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Future Internet
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Industrial Internet of Things (IIoT): Trends and Technologies
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Industrial Internet of Things (IIoT): Trends and Technologies

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 17389

Special Issue Editors

Dr. Zhihao Liu

E-Mail Website
Guest Editor
Department of Production Engineering, The Royal Institute of Technology (KTH), 114 28 Stockholm, Sweden
Interests: Industry 5.0; digital twin and metaverse; embodied AI; human–robot collaboration; robot learning; reinforcement learning; neural information processing; human-compatible AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Franco Davoli

E-Mail Website
Guest Editor
1. Department of Electrical, Electronic, and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16126 Genova, Italy
2. CNIT National Laboratory of Smart and Secure Networks (S2N), 16126 Genova, Italy
Interests: dynamic resource allocation in multiservice networks and in future internet; mobile wireless and satellite networks; multimedia communications and services; flexible; programmable; energy-efficient networking
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Borsatti

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy
Interests: NFV; SDN; intent-based networking; MEC; 5G network slicing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) has emerged as a transformative force revolutionizing industries across the globe. It encompasses the integration of sensor technologies, machine learning, big data analytics, and connectivity to revolutionize industrial processes and enhance productivity. This transformation has not just enhanced industrial processes and productivity but is also paving the way for innovative business models. As the IIoT continues to evolve rapidly, it is crucial to delve into its latest trends, explore state-of-the-art technologies, and understand the emerging challenges and future possibilities. 

In this Special Issue, we invite submissions focusing on the dynamic landscape of the Industrial Internet of Things (IIoT) and its evolving trends and technologies. We encourage researchers, academicians, and industry practitioners to contribute their valuable insights and research findings to this Special Issue, fostering the dissemination of knowledge and advancements in the realm of the Industrial Internet of Things (IIoT). 

Scope: 

This Special Issue seeks to explore various facets of IIoT, including but not limited to:

  • IIoT applications in manufacturing, healthcare, energy, transportation, agriculture, and other industries;
  • Edge computing and IIoT;
  • Security and privacy challenges in IIoT;
  • AI and machine learning in IIoT systems;
  • Big data analytics for IIoT;
  • Connectivity protocols and standards for IIoT: URLLC, TSN, OPC UA, etc.;
  • Sensor technologies and their role in IIoT;
  • Industry 4.0 and the convergence of IIoT with other technologies;
  • Case studies and real-world implementations of IIoT solutions.

Dr. Zhihao Liu
Prof. Dr. Franco Davoli
Dr. Davide Borsatti
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)
  • edge computing
  • cybersecurity in IIoT
  • Artificial Intelligence (AI) in IIoT applications
  • Industry 5.0 with IIoT
  • big data analytics for IIoT
  • sensor networks for IIoT
  • IIoT-driven smart manufacturing
  • IIoT standards and protocols
  • wireless communication in industrial environments
 

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Related Special Issue

Published Papers (7 papers)

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Research

23 pages, 1060 KiB  
Article
Uncertainty-Aware Time Series Anomaly Detection
by Paul Wiessner, Grigor Bezirganyan, Sana Sellami, Richard Chbeir and Hans-Joachim Bungartz
Future Internet 2024, 16(11), 403; https://doi.org/10.3390/fi16110403 - 31 Oct 2024
Viewed by 2886
Abstract
Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have [...] Read more.
Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise. In this paper, we propose LSTMAE-UQ (Long Short-Term Memory Autoencoder with Aleatoric and Epistemic Uncertainty Quantification), a novel approach that incorporates both aleatoric (data noise) and epistemic (model uncertainty) uncertainties for more robust anomaly detection. The model combines the strengths of LSTM networks for capturing complex time series relationships and autoencoders for unsupervised anomaly detection and quantifies uncertainties based on the Bayesian posterior approximation method Monte Carlo (MC) Dropout, enabling a deeper understanding of noise recognition. Our experimental results across different real-world datasets show that consideration of uncertainty effectively increases the robustness to noise and point outliers, making predictions more reliable for longer periodic sequential data. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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19 pages, 1298 KiB  
Article
Determinants to Adopt Industrial Internet of Things in Small and Medium-Sized Enterprises
by Abdullah Khanfor
Future Internet 2024, 16(9), 340; https://doi.org/10.3390/fi16090340 - 20 Sep 2024
Viewed by 1330
Abstract
The Industrial Internet of Things (IIoT) enhances and optimizes operations and product quality by reducing expenses and preserving critical factory components. The IIoT can also be integrated into the processes of small and medium-sized enterprises (SMEs). However, several factors and risks have discouraged [...] Read more.
The Industrial Internet of Things (IIoT) enhances and optimizes operations and product quality by reducing expenses and preserving critical factory components. The IIoT can also be integrated into the processes of small and medium-sized enterprises (SMEs). However, several factors and risks have discouraged SMEs from adopting the IIoT. This study aims to identify the factors influencing IIoT adoption and address the challenges by conducting semi-structured interviews with experienced stakeholders in SME factories. Group quotations and thematic analysis indicate essential themes from these interviews, suggesting two primary categories, human- and machine-related factors, that affect implementation. The main human-related factor is the decision making of high-level management and owners to implement the IIoT in their plants, which requires skilled individuals to achieve IIoT solutions. Machine-related factors present several challenges, including device compatibility-, device management-, and data storage-associated issues. Comprehending and addressing these factors when deploying the IIoT can ensure successful implementation in SMEs, maximizing the potential benefits of this technology. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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16 pages, 430 KiB  
Article
Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
by Janith K. Dassanayake, Minxiao Wang, Muhammad Z. Hameed and Ning Yang
Future Internet 2024, 16(8), 292; https://doi.org/10.3390/fi16080292 - 14 Aug 2024
Viewed by 1287
Abstract
In today’s digital landscape, content delivery networks (CDNs) play a pivotal role in ensuring rapid and seamless access to online content across the globe. By strategically deploying a network of edge servers in close proximity to users, CDNs optimize the delivery of digital [...] Read more.
In today’s digital landscape, content delivery networks (CDNs) play a pivotal role in ensuring rapid and seamless access to online content across the globe. By strategically deploying a network of edge servers in close proximity to users, CDNs optimize the delivery of digital content. One key mechanism involves caching frequently requested content at these edge servers, which not only alleviates the load on the source CDN server but also enhances the overall user experience. However, the exponential growth in user demands has led to increased network congestion, subsequently reducing the cache hit ratio within CDNs. To address this reduction, this paper presents an innovative approach for efficient cache replacement in a dynamic caching environment while maximizing the cache hit ratio via a cooperative cache replacement policy based on reinforcement learning. This paper presents an innovative approach to enhance the performance of CDNs through an advanced cache replacement policy based on reinforcement learning. The proposed system model depicts a mesh network of CDNs, with edge servers catering to user requests, and a main source CDN server. The cache replacement problem is initially modeled as a Markov decision process, and it is extended to a multi-agent reinforcement learning problem. We propose a cooperative cache replacement algorithm based on a multi-agent deep-Q network (MADQN), where the edge servers cooperatively learn to efficiently replace the cached content to maximize the cache hit ratio. Experimental results are presented to validate the performance of our proposed approach. Notably, our MADQN policy exhibits superior cache hit ratios and lower average delays compared to traditional caching policies. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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17 pages, 1149 KiB  
Article
Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation
by Pedro Nunes, Eugénio Rocha and José Santos
Future Internet 2024, 16(6), 214; https://doi.org/10.3390/fi16060214 - 17 Jun 2024
Viewed by 1335
Abstract
Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor [...] Read more.
Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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19 pages, 1936 KiB  
Article
GreenLab, an IoT-Based Small-Scale Smart Greenhouse
by Cristian Volosciuc, Răzvan Bogdan, Bianca Blajovan, Cristina Stângaciu and Marius Marcu
Future Internet 2024, 16(6), 195; https://doi.org/10.3390/fi16060195 - 31 May 2024
Cited by 4 | Viewed by 2874
Abstract
In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling [...] Read more.
In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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17 pages, 1140 KiB  
Article
Enhanced Beacons Dynamic Transmission over TSCH
by Erik Ortiz Guerra, Mario Martínez Morfa, Carlos Manuel García Algora, Hector Cruz-Enriquez, Kris Steenhaut and Samuel Montejo-Sánchez
Future Internet 2024, 16(6), 187; https://doi.org/10.3390/fi16060187 - 24 May 2024
Viewed by 3950
Abstract
Time slotted channel hopping (TSCH) has become the standard multichannel MAC protocol for low-power lossy networks. The procedure for associating nodes in a TSCH-based network is not included in the standard and has been defined in the minimal 6TiSCH configuration. Faster network formation [...] Read more.
Time slotted channel hopping (TSCH) has become the standard multichannel MAC protocol for low-power lossy networks. The procedure for associating nodes in a TSCH-based network is not included in the standard and has been defined in the minimal 6TiSCH configuration. Faster network formation ensures that data packet transmission can start sooner. This paper proposes a dynamic beacon transmission schedule over the TSCH mechanism that achieves a shorter network formation time than the default minimum 6TiSCH static schedule. A theoretical model is derived for the proposed mechanism to estimate the expected time for a node to get associated with the network. Simulation results obtained with different network topologies and channel conditions show that the proposed mechanism reduces the average association time and average power consumption during network formation compared to the default minimal 6TiSCH configuration. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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Graphical abstract

18 pages, 7381 KiB  
Article
Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
by David Todoli-Ferrandis, Javier Silvestre-Blanes, Víctor Sempere-Payá and Salvador Santonja-Climent
Future Internet 2024, 16(4), 130; https://doi.org/10.3390/fi16040130 - 12 Apr 2024
Viewed by 1842
Abstract
LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way [...] Read more.
LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies)
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