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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7638

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Special Issue Editors

Prof. Dr. Miodrag Živković

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Guest Editor

Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
Interests: artificial intelligence; swarm intelligence; optimization metaheuristics; wireless sensor networks; Internet of Things; mobile applications development
Special Issues, Collections and Topics in MDPI journals

Dr. Nebojsa Bacanin

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Guest Editor

Faculty of Informatics and Computing, Singidunum University, 11010 Belgrade, Serbia
Interests: swarm intelligence; optimization metaheuristics; machine learning; deep learning; computer vision; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bosko Nikolic

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Guest Editor

The Department of Computer Science and Information Technology, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
Interests: artificial intelligence; machine learning; NLP; visual simulation

Special Issue Information

Dear Colleagues,

The fast development of cities and the continuous increase in urban populations gave rise to a number of unparalleled environmental challenges that affect natural resources, energy sources, and citizens living in densely populated areas. The most common problems that arise as a consequence of urbanization include air pollution, carbon emissions, low water quality, and high energy consumption, among many others. Additionally, these factors affect human health to a large extent, which should also be addressed by so-called “green technologies”. All these challenges need to be tackled by novel approaches that promote sustainable city development, and possible solutions may come to light from the domain of sustainable information engineering and computer science. Information plays a vital role in sustainable development through modeling, simulations, artificial intelligence (AI), cloud computing, big data, Internet of Things (IoT), network security, etc. Often, several techniques must be combined to achieve complex goals.

The main goal of this Special Issue is the presentation of cutting-edge technologies and techniques that fall into the computer science domain, which can help in the development of sustainable solutions for the above-mentioned challenges.

This Special Issue on “Sustainable Information Engineering and Computer Science” will address this critical, extensive, and recent topic. We encourage original research articles or comprehensive reviews from diverse disciplines. Examples of general topics covered in this Special Issue include, but are not limited to, the following:

Sustainable Industry 4.0 and Healthcare 4.0 applications
Sustainable Internet of Things and wireless sensor network solutions
Sustainable artificial intelligence
Sustainable machine learning
Sustainable cloud and edge computing
Simulation and modeling
Signal processing techniques for IoT-based environments
Improving computer system security for sustainable information engineering and computer science solutions
Feature selection by metaheuristics approaches for sustainable information engineering and computer science solutions
Tuning and training machine learning models by metaheuristics for sustainable information engineering and computer science solutions
Univariate and multivariate time-series prediction by machine learning and statistical learning approaches for sustainable information engineering and computer science solutions

Prof. Dr. Miodrag Živković
Dr. Nebojsa Bacanin
Prof. Dr. Bosko Nikolic
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. Sustainability 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 2400 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

sustainable information engineering
sustainable computer science
sustainable data
sustainable AI solutions
green technologies
sustainable cities

Published Papers (5 papers)

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Research

16 pages, 995 KiB

Open AccessArticle

Analyzing the Impact of COVID-19 on GitHub Event Trends

by Nikola Pejić, Zaharije Radivojević and Miloš Cvetavnović

Sustainability 2023, 15(19), 14622; https://doi.org/10.3390/su151914622 - 9 Oct 2023

Viewed by 826

Abstract

Economic development, as one of the three pillars of sustainability, can be enhanced by utilizing open-source software. The impact of the pandemic on software development and whether or not it managed to sustain the velocity and volume it previously had has already piqued the interest of the research community. From measuring the activity of developers to conducting surveys on the perceived productivity, the research was mostly focused on 2020. This paper focuses on how the pandemic impacted public development on GitHub by analyzing the changes in trends during the 2020–2022 period (COVID-19 period) compared with 2017–2019 (pre-COVID-19 period). While the majority of events have continued with relatively the same trend, having minor increases or decreases, there have been a few that stood out. Several events related to the community activity on GitHub experienced decreases in their trends (ForkEvent’s trend decreased

0.09

×, IssuesEvent

0.01

×, etc.), while events related to discussions have experienced a positive trend (mainly CommitCommentEvent, with a trend increase of 34×, but also IssueCommentEvent, which remained stable despite the

0.01

× decrease of IssuesEvent), but only CommitCommentEvent and PushEvent (which experienced a

2.52

× increase in its trend) exhibited non-stationary behavior in the ADF test. In general, events related to individual development have sustained or increased their trends, while events related to community activity (i.e., forking) or contributions to documentation have somewhat decreased. We believe this shows that although there have been minor reductions in the casual and community contributions on GitHub, the majority of events continued with the same trend or even with some increases, which shows that GitHub developers persevered in the face of the pandemic. Full article

(This article belongs to the Special Issue Sustainable Information Engineering and Computer Science)

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23 pages, 2518 KiB

Open AccessArticle

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

by Goran Savić, Milan Segedinac, Zora Konjović, Milan Vidaković and Radoslav Dutina

Sustainability 2023, 15(18), 13612; https://doi.org/10.3390/su151813612 - 12 Sep 2023

Viewed by 890

Abstract

In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory. Full article

(This article belongs to the Special Issue Sustainable Information Engineering and Computer Science)

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28 pages, 1367 KiB

Open AccessArticle

Intrusion Detection in Healthcare 4.0 Internet of Things Systems via Metaheuristics Optimized Machine Learning

by Nikola Savanović, Ana Toskovic, Aleksandar Petrovic, Miodrag Zivkovic, Robertas Damaševičius, Luka Jovanovic, Nebojsa Bacanin and Bosko Nikolic

Sustainability 2023, 15(16), 12563; https://doi.org/10.3390/su151612563 - 18 Aug 2023

Cited by 16 | Viewed by 1572

Abstract

Rapid developments in Internet of Things (IoT) systems have led to a wide integration of such systems into everyday life. Systems for active real-time monitoring are especially useful in areas where rapid action can have a significant impact on outcomes such as healthcare. However, a major challenge persists within IoT that limit wider integration. Sustainable healthcare supported by the IoT must provide organized healthcare to the population, without compromising the environment. Security plays a major role in the sustainability of IoT systems, therefore detecting and taking timely action is one step in overcoming the sustainability challenges. This work tackles security challenges head-on through the use of machine learning algorithms optimized via a modified Firefly algorithm for detecting security issues in IoT devices used for Healthcare 4.0. Metaheuristic solutions have contributed to sustainability in various areas as they can solve nondeterministic polynomial time-hard problem (NP-hard) problems in realistic time and with accuracy which are paramount for sustainable systems in any sector and especially in healthcare. Experiments on a synthetic dataset generated by an advanced configuration tool for IoT structures are performed. Also, multiple well-known machine learning models were used and optimized by introducing modified firefly metaheuristics. The best models have been subjected to SHapley Additive exPlanations (SHAP) analysis to determine the factors that contribute to occurring issues. Conclusions from all the performed testing and comparisons indicate significant improvements in the formulated problem. Full article

(This article belongs to the Special Issue Sustainable Information Engineering and Computer Science)

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20 pages, 3941 KiB

Open AccessArticle

Smart Agricultural–Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle–Internet of Things Classification Techniques

by K. Vijayalakshmi, Shaha Al-Otaibi, Leena Arya, Mohammed Amin Almaiah, T. P. Anithaashri, S. Sam Karthik and Rima Shishakly

Sustainability 2023, 15(14), 11242; https://doi.org/10.3390/su151411242 - 19 Jul 2023

Cited by 3 | Viewed by 1498

Abstract

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters—namely, accuracy, precision, and specificity—were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops. Full article

(This article belongs to the Special Issue Sustainable Information Engineering and Computer Science)

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16 pages, 1929 KiB

Open AccessArticle

Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks

by Shaha Al-Otaibi, Venkatesan Cherappa, Thamaraimanalan Thangarajan, Ramalingam Shanmugam, Prithiviraj Ananth and Sivaramakrishnan Arulswamy

Sustainability 2023, 15(7), 5759; https://doi.org/10.3390/su15075759 - 25 Mar 2023

Cited by 9 | Viewed by 1608

Abstract

Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency. Full article

(This article belongs to the Special Issue Sustainable Information Engineering and Computer Science)

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