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Sustainable IoT Solutions for Industrial Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 9461

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Special Issue Editors

Dr. Fredrik Ahlgren

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Guest Editor

Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Kalmar, Sweden
Interests: internet of things, energy efficiency; marine engineering; machine learning

Dr. Arslan Musaddiq

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Guest Editor

Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Kalmar, Sweden
Interests: wireless communication networks; resources allocation; Internet of Things

Dr. Neda Maleki

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Guest Editor

Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Kalmar, Sweden
Interests: distributed systems; cloud computing; fog computing; optimization

Dr. Jorge L. Zapico

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Guest Editor

Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Kalmar, Sweden
Interests: sustainability; media technology; ICT

Special Issue Information

Dear Colleagues,

In the last couple of years, the Internet of Things (IoT) has dominated several application domains. IoT has shown its potential to improve our societies and industries by enabling smart communication between objects and devices in a cost-effective manner. The IoT is transforming manufacturing, transportation, lamp oil, gas, logistics, and other industrial sectors. It is also becoming an important aspect for small and medium-sized enterprises (SMEs). There are a number of possibilities how IoT can transform SME operations and future growth.

Apart from IoT's numerous potential benefits for the industrial sector, the IoT-based system needs to determine the factors affecting its sustainability goals. Maintaining and sustaining the operation of millions of battery-operated devices for the long term is a complex task. Not only is it important for the operation of SME, but it is also essential for a green environment. For example, we can deploy a large-scale smart recycling bin system in the city using IoT, however, maintaining such a system based on battery-operated devices is the main challenge that we need to handle. One of the solutions could be using an efficient dynamic sensing mechanism or machine learning-based sensing scheduling to enhance the battery performance. This Special Issue solicits high-quality original research papers that provide recent technical advances, potential real-life use cases, open research problems, and promising solutions with regard to sustainability and resource management for IoT devices' operation.

Topics of interest to this Special Issue include, but are not limited to, the following:

IoT solutions for SMEs while maintaining sustainability;
Energy efficiency in IoT network operation;
IoT resource management using efficient protocols at MAC, network, and physical layer levels;
IoT intelligent data processing machine learning for IoT resource management automation;
Optimization in the cloud and edge computing-based IoT;
Big data processing and modeling of IoT;
Management and storage of IoT data;
Case studies and testbed experiments of IoT applications.

Dr. Fredrik Ahlgren
Dr. Arslan Musaddiq
Dr. Neda Maleki
Dr. Jorge L. Zapico
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

Internet of Things
energy efficiency
resource management
machine learning
data science

Published Papers (5 papers)

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Research

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45 pages, 3785 KiB

Open AccessArticle

A Hybrid Methodology to Assess Cyber Resilience of IoT in Energy Management and Connected Sites

by Amjad Mehmood, Gregory Epiphaniou, Carsten Maple, Nikolaos Ersotelos and Richard Wiseman

Sensors 2023, 23(21), 8720; https://doi.org/10.3390/s23218720 - 25 Oct 2023

Viewed by 804

Abstract

Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors (“hackers”), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today’s CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014–present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing “state-of-the-art” mechanisms. Full article

(This article belongs to the Special Issue Sustainable IoT Solutions for Industrial Applications)

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20 pages, 2211 KiB

Open AccessArticle

Data Integration from Heterogeneous Control Levels for the Purposes of Analysis within Industry 4.0 Concept

by Tibor Horak, Peter Strelec, Michal Kebisek, Pavol Tanuska and Andrea Vaclavova

Sensors 2022, 22(24), 9860; https://doi.org/10.3390/s22249860 - 15 Dec 2022

Cited by 2 | Viewed by 1810

Abstract

Small- and medium-sized manufacturing companies must adapt their production processes more quickly. The speed with which enterprises can apply a change in the context of data integration and historicization affects their business. This article presents the possibilities of implementing the integration of control processes using modern technologies that will enable the adaptation of production lines. Integration using an object-oriented approach is suitable for complex tasks. Another approach is data integration using the entity referred to as tagging (TAG). Tagging is essential to apply for fast adaptation and modification of the production process. The advantage is identification, easier modification, and generation of data structures where basic entities include attributes, topics, personalization, locale, and APIs. This research proposes a model for integrating manufacturing enterprise data from heterogeneous levels of management. As a result, the model and the design procedure for data integrating production lines can efficiently adapt production changes. Full article

(This article belongs to the Special Issue Sustainable IoT Solutions for Industrial Applications)

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25 pages, 1776 KiB

Open AccessArticle

Evaluation of Full-Duplex SWIPT Cooperative NOMA-Based IoT Relay Networks over Nakagami-m Fading Channels

by Tien-Tung Nguyen, Sang Quang Nguyen, Phu X. Nguyen and Yong-Hwa Kim

Sensors 2022, 22(5), 1974; https://doi.org/10.3390/s22051974 - 03 Mar 2022

Cited by 15 | Viewed by 2162

Abstract

In this paper, we investigate the performance of non-orthogonal multiple access (NOMA)-based full-duplex Internet-of-Things (IoT) relay systems with simultaneous wireless information and power transfer (SWIPT) over Nakagami-m fading channels to improve the performance of a cell-edge user under perfect and imperfect successive interference cancellation (SIC). Two scenarios, i.e., direct and non-direct links, between the source node and cell-edge user are examined. The exact closed-form analytical and approximate expressions for the outage probability, system throughput, energy efficiency, and ergodic capacities are derived and validated via Monte Carlo simulations to characterize the proposed system performance. To further improve the system performance, we also provide a low-complexity algorithm to maximize the system throughput over-optimizing the time-switching factor. The results show that our proposed NOMA system can achieve superior performance compared to its orthogonal multiple access (OMA) counterpart under perfect SIC and with a low-to-medium signal-to-noise ratio under imperfect SIC, according to the level of residual self-interference and the quality of links. Full article

(This article belongs to the Special Issue Sustainable IoT Solutions for Industrial Applications)

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Review

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38 pages, 1676 KiB

Open AccessReview

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry

by Muhammad Shoaib Farooq, Muhammad Abdullah, Shamyla Riaz, Atif Alvi, Furqan Rustam, Miguel Angel López Flores, Juan Castanedo Galán, Md Abdus Samad and Imran Ashraf

Sensors 2023, 23(21), 8958; https://doi.org/10.3390/s23218958 - 03 Nov 2023

Cited by 2 | Viewed by 2456

Abstract

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented. Full article

(This article belongs to the Special Issue Sustainable IoT Solutions for Industrial Applications)

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25 pages, 677 KiB

Open AccessReview

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

by Arslan Musaddiq, Tobias Olsson and Fredrik Ahlgren

Sensors 2023, 23(19), 8263; https://doi.org/10.3390/s23198263 - 06 Oct 2023

Cited by 1 | Viewed by 1145

Abstract

Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study. Full article

(This article belongs to the Special Issue Sustainable IoT Solutions for Industrial Applications)

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