Big Data Analytics for the Industrial Internet of Things

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 12936

Special Issue Editors


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Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: wireless sensor and actuator networks; middleware for sensor and actuator networks; vehicular sensor networks; edge computing; fog computing; online stream processing of sensing dataflows; IoT and big data processing; pervasive and mobile computing; cooperative networking; cyber physical systems for Industry 4.0
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: distributed systems; industrial internet of things; industrial digital twins; edge cloud computing; resource orchestration in cloud/edge
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Data Analytics and Visualization Group, Barcelona Supercomputing Center, 08034 Barcelona, Spain
Interests: data analytics; data visualization; industrial machine learning; digital urbanism

Special Issue Information

Dear Colleagues,

The fourth industrial revolution (I4.0) promises to bring a significant transformation of industrial manufacturing processes through extensive digitization of factories.  I4.0 pushes to evolve the rigid and hierarchical view of manufacturing systems proposed by the ISA95 model towards a more open, flexible, and integrated perspective where factory assets, empowered with communication and computing capabilities, collaborate with one another to increase productivity and enhance business agility. The ecosystem of networked and “smartified”  industrial devices, universally referred to as the Industrial Internet of Things (IIOT), is one of the pillars called to sustain the digital transition fostered by I4.0. Like in many other IoT systems, the large use of such devices in industrial manufacturing settings is generating a wealth of raw data; to gain insights from such a large volume of information Big Data, analytics techniques must be employed. Unlike other “data-intensive” domains (such as financial, government, retail, etc.), the industrial one is faced with issues deriving from the need to fulfill requirements such as strong data security, high safety guarantee, and very fast data creation and consumption. Furthermore, it has to cope with complex and distributed computing contexts. On one hand, time-critical control applications consuming data streams in real-time need to run as close to data sources as possible (possibly on the IIoT device itself or on on-premise Edge nodes); on the other hand, higher computing capacity resources, such as those provided in the Cloud, are required to process Big Data at rest, such as, e.g., running analytics for use by the business departments or engineering AI/ML models to be deployed as controllers in machine control on at the shop floor.

This Special Issue aims to collect consolidated research findings and stimulate novel approaches and ideas on how to leverage the Big Data produced by IIoT devices to best support the production process and boost the business of manufacturing companies.

Potential topics include, but are not limited to, the following:

  • Big Data gathering and processing in manufacturing environments;
  • Distributed data processing in the Cloud continuum;
  • Big Data processing for industrial control;
  • Big Data processing for production control;
  • Hybrid and distributed Digital Twins;
  • AI/ML models in the control loop;
  • Distributed ML for IIoT;
  • Big Data for model-driven industrial control systems;
  • Analytics for Big Data at rest;
  • Resource virtualization to support data management;
  • Industrial Big Data confidentiality, tampering and leakage;
  • Security for IIoT devices;
  • Interoperability and compliance with IIoT reference architectures;
  • Scalability and robustness of data-intensive control loops.

Prof. Dr. Paolo Bellavista
Dr. Giuseppe Di Modica
Prof. Dr. Fernando Cucchietti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • industrial internet of things (IIoT)
  • big data processing
  • edge cloud computing
  • cloud continuum
  • digital twins
  • distributed machine learning for IIoT
  • big data processing for control loops
  • performance isolation over virtualized resources

Published Papers (6 papers)

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Research

16 pages, 1446 KiB  
Article
Digital Twin Applications in Manufacturing Industry: A Case Study from a German Multi-National
by Martin Wynn and Jose Irizar
Future Internet 2023, 15(9), 282; https://doi.org/10.3390/fi15090282 - 22 Aug 2023
Cited by 1 | Viewed by 2342
Abstract
This article examines how digital twins have been used in a multi-national corporation, what technologies have been used, what benefits have been delivered, and the significance of people- and process-related issues in achieving successful implementation. A qualitative, inductive research method is used, based [...] Read more.
This article examines how digital twins have been used in a multi-national corporation, what technologies have been used, what benefits have been delivered, and the significance of people- and process-related issues in achieving successful implementation. A qualitative, inductive research method is used, based on interviews provided by key personnel involved in three digital twin projects. The article concludes that digital twin projects are likely to involve incremental rather than disruptive change, and that successful implementation is usually underpinned by ensuring technology, people, and process change factors are progressed in a balanced and integrated fashion. Building upon existing frameworks, three “properties” are identified as being of particular value in digital twin projects—workforce adaptability, technology manageability, and process agility—and a related set of steps and actions is put forward as a template and point of reference for future digital twin implementations. The combination of assessing digital properties and following a set of key actions represents a novel approach to digital twin project planning, and overall the findings are a contribution to the developing theory around digital twins and digitalization, in general, and are also of relevance to professionals embarking on DT projects. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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20 pages, 656 KiB  
Article
I4.0I: A New Way to Rank How Involved a Company Is in the Industry 4.0 Era
by Vitória Francesca Biasibetti Zilli, Cesar David Paredes Crovato, Rodrigo da Rosa Righi, Rodrigo Ivan Goytia Mejia, Giovani Pesenti and Dhananjay Singh
Future Internet 2023, 15(2), 73; https://doi.org/10.3390/fi15020073 - 13 Feb 2023
Cited by 3 | Viewed by 1482
Abstract
Cloud, IoT, big data, and artificial intelligence are currently very present in the industrial and academic areas, being drivers of technological revolution. Such concepts are closely related to Industry 4.0, which can be defined as the idea of a flexible, technological, and connected [...] Read more.
Cloud, IoT, big data, and artificial intelligence are currently very present in the industrial and academic areas, being drivers of technological revolution. Such concepts are closely related to Industry 4.0, which can be defined as the idea of a flexible, technological, and connected factory, encompassing the shop floor itself and its relationship between workers, the chain of supply, and final products. Some studies have already been developed to quantify a company’s level of maturity within the scope of Industry 4.0. However, there is a lack of a global and unique index that, by receiving as input how many implemented technologies a company has, enables its classification and therefore, comparison with other companies of the same genre. Thus, we present the I4.0I (Industry 4.0 Index), an index that allows companies to measure how far they are in Industry 4.0, enabling competitiveness between factories and stimulating economic and technological growth. To assess the method, companies in the technology sector received and answered a questionnaire in which they marked the technologies they used over the years and the income obtained. The results were used to compare the I4.0I with the profit measured in the same period, proving that the greater the use of technology, the greater the benefits for the company. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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19 pages, 614 KiB  
Article
Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
by Paolo Bellavista, Mattia Fogli, Carlo Giannelli and Cesare Stefanelli
Future Internet 2023, 15(2), 42; https://doi.org/10.3390/fi15020042 - 22 Jan 2023
Cited by 2 | Viewed by 1605
Abstract
The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable [...] Read more.
The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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23 pages, 4322 KiB  
Article
Hybrid Sensing Platform for IoT-Based Precision Agriculture
by Hamid Bagha, Ali Yavari and Dimitrios Georgakopoulos
Future Internet 2022, 14(8), 233; https://doi.org/10.3390/fi14080233 - 28 Jul 2022
Cited by 11 | Viewed by 2849
Abstract
Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial [...] Read more.
Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA—an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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18 pages, 2003 KiB  
Article
Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
by Despoina Chochtoula, Aristidis Ilias, Yannis C. Stamatiou and Christos Makris
Future Internet 2022, 14(8), 232; https://doi.org/10.3390/fi14080232 - 28 Jul 2022
Cited by 4 | Viewed by 1965
Abstract
SCADA systems monitor critical industrial, energy and other physical infrastructures in order to detect malfunctions, issue alerts and, in many cases, propose or even take remedial actions. However, due to their attachment to the Internet, SCADA systems are, today, vulnerable to attacks such [...] Read more.
SCADA systems monitor critical industrial, energy and other physical infrastructures in order to detect malfunctions, issue alerts and, in many cases, propose or even take remedial actions. However, due to their attachment to the Internet, SCADA systems are, today, vulnerable to attacks such as, among several others, interception of data traffic, malicious modifications of settings and control operations data, malicious modification of measurements and infrastructure data and Denial-of-Service attacks. Our research focuses on strengthening SCADA systems with cryptographic methods and protection mechanisms with emphasis on data and messaging encryption and device identification and authentication. The limited availability of computing power and memory in sensors and embedded devices deployed in SCADA systems make render cryptographic methods with higher resource requirements, such as the use of conventional public key cryptography such as RSA, unsuitable. We, thus, propose Elliptic Curve Cryptography as an alternative cryptographic mechanism, where smaller key sizes are required, with lower resource requirements for cryptographic operations. Accordingly, our approach integrates Modbus, a commonly used SCADA communication protocol, with Elliptic Curve Cryptography. We have, also, developed an experimental set-up in order to demonstrate the performance of our approach and draw conclusions regarding its effectiveness in real SCADA installations. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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24 pages, 5308 KiB  
Article
Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
by Martina Pappalardo, Antonio Virdis and Enzo Mingozzi
Future Internet 2022, 14(7), 197; https://doi.org/10.3390/fi14070197 - 28 Jun 2022
Viewed by 1697
Abstract
The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On [...] Read more.
The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On the one hand, the quality of these decisions is typically affected by the freshness of the received information, thus requesting frequent updates from the IoT devices. On the other hand, the severe energy, memory, processing, and communication constraints of IoT devices and networks pose limitations in the frequency of sensing and reporting. So, it is crucial to minimize the energy consumed by the device for sensing the environment and for transmitting the update messages, while taking into account the requirements for information freshness. Edge-caching can be effective in reducing the sensing and the transmission frequency; however, it requires a proper refreshing scheme to avoid staleness of information, as IoT applications need timeliness of status updates. Recently, the Age of Information (AoI) metric has been introduced: it is the time elapsed since the generation of the last received update, hence it can describe the timeliness of the IoT application’s knowledge of the process sampled by the IoT device. In this work, we propose a model-driven and AoI-aware optimization scheme for information caching at the network edge. To configure the cache parameters, we formulate an optimization problem that minimizes the energy consumption, considering both the sampling frequency and the average frequency of the requests sent to the device for refreshing the cache, while satisfying an AoI requirement expressed by the IoT application. We apply our caching scheme in an emulated IoT network, and we show that it minimizes the energy cost while satisfying the AoI requirement. We also compare the case in which the proposed caching scheme is implemented at the network edge against the case in which there is not a cache at the network edge. We show that the optimized cache can significantly lower the energy cost of devices that have a high transmission cost because it can reduce the number of transmissions. Moreover, the cache makes the system less sensitive to higher application-request rates, as the number of messages forwarded to the devices depends on the cache parameters. Full article
(This article belongs to the Special Issue Big Data Analytics for the Industrial Internet of Things)
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