Smart Sensors and Industrial IoT (IIoT): A Driver of the Growth of Industry 4.0

2021 ◽  
pp. 37-49
Author(s):  
Vijay Prakash Gupta
Keyword(s):  
Industry 4.0 ◽  
Smart Sensors ◽  
Industrial Iot

scholarly journals Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 628
Author(s):  
Michail J. Beliatis ◽  
Kasper Jensen ◽  
Lars Ellegaard ◽  
Annabeth Aagaard ◽  
Mirko Presser
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Careful Consideration ◽  
Added Value ◽  
Shop Floor ◽  
Circuit Boards ◽  
Next Generation ◽  
Road Map ◽  
Industrial Iot ◽  
Production Cycles

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

Evolving the Security Paradigm for Industrial IoT Environments

2020 ◽  
pp. 69-90
Author(s):  
Luis Rosa ◽  
Miguel Borges de Freitas ◽  
João Henriques ◽  
Pedro Quitério ◽  
Filipe Caldeira ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Detection System ◽  
Geographic Area ◽  
Platform Architecture ◽  
Industrial Iot ◽  
Automation And Control ◽  
Large Geographic Area ◽  
And Control ◽  
Detection Evaluation ◽  
Anomaly Detection System

In recent years, IACS (Industrial Automation and Control Systems) have become more complex, due to the increasing number of interconnected devices. This IoT (internet of things)-centric IACS paradigm, which is at the core of the Industry 4.0 concept, expands the infrastructure boundaries beyond the aggregated-plant, mono-operator vision, being dispersed over a large geographic area. From a cybersecurity-centric perspective, the distributed nature of modern IACS makes it difficult not only to understand the nature of incidents but also to assess their progression and threat profile. Defending against those threats is becoming increasingly difficult, requiring orchestrated and collaborative distributed detection, evaluation, and reaction capabilities beyond the scope of a single entity. This chapter presents the Intrusion and Anomaly Detection System platform architecture that was designed and developed within the scope of the ATENA H2020 project, to address the specific needs of distributed IACS while providing (near) real-time cybersecurity awareness.

scholarly journals Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

2019 ◽  
Vol 9 (20) ◽  
pp. 4323 ◽  
Author(s):  
López de Lacalle ◽  
Posada
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Research Community ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Industrial Internet Of Things ◽  
Visual Computing ◽  
Industrial Internet ◽  
Industrial Iot ◽  
New Industry

The new advances of IIOT (Industrial Internet of Things), together with the progress in visual computing technologies, are being addressed by the research community with interesting approaches and results in the Industry 4.0 domain[...]

Industry 4.0 and clusters: complementaries or substitutes in firm’s knowledge creation?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nils Grashof ◽  
Alexander Kopka ◽  
Colin Wessendorf ◽  
Dirk Fornahl
Keyword(s):  
Industry 4.0 ◽  
Substitution Effect ◽  
Interaction Effects ◽  
Ordinary Least Squares ◽  
Knowledge Generation ◽  
Firm Level ◽  
Cluster Theory ◽  
Content Type ◽  
Industrial Iot ◽  
Key Enabling Technologies

Purpose This paper aims to show the interaction effects between clusters and cluster-specific attributes and the industrial internet of things (IoT) knowledge of a firm on the innovativeness of firms. Cluster theory and the concept of key enabling technologies are linked to test their effect on a firm’s incremental and radical knowledge generation. Design/methodology/approach Quantitative approach at the firm-level. By combining several data sources (e.g. ORBIS, PATSTAT and German subsidy catalogue) the paper relies on a unique database encompassing 8,347 firms in Germany. Ordinary least squares (OLS)-regression techniques are used for data analysis. Findings Industrial IoT is an important driver of radical patents, mediated positively by firm size. For incremental knowledge, a substitution effect occurs between a cluster and IoT effects, which is bigger for larger firms and dependent on cluster attributes and firms’ outside connections. Research limitations/implications The paper opens up new research paths considering long-term disruptive effects of the industrial IoT compared to short-term effects on the innovativeness of firms within clusters. Additionally, it enables further research enriching the discussion about cluster attributes and how these affect ongoing processes. Practical implications Linking cluster theory and policy with Industry 4.0 raises awareness for being considerate in terms of funding and scrutinising one-size-fits-all approaches. Originality/value Connecting the concepts of a cluster and advanced manufacturing technologies as a proxy for industrial IoT, specifically focussing on both radical and incremental innovations is a new approach. Especially, taking into account the interaction effects between cluster attributes and the influence of industrial IoT on the innovativeness of firms.

scholarly journals A Survey on Internet of Things (IoT), Industrial IoT (IIoT) and Industry 4.0

2020 ◽  
Vol 175 (27) ◽  
pp. 20-27
Author(s):  
Sujit N. Deshpande ◽  
Rashmi M. Jogdand
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Industrial Iot

scholarly journals Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures

2021 ◽  
Author(s):  
Konstantinos Tsiknas ◽  
Dimitrios Taketzis ◽  
Konstantinos Demertzis ◽  
Charalabos Skianis
Keyword(s):  
Data Streams ◽  
Industry 4.0 ◽  
Recent Literature ◽  
Physical World ◽  
The State ◽  
Industrial Equipment ◽  
Multilevel Security ◽  
Cyber Threats ◽  
Industrial Iot ◽  
Technological Developments

In today’s Industrial IoT (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities, the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions against these attacks, as they have been proposed in the most recent literature.

Challenges in Green Intellectual Capital and Knowledge Management in Sustainability and Industry 4.0

2021 ◽  
pp. 150-166
Author(s):  
Irene Martín-Rubio
Keyword(s):  
Knowledge Management ◽  
Conceptual Model ◽  
21St Century ◽  
Intellectual Capital ◽  
Industry 4.0 ◽  
Smart Sensors ◽  
Suggested Model

The conceptual model proposed in this study is used to serve a guide for Industry 4.0 to understand the effect of GIC (green intellectual capital) and GKM (green knowledge management) on sustainability. Green challenge in Industry 4.0 has increasingly become a hot topic in both academia and practice. Among the Industry 4.0 topics, digital chain monitoring has a great impact on the performance of the company. The study of the industrial digital chain is a great green challenge in the 21st century in order to understand and manage the flows of green information. Knowledge of the human, relational, and structural (including technological aspects) will help to better understand and management the effects of traceability on sustainability. Several of the concepts and variables in the suggested model can easily be managed by organizations if they carefully measure their green intangible nature with smart sensors.

Reshaping Education and Entrepreneurial Skills for Industry 4.0

2021 ◽  
pp. 64-77
Author(s):  
Adeshina Olushola Adeniyi ◽  
Idris Olayiwola Ganiyu
Keyword(s):  
3D Printing ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Smart Sensors ◽  
Technological Advancement ◽  
Fourth Industrial Revolution ◽  
Intelligent Robots ◽  
Entrepreneurial Skills ◽  
Technological Advances ◽  
Self Driving Cars

Since the coinage of the Fourth Industrial Revolution (4IR), there has been plethora of studies on the concept. The 4IR, otherwise referred to as Industry 4.0, is a nomenclature used by Klaus Schwab to describes the historical progression of technological advancement. The 4IR is principally the integration of the physical, digital, and industrial worlds. The testimonies of these advancements will result in self-driving cars, intelligent robots, autonomous drones, 3D printing, smart sensors, among several others. In fact, this is already a reality and is revolutionising our world. Given all these technological advances and unimaginable possibilities of the future, it is very sacrosanct to examine the role education will play in this era. What entrepreneurial skills will be required for the 4IR? How does entrepreneurial ecosystem position themselves to thrive in this era? This chapter explores those skills needed in the 4IR.

scholarly journals Secure IIoT-Enabled Industry 4.0

2021 ◽  
Vol 13 (22) ◽  
pp. 12384
Author(s):  
Zeeshan Hussain ◽  
Adnan Akhunzada ◽  
Javed Iqbal ◽  
Iram Bibi ◽  
Abdullah Gani
Keyword(s):  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Detection Accuracy ◽  
Hybrid Architecture ◽  
Advanced Persistent Threats ◽  
Current State ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Detection Mechanisms ◽  
Conventional Detection

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.

scholarly journals Guest Editorial: Design Engineering in the Age of Industry 4.0

2021 ◽  
pp. 1-4
Author(s):  
Janet K. Allen ◽  
Sesh Commuri ◽  
Jianxin Jiao ◽  
Jelena Milisavljevic-Syed ◽  
Farrokh Mistree ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Production Systems ◽  
Systems Design ◽  
Smart Manufacturing ◽  
Future Research ◽  
Design Engineering ◽  
Production Environment ◽  
Special Issue ◽  
Industrial Iot

Abstract This special issue is motivated by the trend of smart factories of the future towards the fourth Industrial Revolution, which makes it possible to better leverage capabilities and resources in a human-cyber-physical production environment. This emerging paradigm of Industry 4.0 poses new systems design problems at the interface of smart manufacturing, robust and flexible automation, distributed and reconfigurable production systems industrial IoT, and supply chain integration. Recent advances of design engineering in the age of Industry 4.0 are presented in this special issue. More than forty (40) papers were received and peer-reviewed, out of which thirteen (13) papers were selected for publication. These are both theoretical and practical, as well as state-of-the-art reviews, new perspectives, and outlook for future research directions in the field. The papers span a range of design aspects and Industry 4.0 technologies. There are three intersecting clusters in this category: design principles and techniques for Industry 4.0, smart manufacturing technologies, and machine learning and data-driven techniques for Industry 4.0.

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