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Sustainability
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Green ICT, Artificial Intelligence and Smart Cities
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Green ICT, Artificial Intelligence and Smart Cities

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 14353

Special Issue Editors

Dr. Bernardi Pranggono

E-Mail Website
Guest Editor
School of Computing and Information Science, Anglia Ruskin University, Cambridge, UK
Interests: smart grid; cybersecurity; cloud computing; Internet of Things; green ICT
Special Issues, Collections and Topics in MDPI journals
Dr. Alex Shenfield

E-Mail Website
Guest Editor
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK
Interests: artificial intelligence; cybersecurity; Internet of Things; cognitive radio; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Augustine Ikpehai

E-Mail Website
Guest Editor
Department of Engineering and Mathematics, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
Interests: smart grid technologies; IoT systems; SDN/NFV; power line communication; energy management; intelligent systems for critical infrastructure; smart wireless power transfer; transactive energy systems and resource allocation & control in communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the global urban population is growing significantly, governments around the world rely heavily on Information and Communication Technology (ICT) in developing smart and hyper-connected infrastructure that allow cities to provide better services to citizens and reduce energy consumption. These smart technologies enable cities to implement new ways to monitor the environment, buildings, street lighting, traffic, crowds, crimes, etc.

ICT is the underlying technology in forming a smarter and more intelligent city around the world; however, if not implemented wisely, ICT can be a major environmental issue. Currently, the global ICT industry accounts for approximately 2% of global greenhouse gas (GHG) emissions. According to a recent study, this footprint is predicted to increase significantly to around 14% by 2040. Intelligent use of ICT innovations can help with the reduction of GHG while still achieving its objectives. Based on the Global e-Sustainability Initiative (GeSI) study, ICT potentially has the capability to cut the carbon footprint of other areas by a factor of 10. Therefore, ICT innovations such as Green ICT, Internet of Things (IoT), and Artificial Intelligence (AI) will play a major role not only in making a city smarter but also greener and more sustainable.

This Special Issue on Green ICT, Artificial Intelligence, and Smart Cities aims to bring together researchers and practitioners interested in the advances and applications of ICT in the fields of Smart Cities, Green Information and Communication Technologies, Artificial Intelligence, Sustainability, and Energy-Aware Systems.

The broad areas include but not limited to:

  • Smart Cities Frameworks
  • Green Information and Communication Technologies
  • Artificial Intelligence
  • Wireless Sensor Networks
  • Energy-Saving in Data Centers
  • Energy-Aware Protocols
  • Cyber-Physical Systems
  • Low-Power Networks
  • Connected Autonomous Vehicles
  • Smart Grid
  • Energy Harvesting in Cellular Networks (including RF)
  • Smart Homes
  • Energy-Efficient Network Topologies & Architectures
  • Edge Computing
  • Crowd Sensing

Dr. Bernardi Pranggono
Dr. Alex Shenfield
Dr. Augustine Ikpehai
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

  • Green ICT
  • Artificial Intelligence
  • Machine Learning
  • Internet of Things
  • Big Data Analytics
  • Smart Sustainable Cities
  • Safety, Security, and Privacy for Smart Cities

Published Papers (5 papers)

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Research

25 pages, 6898 KiB  
Article
Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control
by Jiří David, Pavel Brom, František Starý, Josef Bradáč and Vojtěch Dynybyl
Sustainability 2021, 13(8), 4572; https://0-doi-org.brum.beds.ac.uk/10.3390/su13084572 - 20 Apr 2021
Cited by 5 | Viewed by 2392
Abstract
This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the [...] Read more.
This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety. Full article
(This article belongs to the Special Issue Green ICT, Artificial Intelligence and Smart Cities)
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18 pages, 9041 KiB  
Article
Usage of Real Time Machine Vision in Rolling Mill
by Jiří David, Pavel Švec, Vít Pasker and Romana Garzinová
Sustainability 2021, 13(7), 3851; https://0-doi-org.brum.beds.ac.uk/10.3390/su13073851 - 31 Mar 2021
Cited by 12 | Viewed by 2323
Abstract
This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm [...] Read more.
This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm allows the conversion of image information from the front of the billet, which enters the rolling process, into a string of characters, which is further used to control the technological process. The purpose of this identification is to prevent the input pieces from being confused because different parameters of the rolling process are set for different pieces. In solving this task, it was necessary to design the optimal technical equipment for image capture, choose the appropriate lighting, search for text and recognize individual symbols, and insert them into the control system. The research methodology is based on the empirical-quantitative principle, the basis of which is the analysis of experimentally obtained data (photographs of billet faces) in real operating conditions leading to their interpretation (transformation into the shape of a digital chain). The first part of the article briefly describes the billet identification system from the point of view of technology and hardware resources. The next parts are devoted to the main parts of the algorithm of automatic identification—optical recognition of strings and recognition of individual characters of the chain using artificial intelligence. The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further processing. Successful implementation of the automatic inspection system will increase the share of operation automation and lead to ensuring automatic inspection of steel billets according to the production plan. This issue is related to the trend of digitization of individual technological processes in metallurgy and also to the social sustainability of processes, which means the elimination of human errors in the management of the billet rolling process. Full article
(This article belongs to the Special Issue Green ICT, Artificial Intelligence and Smart Cities)
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12 pages, 824 KiB  
Article
A Fair and Secure Reverse Auction for Government Procurement
by Chia-Chen Lin, Ya-Fen Chang, Chin-Chen Chang and Yao-Zhu Zheng
Sustainability 2020, 12(20), 8567; https://0-doi-org.brum.beds.ac.uk/10.3390/su12208567 - 16 Oct 2020
Cited by 2 | Viewed by 2083
Abstract
With the development of e-commerce, the electronic auction is attracting the attention of many people. Many Internet companies, such as eBay and Yahoo!, have launched online auction systems. Many researchers have studied the security problems of electronic auction systems, but few of them [...] Read more.
With the development of e-commerce, the electronic auction is attracting the attention of many people. Many Internet companies, such as eBay and Yahoo!, have launched online auction systems. Many researchers have studied the security problems of electronic auction systems, but few of them are multi-attribute-based. In 2014, Shi proposed a provable secure, sealed-bid, and multi-attribute auction protocol based on the semi-honest model. We evaluated this protocol and found that it has some design weaknesses and is vulnerable to the illegal operations of buyers, which results in unfairness. In this paper, we improved this protocol by replacing the Paillier’s cryptosystem with the elliptic curve discrete (ECC), and we designed a novel, online, and multi-attribute reverse-auction system using the semi-honest model. In our system, sellers’ identities are not revealed to the buyers, and the buyers cannot conduct illegal operations that may compromise the fairness of the auction. Full article
(This article belongs to the Special Issue Green ICT, Artificial Intelligence and Smart Cities)
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14 pages, 2763 KiB  
Article
Rewritable and Sustainable 2D Barcode for Traceability Application in Smart IoT Based Fault-Tolerant Mechanism
by Rongjun Chen, Yongxing Yu, Shundao Xie, Huimin Zhao, Songjin Liu, Jinchang Ren and Hong-Zhou Tan
Sustainability 2020, 12(17), 7192; https://0-doi-org.brum.beds.ac.uk/10.3390/su12177192 - 03 Sep 2020
Cited by 6 | Viewed by 2617
Abstract
With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the existing 2D barcode cannot be changed once [...] Read more.
With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the existing 2D barcode cannot be changed once it is printed, it can only be replaced with more expensive radio frequency identification (RFID) labels or new 2D barcodes, causing a waste of human resources and costs. For better circulation efficiency and resource utilization, we propose a new design of the rewritable and sustainable 2D barcode based on the fault-tolerance mechanism. The ability to add new information in the 2D barcode can be achieved through data encryption and the insertion of a rewritable layer. It means the message of 2D barcodes could be changed, and increases the flexibility and liquidity of the 2D barcode application. Besides, the encoding and decoding method of the proposed 2D barcode is presented. Experimental results have illustrated the superiority of rewritable and sustainable 2D barcodes in the traceability of herbal medicine compared with the conventional 2D barcodes, and demonstrated the feasibility of the design. The findings show the potential for significant application in the field of traceability in smart IoT, as well as in the manufacturing industry and logistics. Full article
(This article belongs to the Special Issue Green ICT, Artificial Intelligence and Smart Cities)
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20 pages, 2015 KiB  
Article
An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services
by Sohani Liyanage and Hussein Dia
Sustainability 2020, 12(10), 4117; https://0-doi-org.brum.beds.ac.uk/10.3390/su12104117 - 18 May 2020
Cited by 16 | Viewed by 3670
Abstract
On-demand multi-passenger shared transport options are increasingly being promoted as an influential strategy to reduce traffic congestion and emissions and improve the convenience and travel experience for passengers. These services, often referred to as on-demand public transport, are aimed at meeting personal travel [...] Read more.
On-demand multi-passenger shared transport options are increasingly being promoted as an influential strategy to reduce traffic congestion and emissions and improve the convenience and travel experience for passengers. These services, often referred to as on-demand public transport, are aimed at meeting personal travel demands through the use of shared vehicles that run on flexible routes using advanced tools for dynamic scheduling. This paper presents an agent-based traffic simulation model that was developed to evaluate the performance of on-demand public transport and compare it with existing scheduled bus services using a case study of the inner city of Melbourne in Australia. The key performance measures used in the comparative evaluation included quality of service and passenger experience in terms of waiting times, the efficiency of service and operations in terms of hourly vehicle utilization, and system efficiency in terms of trip completion rates, passenger kilometers travelled and total passenger trip times. The results showed significant benefits for passengers who use on-demand bus services compared to scheduled bus services. The on-demand bus service was found to reduce average total passenger waiting times by 89% during the Morning Peak; by 78% during the Mid-Day period; by 81% during the Afternoon Peak; and by more than 95% during other periods of the day. From an operator’s perspective, the on-demand services were found to achieve around 70% vehicle utilization rates during peak hours compared to a utilization rate not exceeding 16% for the scheduled bus services. Even during off-peak periods, the occupancies for on-demand services were almost twice the vehicle occupancies for scheduled bus services. In terms of system efficiency, the on-demand services achieved a trip completion rate of 85% compared to a trip completion rate of 67% for the scheduled bus services. The total passenger-kilometers travelled was similar for both scheduled and on-demand bus services, which refutes claims that on-demand bus services induce more kilometers of travel. The trip completion times were around 55% shorter for on-demand bus services compared to scheduled services, which represents a significant saving in travel time for users. Finally, the paper presents average emissions per completed trip for both types of services and shows a significant reduction in emissions for on-demand services compared to conventional bus services. These include, on average, a 48% reduction in CO2 emissions per trip; 82% reduction in NO emissions per trip; and 41% reduction in p.m.10 emissions per trip. These findings clearly demonstrate the superior benefits of on-demand bus services compared to scheduled bus services. Full article
(This article belongs to the Special Issue Green ICT, Artificial Intelligence and Smart Cities)
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