Special Issue "IoT Applications and Industry 4.0"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Dr. Thomas Lagkas
Website
Guest Editor
Department of Computer Science, The University of Sheffield International Faculty, Thessaloníki, Greece
Interests: wireless communication networks; resource allocation; IoT; 5G
Dr. Panagiotis Sarigiannidis
Website
Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: optical and wireless networks; Internet of things; security and privacy in smart networks; smart grid
Special Issues and Collections in MDPI journals
Dr. John Soldatos
Website
Guest Editor
Athens Information Technology, Athens, Greece
Interests: internet-of-things; cloud computing; big data; artificial intelligence

Special Issue Information

Dear Colleagues,

In recent years, we have been witnessing the rise of the Internet-of-Things (IoT) computing paradigm, which is propelled by the exponential increase of the number of Internet-connected devices that already amount to tens of billions. IoT enables a wide range of different applications, which leverage data from the physical world as a means of providing new forms of intelligence and enabling paradigm shifts in areas such as healthcare, transport, smart cities, and logistics. Despite its potential to disrupt almost all sectors of the economy, it was recently acknowledged that the lion’s share of IoT in the years to come will stem from its deployment in industrial environments, which is usually coined as Industrial Internet-of-Things (IIoT). IIoT is the cornerstone of the fourth industrial revolution (Industry 4.0), which hinges on the digitization of industrial processes and the use of Internet-connected devices and cyberphysical systems for automation and control. IIoT is already disrupting production operations in industrial plants, based on its deployment in the scope of novel use cases, like flexible automation, predictive maintenance, optimal supply chain management, digital twins, augmented reality, and more.    

Nevertheless, to effectively support IIoT deployments in industrial settings, there is still a need to overcome several challenges stemming from the special attributes of device-to-device communications, embedded devices, big data management, but also from the expanded complexity of Internet-connected devices that currently include smart objects with (semi)autonomous behavior such as drones, industrial robots, and autonomous guided vehicles. The latter create new challenges associated with the identification of efficient ways for integrating IoT technologies in contemporary applications exploiting the potentials of real-time monitoring, interactive control, self-management, and data analytics towards “smart” behavior and enhanced performance. This is the reason the research community is intensively working on devising, implementing, and validating advanced IIoT solutions that address the above-listed challenges in device saturated environments, which include smart objects. In this context, this Special Issue focuses on Industrial Internet-of-Things systems and applications, which represent the highest value segment of the IoT market. The main scope of this Special Issue is to identify and promote new techniques for the realization of promising IoT applications in various areas, with particular emphasis on Industry 4.0 scenarios and their deployments in industrial settings, such as manufacturing shop-floors, energy plants, oil refineries, warehouses, and smart cities. We welcome submissions on the core technologies that underpin IIoT (e.g., networking, Big Data, cybersecurity), as well as on prototypes, case studies, and pilot deployments of IIoT/Industry 4.0 applications. Overall topics of interest include (but are not limited to):

  • Industry 4.0;
  • Smart electric grids;
  • Smart farming;
  • Environmental telemetry;
  • Smart city;
  • Smart home;
  • Renewable energy;
  • Remote e-health;
  • Vehicular networks;
  • Autonomous vehicles;
  • Flying networks;
  • Sensor networks;
  • Wearables;
  • Smart retails and supply chain;
  • Industrial Internet-of-Things architectures;
  • Design and implementation of cyberphysical production systems;
  • Multisensor systems for automated data collection in industrial environments;
  • Industrial Internet-of-Things systems that comprise smart objects (drones, robots, autonomous guided vehicles);
  • Performance analysis and evaluation of Industrial Internet-of-Things systems;
  • Cybersecurity in Industrial Internet-of-Things environments;
  • Framework for convergence of IT and OT security;
  • Distributed data analytics for machine intelligence;
  • Industrial IoT technologies for flexible production lines;
  • Internet-of-Things analytics for digital simulations and digital twins;
  • Analytics for production monitoring;
  • Advanced machine learning and artificial intelligence approaches for industrial use cases;
  • Industrial IoT use cases such as digital automation, predictive maintenance, and zero-defect production;
  • Industrial IoT cases studies in sectors like manufacturing, energy, oil and gas, mining, and supply chain management.

Dr. Thomas Lagkas
Dr. Panagiotis Sarigiannidis
Dr. John Soldatos
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 papers will be 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. Information 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 1000 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

  • IoT
  • Internet-of-Things
  • Industrial Internet-of-Things
  • IIoT
  • Big Data
  • security
  • cybersecurity
  • high speed connectivity
  • 5G
  • LoRAWAN
  • NB-IoT
  • artificial intelligence

Published Papers (8 papers)

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Research

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Open AccessArticle
Configurable Distributed Data Management for the Internet of the Things
Information 2019, 10(12), 360; https://doi.org/10.3390/info10120360 - 20 Nov 2019
Abstract
One of the main challenges in modern Internet of Things (IoT) systems is the efficient collection, routing and management of data streams from heterogeneous sources, including sources with high ingestion rates. Despite the existence of various IoT data streaming frameworks, there is still [...] Read more.
One of the main challenges in modern Internet of Things (IoT) systems is the efficient collection, routing and management of data streams from heterogeneous sources, including sources with high ingestion rates. Despite the existence of various IoT data streaming frameworks, there is still no easy way for collecting and routing IoT streams in efficient and configurable ways that are easy to be implemented and deployed in realistic environments. In this paper, we introduce a programmable engine for Distributed Data Analytics (DDA), which eases the task of collecting IoT streams from different sources and accordingly, routing them to appropriate consumers. The engine provides also the means for preprocessing and analysis of data streams, which are two of the most important tasks in Big Data analytics applications. At the heart of the engine lies a Domain Specific Language (DSL) that enables the zero-programming definition of data routing and preprocessing tasks. This DSL is outlined in the paper, along with the middleware that supports its runtime execution. As part of the paper, we present the architecture of the engine, as well as the digital models that it uses for modelling data streams in the digital world. We also discuss the validation of the DDA in several data intensive IoT use cases in industrial environments, including use cases in pilot productions lines and in several real-life manufacturing environments. The latter manifest the configurability, programmability and flexibility of the DDA engine, as well as its ability to support practical applications. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
Precision Agriculture: A Remote Sensing Monitoring System Architecture
Information 2019, 10(11), 348; https://doi.org/10.3390/info10110348 - 09 Nov 2019
Cited by 1
Abstract
Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production [...] Read more.
Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
A Smart Energy Harvesting Platform for Wireless Sensor Network Applications
Information 2019, 10(11), 345; https://doi.org/10.3390/info10110345 - 06 Nov 2019
Abstract
Advances in micro-electro-mechanical systems (MEMS) as well as the solutions for power scavenging can now provide feasible alternatives in a variety of applications. Wireless sensor networks (WSN), which operate on rechargeable batteries, could be based on a fresh basis which aims both at [...] Read more.
Advances in micro-electro-mechanical systems (MEMS) as well as the solutions for power scavenging can now provide feasible alternatives in a variety of applications. Wireless sensor networks (WSN), which operate on rechargeable batteries, could be based on a fresh basis which aims both at environmental power collection and wireless charging in various shapes and scales. Consequently, a potential illimitable energy supply can override the hypothesis of the limited energy budget (which can also impact the system’s efficiency). The presented platform is able to efficiently power a low power IoT system with processing, sensing and wireless transmission potentials. It incorporates a cutting-edge energy management IC that enables exceptional energy harvesting, applicable on low power and downsized energy generators. In contrast to other schemes, it supports not only a range of power supply alternatives, but also a compound energy depository system. The objective of this paper is to describe the design of the system, the integrated intelligence and the power autonomy performance. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
Design of IoT-based Cyber–Physical Systems: A Driverless Bulldozer Prototype
Information 2019, 10(11), 343; https://doi.org/10.3390/info10110343 - 05 Nov 2019
Cited by 2
Abstract
From autonomous vehicles to robotics and machinery, organizations are developing autonomous transportation systems in various domains. Strategic incentives point towards a fourth industrial revolution of cyber–physical systems with higher levels of automation and connectivity throughout the Internet of Things (IoT) that interact with [...] Read more.
From autonomous vehicles to robotics and machinery, organizations are developing autonomous transportation systems in various domains. Strategic incentives point towards a fourth industrial revolution of cyber–physical systems with higher levels of automation and connectivity throughout the Internet of Things (IoT) that interact with the physical world. In the construction and mining sectors, these developments are still at their infancy, and practitioners are interested in autonomous solutions to enhance efficiency and reliability. This paper illustrates the enhanced design of a driverless bulldozer prototype using IoT-based solutions for the remote control and navigation tracking of the mobile machinery. We illustrate the integration of a cloud application, communication protocols and a wireless communication network to control a small-scale bulldozer from a remote workstation. Furthermore, we explain a new tracking functionality of work completion using maps and georeferenced indicators available via a user interface. Finally, we provide a preliminary safety and security risk assessment of the system prototype and propose guidance for application in real-scale machinery. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
Resource Allocation Combining Heuristic Matching and Particle Swarm Optimization Approaches: The Case of Downlink Non-Orthogonal Multiple Access
Information 2019, 10(11), 336; https://doi.org/10.3390/info10110336 - 30 Oct 2019
Abstract
The ever-increasing requirement of massive connectivity, due to the rapid deployment of internet of things (IoT) devices, in the emerging 5th generation (5G) mobile networks commands for even higher utilization of the available spectrum. Non-orthogonal multiple access (NOMA) is a promising solution that [...] Read more.
The ever-increasing requirement of massive connectivity, due to the rapid deployment of internet of things (IoT) devices, in the emerging 5th generation (5G) mobile networks commands for even higher utilization of the available spectrum. Non-orthogonal multiple access (NOMA) is a promising solution that can effectively accommodate a higher number of users, resulting in increased spectrum utilization. In this work, we aim to maximize the total throughput of a NOMA system, while maintaining a good level of fairness among the users. We propose a three-step method where the first step matches the users to the channels using a heuristic matching algorithm, while the second step utilizes the particle swarm optimization algorithm to allocate the power to each channel. In the third step, the power allocated to each channel is further distributed to the multiplexed users based on their respective channel gains. Based on extensive performance simulations, the proposed method offers notable improvement, e.g., 15% in terms of system throughput and 55% in terms of user fairness. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
Internet of Things Infrastructure for Security and Safety in Public Places
Information 2019, 10(11), 333; https://doi.org/10.3390/info10110333 - 28 Oct 2019
Abstract
We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a [...] Read more.
We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a protection and security environment for citizens. The framework is currently being used to support visitors in public spaces and events, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles, and privacy. State-of-the-art technologies in the domain of IoT data collection and analytics are combined with localization techniques, ontologies, reasoning mechanisms, and data aggregation in order to acquire a better understanding of the ongoing situation and inform the necessary people and devices to act accordingly. Finally, we present the first results of people localization and the platforms’ ontology and representation framework. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Open AccessArticle
A Novel Approach to Component Assembly Inspection Based on Mask R-CNN and Support Vector Machines
Information 2019, 10(9), 282; https://doi.org/10.3390/info10090282 - 11 Sep 2019
Abstract
Assembly is a very important manufacturing process in the age of Industry 4.0. Aimed at the problems of part identification and assembly inspection in industrial production, this paper proposes a method of assembly inspection based on machine vision and a deep neural network. [...] Read more.
Assembly is a very important manufacturing process in the age of Industry 4.0. Aimed at the problems of part identification and assembly inspection in industrial production, this paper proposes a method of assembly inspection based on machine vision and a deep neural network. First, the image acquisition platform is built to collect the part and assembly images. We use the Mask R-CNN model to identify and segment the shape from each part image, and to obtain the part category and position coordinates in the image. Then, according to the image segmentation results, the area, perimeter, circularity, and Hu invariant moment of the contour are extracted to form the feature vector. Finally, the SVM classification model is constructed to identify the assembly defects, with a classification accuracy rate of over 86.5%. The accuracy of the method is verified by constructing an experimental platform. The results show that the method effectively completes the identification of missing and misaligned parts in the assembly, and has good robustness. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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Review

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Open AccessReview
A Review on UAV-Based Applications for Precision Agriculture
Information 2019, 10(11), 349; https://doi.org/10.3390/info10110349 - 11 Nov 2019
Cited by 4
Abstract
Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related [...] Read more.
Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations. Full article
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
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