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There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains...
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In the last decade, we have seen the uptake of connected solutions to support and improve manufacturing, warehousing and distribution operations in industry. However, deterministic and reliable wireless communication in large-scale, dynamic and mobile applications still remains one of the greatest challenges for the realization of the Digital Facto...
Industrial wireless sensor networks can facilitate the deployment of a wide range of novel industrial applications, including mobile applications that connect mobile robots, vehicles, goods and workers to industrial networks. Current industrial wireless sensor standards have been mainly designed for static deployments, and their performance signifi...
![Fig. 1. Hierarchical architecture in industrial wireless networks [7].](profile/Maria-Del-Carmen-Lucas-Estan/publication/330984401/figure/fig1/AS:725151591182337@1549901020854/Hierarchical-architecture-in-industrial-wireless-networks-7_Q320.jpg)
Industry 4.0 will interconnect and digitalize traditional industries to enable smart and adaptable factories that efficiently utilize resources and integrate systems. A key enabler of this paradigm is the communications infrastructure that will support the ubiquitous connectivity of Cyber-Physical Production Systems. The integration of wireless net...
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... Figure 2 illustrates the general layout of IIoT within Industry 4.0. This framework is made up of four main layers [13,14], including the physical resource layer, the network layer, the cloud layer and the application layer. The physical resources layer includes intelligent IoT devices such as sensors, actuators, manufacturing objects and facilities, and other objects related to industrial manufacturing and automation. ...
... To increase reliability, various approaches could be employed. In this context, authors in [13] have presented some of the approaches used to increase the reliability of wireless sensor networks used in industries such as redundancy, frequency-hopping and interference minimization. Furthermore, predictive maintenance using AI and machine learning models can foresee potential failures and schedule maintenance before they cause disruptions. ...
The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT.
... The strategic value of IoT in supply chain management is evident not only in operational efficiencies but also in creating competitive advantages. Companies that adopt IoT are better positioned to respond to market changes rapidly, personalize customer experiences, and develop resilient supply chains that can withstand disruptions (Li et al., 2017). These capabilities are essential in today's highly competitive and fast-paced business environment. ...
This review paper explores the pivotal role of the Internet of Things (IoT) in enhancing transparency and efficiency within supply chains. As global industries increasingly demand sustainability and streamlined operations, IoT emerges as a key enabler, providing unprecedented visibility and control. This paper synthesizes findings from a broad range of literature, including case studies and empirical research, to assess the impact of IoT on supply chain dynamics. The analysis reveals that IoT facilitates real-time tracking of goods and data flow, significantly increasing transparency across all supply chain tiers. This enhanced visibility aids in proactive decision-making, compliance with regulatory standards, and adherence to sustainability criteria. Additionally, the integration of IoT is shown to streamline operations, reduce costs, and elevate customer satisfaction through more reliable and responsive service delivery. The paper concludes that while the adoption of IoT drives considerable improvements in supply chain management, it also introduces challenges such as the need for substantial infrastructure investment and ongoing concerns regarding data security and privacy. Recommendations for practitioners include adopting a phased IoT integration strategy and prioritizing robust cybersecurity measures to safeguard sensitive information. This review provides a comprehensive overview of IoT's capabilities and outlines strategic approaches to harness its full potential for transforming supply chain operations.
... The evolution of customer relationships can, therefore, have impacts on the value proposition BM element. Thus the issues in one BM element can lead to issues in another (Li et al., 2017). ...
While the Industrial Internet of Things (IIoT) holds much promise, there is a mismatch between its potential and companies capturing value from investments in IIoT. Indeed, even when companies recognize the value of IIoT, they do not necessarily know how to grasp related opportunities and are challenged in developing a suitable business model. Accordingly, to alleviate roadblocks to capturing value from IIoT, in this paper we address the challenge of identifying suitable business models in the age of the industrial metaverse. We do so through an extensive review and classification of main IIoT business model archetypes that are successful in practice. In particular, we conduct a content analysis of IIoT projects based on over 2000 articles in industry trade magazines and newspapers. Our analysis identifies four distinct business model archetypes in the context of IIoT, viz. IIoT digical, IIoT service-centered, IIoT data-driven, and IIoT platform, and further explores the challenges that need to be addressed to ensure that companies can capture value from their IIoT initiatives. We explore appropriate
contexts for these business model archetypes, and, in doing so, we provide actionable guidance for industrial (marketing) managers seeking to position their IIoT offerings and maximize their value.
... It enables real-time data exchange, remote monitoring, and control of industrial processes, facilitating seamless integration and interoperability across different systems and components. Ethernet switches, routers, and cables are commonly used to build robust industrial networks that can withstand harsh environmental conditions and operate reliably in mission-critical applications [69]. ...
The advent of Industry 4.0 has catalyzed a transformative shift in advanced manufacturing, driven by the integration of cutting-edge technologies in connectivity, automation, and data exchange. This review delves into the pivotal role played by these technological pillars in reshaping the modern industrial landscape. In the realm of connectivity, emphasis is placed on the evolution towards ultra-fast and reliable communication networks, exemplified by the emergence of 5G technology and the proliferation of edge computing solutions. Automation, another cornerstone of Industry 4.0, is explored through the lens of artificial intelligence (AI) and robotics, showcasing how intelligent automation systems are revolutionizing production processes and enhancing operational efficiency. Moreover, the review investigates the critical importance of data exchange in facilitating seamless interoperability and information flow across interconnected manufacturing ecosystems. Key advancements in data exchange technologies, including blockchain and federated learning, are examined for their potential to ensure data integrity, privacy, and security. By synthesizing insights from these three domains, this review provides a comprehensive overview of the technological landscape driving the advancement of Industry 4.0 in advanced manufacturing. It underscores the transformative potential of connectivity, automation, and data exchange in fostering innovation, agility, and sustainability across industrial sectors, while also highlighting the challenges and opportunities that lie ahead in harnessing these technologies to their fullest extent.
... Target coverage problem, as a fundamental issue in wireless sensor networks, has wide applications, such as agriculture monitoring (Mois et al. 2017), environmental applications (Lombardo et al. 2017) and industrial inspection (Li et al. 2017). It has drawn attention of numerous researchers. ...
In this paper, we study approximation algorithms for the problem of maximum weighted target cover with distance limitations (MaxWTCDL). Given n targets T=t1,t2,…,tn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=\left\{ t_{1},t_{2},\ldots ,t_{n}\right\} $$\end{document} on the plane and m mobile sensors S=s1,s2,…,sm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S=\left\{ s_{1},s_{2},\ldots ,s_{m}\right\} $$\end{document} randomly deployed on the plane, each target ti\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_i$$\end{document} has a weight wi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_{i}$$\end{document} and the sensing radius of the mobile sensors is rs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{s}$$\end{document}, suppose there is a movement distance constraint b for each sensor and a total movement distance constraint B, where B>b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B>b$$\end{document}, the goal of MaxWTCDL is to move the mobile sensors within the distance constraints b and B to maximize the weight of covered targets. We present two polynomial time approximation algorithms. One is greedy-based, achieving approximation ratio 12v\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{2v}$$\end{document} in time O(mn2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(mn^2)$$\end{document}, where . The other is LP-based, achieving approximation ratio 1v(1-e-1)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{v}(1-e^{-1})$$\end{document} in time TLP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{LP}$$\end{document}, where TLP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{LP}$$\end{document} is the time needed to solve the linear program.
... I4.0-Tech facilitates vertical and horizontal integration by sharing real-time information among different entities within the production chain. This integration enables seamless communication and collaboration among various members of the manufacturing ecosystem, resulting in enhanced efficiency and productivity (Fatorachian & Kazemi, 2018;Li et al., 2017). These technologies can be classified into three streams, as Stentoft and Rajkumar (2020) 3. DPT is categorized as BDA, IoT, CC, simulation, and others. ...
The intersection of Industry 4.0 technology (I4.0‐Tech) with sustainability (SUS) remains largely unexplored, particularly due to its interactions with supply chain visibility (SCV) and circular economy practices (CEP). To fill this gap, our study empirically examines I4.0‐Tech's impact on SUS through SCV and CEP. We utilized structural equation modeling to test the hypothesized relationships using data from a survey of 344 medium and large Chinese firms. The empirical findings support the indirect role of I4.0 through SCV, and CEP. I4.0‐Tech enables real‐time material and product tracking for SCV. Optimizing material utilization, fostering closed‐loop supply chains, and allowing circular product design enhance CEP. Furthermore, our findings show a positive relationship between SCV, CEP, and SUS. This highlights the need for robust supply chain capabilities in enabling I4.0‐Tech to drive SUS. This paradigm emphasizes recalibrating approaches to SCV and CEP‐focused frameworks to easily include and exploit I4.0‐Tech. This study contributes to the literature and gives actionable insights for managers and policymakers wanting to synergize I4.0‐Tech, SCV, and CEP in industrial processes to promote sustainability goals and a sustainable future.
... In recent years, the swift progress in distributed computing and micro-electromechanical systems (MEMS) has propelled wireless sensor networks into numerous domains, encompassing military [1], intelligent architecture [2], and industrial sectors [3,4]. To observe diverse phenomena like network traffic, sensors are equipped with sensing, computing, communication, and additional functionalities. ...
Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, we present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spatial features of each grid with information from its correlated grids, generating spatial predictions within the spatial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and cellular data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.
... By virtue of the digital object like process analysis and optimization, in silico process model could be developed and updated in sync with data from the physical component to provide guidance for the actual procedures and further improve the overall efficiency and mitigate the risks in operation and maintenance. In general, the applications of the digital twins have demonstrated their capabilities in facilitating remote sensing, real-time monitoring, data acquisition, information exchange, process visualization, and process knowledge extraction [4,[13][14][15][16]. Integrated DTs have been applied in various industries including aerospace, semiconductor, energy production, smart product manufacturing, automotive transportation, and healthcare [5,7,10]. ...
The capabilities of digital twins (DT) and the successes they have demonstrated in various industries have attracted significant attention from the biopharmaceutical manufacturing field. The realization of DT could help reduce cost, improve productivity, maintain high and constant product quality, and meet Quality-by-Design (QbD) guidelines. Nonetheless, the development of DT is still at its infancy stage and an integrated DT in biopharma is rarely reported. To assist with further advancement in the development of DT, this book chapter gives an overview of the current state of key components involved in a digital twin for biopharmaceutical manufacturing. Currently available software for DT development, process analytical technologies (PATs) for data acquisition and information communication, various modeling strategies to construct the digital replica, and the integration between physical and virtual plants are comprehensively reviewed.
... Although WSN node localization offers numerous benefits, there are several challenges that need to be addressed for successful implementation [88], [89]. These challenges arise due to the unique characteristics and constraints of WSNs [39]. ...
Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.
... Industry 4.0 relies on Wireless Sensor Networks (WSNs) to automate industrial applications [1]. Typically, each device hosts a critical application that generates data packets requiring high Quality of Service (QoS) in terms of reliability and latency. ...
Industrial Wireless Sensor Networks (IWSN) play a key role in the Industry 4.0 revolution. The network infrastructure is critical to interconnect sensors and actuators and needs to respect Key Performance Indicators. IEEE 802.15.4-TSCH is a candidate technology for IWSN since it relies on scheduled transmissions and frequency hopping to make the network more reliable. However, distributed scheduling solutions fail to provide high reliability and low end-to-end latency. Software Defined Network (SDN) tends now to emerge in wireless networks as well, where a controller is in charge of the whole network configuration. But scheduled wireless networks require to go beyond usual SDN forwarding rules by including the radio resource allocation (dedicated time-frequency blocks). Moreover, radio links are known to be unreliable, and we need to adapt the control and data planes to make the network efficient. We propose SDN-TSCH to orchestrate a scheduled network adapting the SDN paradigm. More specifically, the controller is in charge of i) selecting the time source, ii) maintaining a tree structure for the control plane, with scheduled resources dedicated to the control plane, and iii) installing a new data flow while guaranteeing flow isolation. We also propose a very efficient link quality estimation technique tailored for scheduled TSCH networks. Our simulations highlight that the SDN controller can allocate in SDN-TSCH just-enough resources to respect both latency and reliability constraints.
