. To address the challenges encountered during neurosurgery training, and combining with cloud computing, in this paper, a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed, in which a VR simulator is embedded. The system enables real-time remote surgery training interaction through 5G transmission. Six experts and 18 novices were invited to participate in the experiment to verify the system. Subsequently, the two simulators were evaluated using face and construction validation methods. The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum (LC-CUSUM) evaluation method to validate the effectiveness of the two simulators. The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity, and had a better effect on the improvement of surgical skills. Moreover, the surgical training scheme proposed in this paper is effective, and the remote collaborative training effect of the system is ideal.

Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model. In this paper, we study an ML-based CS Channel Estimation (CE) method for wireless communications, which plays an important role in Industrial Internet of Things (IIoT) applications. For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (MIMO-FBMC/OQAM) systems, a Distributed Compressed Sensing (DCS)-based CE approach is studied. A distributed sparse adaptive weak selection threshold method is proposed for CE. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and CE is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

Nowadays, the Internet of Things (IoT) is inseparable from the industrial revolution 4.0 and society 5.0. IoT allows all aspects to be connected at the same time. In industrial processes, the IoT controls the actuator and monitors the behavior system. Industrial IoT (IIoT) connected the Human Machine Interface (HMI) Haiwell C7S with the actuator (Schneider Altivar 12) and the controller (OMRON CP1E-NA20DRA). The communication between the variable speed drive and Haiwell C7S using Modbus protocol is connected with RS485. Schneider Altivar 12 is a variable-speed drive that controls the Induction Motor (IM) rotor speed using the conventional V/F method. The scalar control of IM controls supply frequency and voltage simultaneously. The success parameter of the proposed systems is a rotor speed response and voltage in various supply frequencies. The results show that the 5 Hz supply frequency makes the absolute error of rotor speed response 11.43% for tachometer measurement, 0.67% from VSD data, and 12.67% for rotary encoder measurement. This absolute error will decrease significantly when the supply frequency exceeds 20 Hz, or the rotor speed response exceeds 1200 rpm. The changes in the supply frequency change the voltage's magnitude. The voltage will increase proportionally, along with the increase in supply frequency.

In our rapidly globalizing and digitizing world, small and medium-sized manufacturing enterprises (SMEs) face significant challenges that compel them to adopt a co-opetition strategy—a blend of competition and collaboration. Despite their potential benefits, the high failure rates and unmet expectations of co-opetition networks highlight a significant gap in the empirical frameworks for establishing and maintaining these networks. This research seeks to address these gaps by developing a framework that enhances value cocreation in the Portuguese ornamental stone sector, integrating the latest developments in the Industrial Internet of Things (IIoT), Service-Dominant Logic (S-D Logic), and service science. Question: How can a framework integrating IIoT, S-D Logic, and service science enhance value cocreation and manage co-opetition among SMEs in the Portuguese ornamental stone sector? Methods: Theoretical insights and practical applications were synthesized to develop and validate a comprehensive co-opetition framework. This framework was tested through an experimental pilot project in the Portuguese ornamental stone sector, leveraging IIoT. Results: The implementation of the framework demonstrated significant operational efficiency, including enhanced performance, reduced production variance, and better resource utilization, indicating that integrating IIoT within co-opetition networks can effectively support SMEs. Conclusions: This study confirms the transformative impact of embedding IIoT in co-opetition networks, offering a replicable and scalable framework for other sectors. This framework addresses the empirical gap and aligns with broader socio-economic goals, setting the stage for further research into its applicability and potential across diverse industrial environments.

This paper concerns the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. To identify the relevant technologies and functionalities, research was carried out in both the descriptive and statistical literature. As a result, both technologies that are currently the most widely distributed, as well as eleven IT functionalities relevant to mechanical engineers, were identified and described in terms of their application and implementation. Furthermore, a knowledge gap was identified in the area of industry transfer in the field of Industry 4.0/digitalization.

Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3MSF) for the TSCH link layer, which, combined with previous work on the routing layer, results in a 6TiSCH architecture able to dynamically exploit modern multi-modal hardware on a per-link basis through variable-duration timeslots, simultaneous transmission, and routing metric normalization. This paper describes, in great detail, its design and discusses the rationale behind every choice made. Finally, we evaluate three basic scenarios through simulations, showcasing both the functionality and flexibility of our 6TiSCH implementation.

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks’ data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system’s development and validation processes. Rigorous validation confirms the system’s accuracy, performance, and security through an experimental testbed. The results demonstrate the system’s dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system’s robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium. [ABSTRACT FROM AUTHOR]

With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments. [ABSTRACT FROM AUTHOR]

In a constantly changing world, information stands as one of the most valuable assets for a manufacturing site. However, exchanging information is not a straightforward process among factories, and concerns regarding the trustability and validation of transactions between various stakeholders have emerged within the context of micro-factories. This work presents an architecture designed to enable information exchange among heterogeneous stakeholders, taking advantage of the cloud infrastructure. It was designed to enable the use of several tools, connected through a middleware system deployed on the cloud. To demonstrate the potential of this architecture, a platform was instantiated, and two use cases—designed to accurately represent real manufacturing sites—were implemented. [ABSTRACT FROM AUTHOR]

Due to the recent rapid development of the ICT (Information and Communications Technology) field, the industrial sector is also experiencing rapid informatization. As a result, malware targeting information leakage and financial gain are increasingly found within IIoT (the Industrial Internet of Things). Moreover, the number of malware variants is rapidly increasing. Therefore, there is a pressing need for a safe and preemptive malware detection method capable of responding to these rapid changes. The existing malware detection method relies on specific byte sequence inclusion in a binary file. However, this method faces challenges in impacting the system or detecting variant malware. In this paper, we propose a data augmentation method based on an adversarial generative neural network to maintain a secure system and acquire necessary learning data. Specifically, we introduce a digital twin environment to safeguard systems and data. The proposed system creates fixed-size images from malware binaries in the virtual environment of the digital twin. Additionally, it generates new malware through an adversarial generative neural network. The image information produced in this manner is then employed for malware detection through deep learning. As a result, the detection performance, in preparation for the emergence of new malware, demonstrated high accuracy, exceeding 97%. [ABSTRACT FROM AUTHOR]

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is designed to leverage the smart industries' performance with information and communication technologies. The functions of the industrial devices and the process control are managed using IoT components for automated production. However, external IoT support increases the risk of vulnerabilities that intervene in the industries' operational process. In this article, Blockchain-aided Secure Process Control is introduced. This proposal focuses on mitigating the intervening external vulnerabilities in the sequential task processing intervals. The resilient and vulnerable process controls are differentiated based on their allocation-to-outcome factor. This factor serves as the key to classify the nature of the process control. For this purpose, classification tree-based learning is employed. The outcome of the classification tree is useful in blockchain interactions with the process controller. The process controller is administered using the blockchain system based on the classification output. The harmonized process of blockchain and outcome factor-based classification improves the job delivery rate under controlled time. The metrics latency, adversary detection, false rate, and job failures are used for analyzing the proposed method's performance. The proposed method achieves an 11.9% high allocation rate, 14.14% less adversary impact, and 16.45% less processing time for different tasks. [ABSTRACT FROM AUTHOR]

The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoTs) has drawn significant attention in the industry. Among its rich applications, the field's video surveillance deserves particular interest due to its advantage in better understanding network control. However, existing decoding methods are limited by the video coding order, which cannot be decoded in parallel, resulting in low decoding efficiency and the inability to process the massive amount of video data in real time. In this work, a parallel decoding framework based on the speculative technique is proposed. In particular, the video is first speculatively decomposed into data blocks, and then a verification method is designed to ensure the correctness of the decomposition. After verification, the data blocks having passed the validation can be decoded concurrently in the parallel computing platform. Finally, the concurrent decoding results are concatenated in line with the original encoding order to form the output. Experiments show that compared with traditional serial decoding ones, the proposed method can improve the performance by 9 times on average in the parallel computing environment with NVIDIA Tegra 4 chips, thus significantly enhancing the real-time video data's decoding efficiency with guaranteed accuracy. Furthermore, proposed and traditional serial methods obtain almost the same peak signal-to-noise ratio (PSNR) and mean square error (MSE) metrics at different bit rates and resolutions, showing that the introduction of the speculative technique does not degrade the decoding accuracy. [ABSTRACT FROM AUTHOR]

Both technical and commercial businesses have become increasingly interested in blockchain technology. The Internet of Things (IoT) was established out of the interconnectedness of numerous new technologies, such as wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing settings and activities have been successfully altered into the fourth industrial revolution, known as Industry 4.0, by applying cutting-edge technologies such as cloud computing (CC), cloud service provider (CSP), Information communication technologies (ICT), and Enterprise Model, as well as other technological advancements. Data management is characterized as acquiring data to make better business decisions; data about a corporation is processed, secured, and stored. In the early notion, there were connected contrivances and Machine-to-Machine (M2M) interactions, and transaction data is recorded on the Blockchain. Security is a challenging subject that must be carefully considered throughout a CSP's design and development phases. By focusing on such issues while taking into account the traditional characteristics of IoT/ IIoT-predicated environments, we proposed a Secure and Distributed Framework for Resource Management (SDFRM) in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system, the dynamic and trust-based Distributed Management Framework (DMF) of shared resource access, in this research article. Furthermore, to offer strong privacy assurances for Access Control (AC)-equivalent actions, a privacy-preserving approach is devised and implemented into the Distributed Management Framework (DMF). Based on blockchain technology and peer-to-peer networks, the recommended DMF provides for dynamic access control and system governance without relying on third-party vulnerabilities. A privacy-preserving technique is presented and implemented into the DMF to give adequate privacy safeguards for AC-related processes. With an average throughput of 98.15 percent, our proposal exceeds the Multichannel Blockchain regarding successful storage transactions. [ABSTRACT FROM AUTHOR]

Regulatory Technology (RegTech) is an emerging set of computing and network-based information systems and practices intended to enhance and improve regulatory compliance processes. Such technologies rely on collecting exclusive information from the environment and humans through automated Internet of Things (IoT) sensors and self-reported data. The key enablers of RegTech are the increased capabilities and reduced cost of IoT and Artificial Intelligence (AI) technologies. This article focuses on a survey of RegTech, highlighting the recent developments in various sectors. This work identifies the characteristics of existing implementations of RegTech applications in the financial industry. It examines the critical features that non-financial industries such as agriculture must address when using such technologies. We investigate the suitability of existing technologies applied in financial sectors to other industries and the potential gaps to be filled between them in terms of designing information systems for regulatory frameworks. This includes identifying specific operational parameters that are key differences between the financial and non-financial sectors that can be supported with IoT and AI technologies. These can be used by both producers of goods and services and regulators who need an affordable and efficient supervision method for managing relevant organizations. [ABSTRACT FROM AUTHOR]

The industry is in continuous evolution in the context of the Industrial Internet of Things (IIoT) and Industry 4.0 requirements and expected benefits. Some sectors allow higher reconfiguration dynamics considering the interference capabilities and process/equipment renewals, but others have considerable inertia that is many times justified. In most encountered situations, the reality confirms that the industry is struggling with new demands such as interoperation and efficiency improvements. The water industry makes no difference, being a sector with critical infrastructures and highly varied subsystems, where invasive interference in legacy solutions tends to be avoided. Following previous successful footsteps in researching a proactive decentralized historian, the current work focuses on a case study that refers to a water treatment and distribution facility that has been operated for several years, and the current operating regime was established by local operators following accumulated observations, restrictions, and response strategies. The proactive historian was tailored for the current case study, and it was applied and tested in the suboptimal functioning scenario where the water sources configuration was manually selected and used for water availability and energy efficiency, but without assuming current/future failures or different water demands. The proposed low-cost historian targeted to improve the functioning and operation of the water facility considering energy efficiency and other impacting outcomes of the current strategy and to establish an automatic functioning regime in a completely non-invasive manner towards the local legacy solution. The results were satisfactory, proving that the historian is able to adapt to a particular and suboptimal functioning real industrial scenario, establish recipes in a process-aware manner, and interoperate with the local legacy solution in order to apply improving actions. [ABSTRACT FROM AUTHOR]

The requirements of Industry 4.0 determine the necessity to change thinking in the field of production development, adopted management methods and modernisation of production resources. When planning the implementation of a new production system (or retrofit), it is possible to use the RAMI 4.0 reference model, which was published in April 2015 by the VDI/VDE Society Measurement and Automatic Control. A key aspect of modern industrial systems is connectivity and trouble-free data exchange. In the case of data exchange, the basic element holding back the development of Industry 4.0 is the lack of standardisation, as well as the lack of interoperability between IIoT network nodes. Modern IIoT applications require high network throughput, low latency and reliability. In view of such guidelines, efficient communication standards and specialised equipment are required. Edge Computing is one of the most important technology trends of the 21st century that will play a key role in the IIoT market. Due to the diversity of available technologies and solutions, no universal standards have been developed to date that can be referred to when planning, building and implementing new applications. The article presents an overview of the most popular industrial communication protocols and their systematisation in terms of meet the requirements for IIoT devices. [ABSTRACT FROM AUTHOR]

Increasing industrial development and digital transformations have given rise to a technology called Digital Twin (DT) that has the potential to break the barrier between physical and cyberspace. DT is a virtual and dynamic model enabled through a bidirectional data flow that creates high-reliability models with interconnection and fusion between the physical and digital systems for full integration. In smart manufacturing, this technology is increasingly used in research and industry. However, the studies conducted do not provide a definition or a single integrally connected model. To develop the Digital Twin shown in this research, the literature was reviewed to learn about the enabling technologies and architectures used at the industrial level. Then, a methodology was used to obtain the physical process information, create the digital environment, communicate the physical environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real-time to obtain the digital twin supported with augmented reality. The system was implemented in the MPS-500 modular production station that has industrial sensors and actuators. The virtual environment was designed with Blender and Vuforia to create the augmented reality environment. In the proposed methodology, robust devices (field and control level) and low-cost embedded systems were used for the creation and communication of the virtual environment (monitoring and control); for the joint work of these technologies, they were carried out through the use of the following protocols: Open Platform Communications United Architecture (OPC UA), Ethernet, and machine to machine (M2M), with which a communication was achieved between the different levels of the automation pyramid. The results show that the proposed methodology for the implementation of the DT allows bidirectional communication between the physical and virtual environment and can also be visualized with the support of AR, thus providing its characteristics to the proposed DT. Digital Twin is an essential factor in creating virtual environments and improving applications between the real and digital world, establishing a bidirectional communication through the Ethernet protocol, with a communication time of approximately 100 ms. This technology interacts with the virtual environment and performs mappings, thus achieving timely and dynamic adjustment. This improves data management and production and incorporates process simulation and physical control in real-time, allowing to execute and trigger actions in the physical equipment simultaneously. [ABSTRACT FROM AUTHOR]

The Internet began with a simple concept – connecting computer systems together to proportion facts in numerous ways. Since that humble beginning, humans have started out to connect extra devices to the Internet. That’s the premise of the time period Internet of Things (IoT). The Internet of Things extends net connectivity beyond traditional devices like computing device and pc computer systems, smartphones and pills to an ever-growing community of normal matters that utilize embedded generation to speak and interact with the external surroundings, all via the Internet. It’s the inter-networking of physical gadgets (“connected gadgets” and “clever devices”) that enable those objects to accumulate and exchange statistics. the integration of sensors with the cloud which is the highlighting aspect in the future IOT. This concept are very much importance in developing smart cities. These smart cities improve the quality of living in cities. In one of the famous surveys we found that, up to 2020 26 billion devices will be a part of Internet of Things. Normally, devices linked to the Internet to ship records and acquire instructions/statistics the use of wireless technologies, which includes Satellite, WiFi, Bluetooth, LPWA, 2G, 3G and 4G, but it may additionally be related through fiber, as an instance. These gadgets, linked to the Internet, are a part of the IoT solution. If you pick out the incorrect technology, your device may not have a way to connect with the internet and facts will not arrive in the cloud infrastructure. Imagine an IoT device received with 3G generation to be used inside the middle of a forest, wherein most effective satellite tv for pc connection are available? It might be just a new rock in the surroundings, however no statistics been send or obtained. [ABSTRACT FROM AUTHOR]

This review article is designed for SME owners and directors to understand better what blockchains are and how they work and to support them to start an entirely new blockchain or fork an existing one and create a new token. The article presents technology bases and how blockchains can serve SMEs and foreseeable challenges that could jeopardize the achieved results. Particular attention the authors paid to blockchain applications, transaction cost reduction, and sensitive business information security. Even more attention the authors gave to the challenges connected with digital infrastructures and business practices, investments, lack of interoperability, lack of skills, and legal aspects. Chapter four discusses the creation of blockchains for SMEs from the aspects of choosing a type of blockchain, choosing of consensus mechanism, a platform selection, nodes design, operating systems, instances design, application programming interfaces, and user interfaces, and the blockchain finalizing. In chapter five, Conclusions, the authors draw conclusions based on the work presented in the article. [ABSTRACT FROM AUTHOR]

The smart manufacturing ecosystem enhances the end-to-end efficiency of the mine-to-market lifecycle to create the value chain using the big data generated rapidly by edge computing devices, third-party technologies, and various stakeholders connected via the industrial Internet of things. In this context, smart manufacturing faces two serious challenges to its industrial IoT big data integrity: real-time transaction monitoring and peer validation due to the volume and velocity dimensions of big data in industrial IoT infrastructures. Modern blockchain technologies as an embedded layer substantially address these challenges to empower the capabilities of the IIoT layer to meet the integrity requirements of the big data layer. This paper presents the trusted consortium blockchain (TCB) framework to provide an optimal solution for big data integrity through a secure and verifiable hyperledger fabric modular (HFM). The TCB leverages trustworthiness in heterogeneous IIoT networks of governing end-point peers to achieve strong integrity for big data and support high transaction throughput and low latency of HFM contents. Our proposed framework drives the fault-tolerant properties and consensus protocols to monitor malicious activities of tunable peers if compromised and validates the signed evidence of big data recorded in real-time HFM operated over different smart manufacturing environments. Experimentally, the TCB has been evaluated and reached tradeoff results of throughput and latency better than the comparative consortium blockchain frameworks.