Industrial Networks Research Articles

An improved energy saving clustering method for IWSN based on Gaussian mutation adaptive artificial fish swarm algorithm

The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS).

Dynamic analysis and application of network structure control in risk conduction in the industrial chain

According to control theory, a dynamical system is controllable if, with a suitable choice of inputs, it can be driven from any initial state to any desired final state within a finite time. Most dynamic characteristics of real networks are nonlinear, so achieving target control is more practical and necessary. The network’s control energy is also a problem that must be considered. Whether and how to control the complex system of the industrial chain has high theoretical and practical significance. In this study, we use the GARCH model, DCC model, and network structure control theory comprehensively to study the price fluctuation risk of the mining stock market from the perspective of the industry chain and network control dynamics and obtain interesting results. (1) Risk conduction among stocks has a prominent industry-driving effect, and the risk conduction ability of upper and middle stocks is stronger. (2) The risk regulation cost, time cost, and node number cost of the whole-industry chain are all higher than those of the two-tier chain, which indicates that the correlation complexity of the network has a positive relationship with risk control. (3) Key risk nodes play an essential role in risk control, so monitoring key stocks from the industrial chain perspective is necessary to control risks in time. This work can provide valuable suggestions for market regulators and policy-makers in terms of risk management and control.

High-temperature heat pumps in industrial heating networks: A study on energy use, emissions, and economics

Industrial heat significantly contributes to global primary energy use and primarily relies on fossil-fuel combustion. Recovering residual heat in the industry offers a means to reduce overall energy use. However, the temperature and amount of residual heat available varies widely across industrial sites. Some may have a large amount of residual heat available at relatively high temperatures, while others may not have any residual heat available. Furthermore, for most industries, the residual heat available is at temperatures below 100 °C, while the heat demands are at higher temperatures. Hence, clustering these industries by industrial heating networks, using high-temperature heat pump integration, could offer a promising solution. Research on this concept regarding energy use, emissions and economics is however lacking in the literature. This study however distinguishes and subsequently compares three different heat network configurations in terms of their energy use, carbon emissions and financial appraisal. These configurations include a ‘consumer based heat upgrading network’, a ‘supplier based heat upgrading network’ and a ‘supplier based heat upgrading network with an additional hot water network’. For this purpose a generic methodology is developed, using first and second law principles completed with empirical data for performance and costs. The methodology is applied to data collected from ten companies clustered within the North Sea Port, Ghent (Belgium). The results indicate that the first configuration exhibits the most efficient use of energy and consequently also has the lowest carbon emissions. In addition it also has the lowest levelized cost of heat. This configuration shows, depending on maximum supply temperature of the heat pump, a potential reduction in carbon emissions ranging from 70 % to 80 % in comparison to natural gas boilers. Considering low gas prices, a positive financial appraisal is difficult without carbon taxation. On the other hand, an evaluation during the energy crisis of 2021–2022 indicates that the even without carbon taxation, the levelized cost of heat decreases by 19 % compared to a gas boiler at a maximum heat pump temperature of 160 °C. It was also found that in scenarios of dynamic energy prices a hybrid configuration of a consumer based heat upgrading network and a natural gas boiler could lower the LCOH compared to the individual solutions, by up to 7.6 % compared to the best individual solution. This is done by activating the technology with the lowest operational cost in each time frame.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Network Integration as a Tool for Sustainable Business Development

Business enterprises are required to adopt new economic models and business strategies in line with global trends such as the green agenda, the shortening of innovation cycles, digitalization (networking platforms) and socio-economic security. The models of network integrations (clusters, industrial symbioses, innovation ecosystems, etc.) are developing faster than other business models. Networking allows businesses to synergetically, based on the principles of collaboration, trust and self-organization, increase intellectual, innovative, social, and environmental values, which leads to economic growth and sustainable development. Through networking integration, the business performance of all participants in multi-company projects can be improved, regardless of their size and activities. The purpose of this study is to develop a methodological framework for assessing the synergistic effect and the level of collaborative maturity of participants in network industrial integration. This paper provides a comprehensive analysis and evaluation of network business integration efficiency. The authors’ methodology is based on the AHP hierarchy method, expert evaluation and Harrington’s level of desirability. The methodology is validated on Russian industrial integrations: industrial cluster of Nizhny Novgorod region; pipeline valve manufacturing cluster; South Ural industrial cluster producing spare parts and assembly units for roadbuilding, construction and agricultural vehicles. The results show that the level of collaborative maturity of business partners and the synergies from network integration effect the sustainability of business development. Recommendations for businesses are provided to improve their network integration and collaborative maturity. Furthermore, the findings allow the authors to define directions for further research, considering the interests of the business community, regional authorities, and financial institutions, and provide a framework for researchers to investigate the link between industrial network integration and economic growth.

Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China.

To date, the differentiated requirements for network performance in various health care service scenarios-within, outside, and between hospitals-remain a key challenge that restricts the development and implementation of digital medical services. This study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services. Based on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network. The construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios-namely, mobile ward rounds, remote first aid, and remote ultrasound-were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective. The proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally.

Simulation-based evaluation of advanced threat detection and response in financial industry networks using zero trust and blockchain technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

The Role of Business Incubators in Encouraging Maritime Technology Start-up Innovation

This study examines the critical role of business incubators in driving innovation in maritime technology start-ups. With the growing need for innovative technology solutions in the maritime industry, business incubators have emerged as a key catalyst in developing and introducing new ideas to the market. The study used a qualitative approach, involving in-depth interviews with 30 stakeholders, including start-up founders, incubator managers, and maritime industry experts. The results showed that business incubators provide critical support in the form of access to funding, expert mentoring, research and development facilities, and extensive industry networks. The findings also revealed that incubators that focus on the maritime sector are more effective in helping start-ups overcome industry-specific challenges. The study highlights the importance of collaboration between incubators, the maritime industry, and educational institutions to create an ecosystem that supports innovation. Implications of this study include recommendations for policymakers and industry players in designing more effective incubation programs to drive maritime technology innovation.

Industrial Production Networks and Small Towns: A Case Study from Algeria

This paper investigates the conditions and consequences of integrating small towns into industrial production networks. It is based on empirical research conducted in Algeria, a hydrocarbon-dependent rentier economy characterized by significant regional inequalities and the political aims of economic diversification and spatial rebalancing. Elaborating the case study of a state-owned cement factory in the small town of Sigus, the research provides insights into the multiple roles of the state in shaping production network integration and the characteristics of small towns as economic locations. The methodology combines secondary data and information with primary research based on semi-structured interviews. It reveals the importance of a multi-scalar regional framework in production network integration, whereby national factors played a key role due to the centralized Algerian state, the state-owned character of the investing company, and the shortcomings of the small town’s local environment. It emphasizes the contradictory impacts of production network integration in economic, social, and environmental terms, primarily on a local level. These contradictions underscore the necessity for critical evaluations to maximize the benefits of production network integration while mitigating its adverse effects. They also call for the more consistent involvement of the local community in similar economic development decisions. Notably, this research contributes significantly to the existing body of literature by addressing the underexplored topic of integrating small towns into production networks within the Algerian context. Doing so offers a more nuanced understanding of the particular economic, social, and environmental dynamics at play in these locations, thereby enriching the discourse on economic development strategies for small towns in rentier economies like Algeria.

Filtering ‘3–2’ industrial symbiosis networks at a carbon-intensive cluster in a small island developing state to reuse CO2 and water

Small island developing states (SIDS) face internal and external pressures for more sustainable manufacturing e.g., economic and ecological water provision, and anti-carbon leakage tariffs. As SIDS have special developmental challenges, locally appropriate strategies are needed. In one of these SIDS, Trinidad and Tobago, is a carbon-intensive industrial cluster of global standing, the Point Lisas Industrial Estate (PLIE). So, to investigate ‘3–2’ industrial symbiosis (IS) on the PLIE, a simple enterprise input-output MILP model of a representative IS network was developed. Different quality wastewater streams and high-purity process CO2 from ammonia processes were selected as materials to be reused in: existing petrochemical plants, a mineral carbonate factory and a propylene carbonate plant. To filter the IS relations, economic and environmental objectives were set for each material. Combining economic objectives left a tri-objective problem, which was resolved with ε-constraint optimization and multi-criteria decision-making methods. Kleinberg's hub and authority scores were found to give beneficial insight into the solved IS networks. Potential revenue-generating opportunities were uncovered for sharing and reusing water and process CO2. The results suggest adding two proposed carbonate factories could increase the mass of CO2 reused in the cluster by 10.4 % and mitigate releasing 5 Tg/y of rejected desalination brine.

How Wanghong Live Streamers Affect Online Users’ Purchasing Behavior on Live Streaming Platforms: A PLS-SEM Approach

ABSTRACT Considering the distinct effects of the Wanghong group in retaining online consumers, more and more live streamers tend to accept the Wanghong economy and aim to become Wanghong live streamers. Although existing scholars have investigated the relationship between live streamers and online consumers, limited studies have discussed how Wanghong live streamers affect online consumers’ watching interest and lead to their final purchasing behaviors. To explore their purchasing decision-making process, this paper draws on the Attention-Interest-Desire-Action (AIDA) model and divides the process into Attention, Interest, Desire, and Action units. Through the data analysis based on the partial least squares path modeling and variance-based structural equation modeling (PLS-SEM), both attractive appearance and social interaction designed by Wanghongs positively affect online consumers’ watching interest, generate their purchasing desires, and cause their final purchasing behaviors. The research results are helpful for related scholars and network industries to understand the interactive mechanism between online consumers and Wanghongs. Exploring the distinct effect of this group will be beneficial in driving the development of the Wanghong economy and improving the environment of live streaming economy.

Exponential Random Graph Model Perspective: Formation and Evolution of a Collaborative Innovation Network in China’s New Energy Vehicle Industry

In light of the crucial role of collaborative R&D in advancing technology within the new energy vehicle industry, this study seeks to explore ways to overcome the barriers to technological innovation by establishing an effective collaborative innovation network. Utilizing joint patent-authorized data from China’s new energy vehicles between 2005 and 2019, the collaborative innovation network was developed, and the Exponential Random Graph Model (ERGM) was employed to analyze its formation and evolution mechanisms. The results indicate that the network has undergone significant expansion, closely linked to strong national policy support and the active involvement of innovation participants. The network exhibits effects of expansion, transfer, and closure. External attribute analysis revealed the Matthew effect and geographical compatibility effect and found that organizational compatibility tends to foster complementary cooperation. The findings offer insights into optimizing collaborative innovation networks in the NEVs industry and suggest strategies for policymakers and industry players to promote collaborative innovation.

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

Characterization of Industrial Carbon Emission Dynamics and Network Structure Evolution in China

Given that industry is the major source of carbon emissions, clarifying the carbon transfer regularity triggered by industry production linkage is of great importance in realizing the synergistic development of industry to reduce pollution and carbon emissions. Based on the perspective of responsibility allocation, the study accounted for the industry carbon emissions and constructed a carbon transfer weighted directed network model and applied the social network analysis method to explore the structure of the industry carbon transfer network and its evolution characteristics from 2005 to 2020. The results showed that： ① The overall carbon emissions of industries from 2005 to 2020 presented a rapid growth trend and large differences occurred in emissions between industries. ② The scale of the carbon transfer network was expanding rapidly and the characteristics of the small-world network were significant. ③ The community structure of the carbon transfer network was obvious, including four optimal associations： high carbon emission, carbon transmission, carbon endogenous, and high spillover. ④ The construction industry and the metal smelting and rolling processing industry were the core of the carbon transfer network structure, with strong network control. In view of the above characteristics, we proposed countermeasures and suggestions in terms of implementing the allocation of carbon emission reduction responsibilities and differentiated emission reduction measures for associations as well as focusing on the management of core nodes of the network, with the intent of providing cross-sectoral synergistic emission reduction ideas for the development of sustainable low-carbon transformation in China's industry sector.

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

CONSIDERATIONS ON THE ANALYSIS OF THE OPERATING CHARACTERISTICS OF THE HORIZONTAL SYNCHRONOUS MOTORS EQUIPPING THE HYDRO AGGREGATES IN HIGH-POWER ENERGY PUMPING STATIONS

In this paper, the authors present modelling and analysis of the operating characteristics of the synchronous motor with electromagnetic excitation, for driving the centrifugal pump that equips hydro aggregates in high-power energy pumping stations, motor powered from fixed or adjustable voltage sources. Two operating scenarios were considered, namely: the stationary operation of the synchronous motor fed from an ideal, controllable voltage source and the case of its supply from the industrial network. For solving, algorithms and calculation programs developed in the Mathcad programming environment were designed. For example, a representative case study of one of the three identical hydro aggregates from one of a high-power energy pumping station of an existing hydropower facility in Romania was chosen. The hydro aggregate is driven by a horizontal synchronous motor with electromagnetic excitation powered at medium voltage (6 kV).

Network Robustness Prediction: Influence of Training Data Distributions.

Network robustness refers to the ability of a network to continue its functioning against malicious attacks, which is critical for various natural and industrial networks. Network robustness can be quantitatively measured by a sequence of values that record the remaining functionality after a sequential node- or edge-removal attacks. Robustness evaluations are traditionally determined by attack simulations, which are computationally very time-consuming and sometimes practically infeasible. The convolutional neural network (CNN)-based prediction provides a cost-efficient approach to fast evaluating the network robustness. In this article, the prediction performances of the learning feature representation-based CNN (LFR-CNN) and PATCHY-SAN methods are compared through extensively empirical experiments. Specifically, three distributions of network size in the training data are investigated, including the uniform, Gaussian, and extra distributions. The relationship between the CNN input size and the dimension of the evaluated network is studied. Extensive experimental results reveal that compared to the training data of uniform distribution, the Gaussian and extra distributions can significantly improve both the prediction performance and the generalizability, for both LFR-CNN and PATCHY-SAN, and for various functionality robustness. The extension ability of LFR-CNN is significantly better than PATCHY-SAN, verified by extensive comparisons on predicting the robustness of unseen networks. In general, LFR-CNN outperforms PATCHY-SAN, and thus LFR-CNN is recommended over PATCHY-SAN. However, since both LFR-CNN and PATCHY-SAN have advantages for different scenarios, the optimal settings of the input size of CNN are recommended under different configurations.

A Reinforcement Learning Approach for D2D Spectrum Sharing in Wireless Industrial URLLC Networks

A Reinforcement Learning Approach for D2D Spectrum Sharing in Wireless Industrial URLLC Networks

Political Expression in Non-mainstream Metaphorical Music:Exploring the Metaphorical Musical Works of Hong Kong, China-based Artist Wong Yiu Ming

The intensification of authoritarian ideology and censorship in China has made it increasingly challenging to communicate politically sensitive messages through cultural products (Ye and Zhao, 2023). To navigate these constraints, it is crucial to examine the strategic use of political metaphors in music. This dissertation explores the political metaphors embedded in the musical oeuvre of Wong Yiu-Ming (Anthony Wong), a prominent artist from Hong Kong, China. Through a comprehensive analysis of imagery and textual content, this study investigates how these metaphors facilitate the expression of political orientations and assesses the impact of Wong's identity and actions on his audience. The findings suggest that non-mainstream music holds a distinct political significance compared to mainstream music. Metaphorical representations in non-mainstream music effectively challenge state power by circumventing censorship, leveraging the global industrial network, enabling political expression, and aligning with the artist's multifaceted identity.