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Future Internet
Special Issues
Artificial Intelligence and Blockchain Technology for Smart Cities
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Artificial Intelligence and Blockchain Technology for Smart Cities

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

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 22441

Special Issue Editors

Dr. Alessandro Sebastian Podda

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: artificial intelligence; deep learning; information security; financial forecasting; blockchain; smart contracts
Special Issues, Collections and Topics in MDPI journals
Dr. Livio Pompianu

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: information security; blockchain analytics; smart contracts; cryptocurrency scams
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The spread of smart cities and the resulting increase in the number of devices and users involved pose many challenges to making the best use of available data, such as the need for techniques for processing large masses of heterogeneous data and enhancing privacy and traceability.

Both blockchain and artificial intelligence technologies are helping to address the challenges of smart cities. Indeed, although artificial intelligence provides methods for representing, learning and reasoning about complex information, blockchain technology offers programs whose correct execution is automatically applied without relying on a trusted authority.

This Special Issue is dedicated to interdisciplinary research in the areas of blockchain and AI systems for smart cities. It calls for cutting-edge contributions to fundamental theoretical research as well as its application in practice. This Special Issue covers, but is not limited to, the following topics:

  • Design, models and specifications of systems and services for smart cities;
  • Machine learning and deep learning methods for smart cities;
  • Blockchain-based solutions for smart cities;
  • Smart contracts for smart cities;
  • Decentralized autonomous organizations (DAOs) for smart cities;
  • Privacy, security and trust;
  • People and objects positioning/detection;
  • Embedded systems and software for smart cities;
  • Fog and edge computing;
  • Smart grids and smart infrastructures;
  • Smart city applications (e.g., health, transport, tourism, logistics);
  • Proofs-of-concept of smart systems;
  • Implementation, integration, testing and deployment issues.

Dr. Alessandro Sebastian Podda
Dr. Livio Pompianu
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

  • smart cities
  • artificial Intelligence
  • blockchain
  • IoT applications security privacy

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Published Papers (6 papers)

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Research

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24 pages, 21174 KiB  
Article
An Ensemble Deep CNN Approach for Power Quality Disturbance Classification: A Technological Route Towards Smart Cities Using Image-Based Transfer
by Mirza Ateeq Ahmed Baig, Naeem Iqbal Ratyal, Adil Amin, Umar Jamil, Sheroze Liaquat, Haris M. Khalid and Muhammad Fahad Zia
Future Internet 2024, 16(12), 436; https://doi.org/10.3390/fi16120436 - 22 Nov 2024
Viewed by 130
Abstract
The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid [...] Read more.
The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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19 pages, 21112 KiB  
Article
Predicting the Duration of Forest Fires Using Machine Learning Methods
by Constantina Kopitsa, Ioannis G. Tsoulos, Vasileios Charilogis and Athanassios Stavrakoudis
Future Internet 2024, 16(11), 396; https://doi.org/10.3390/fi16110396 - 28 Oct 2024
Viewed by 2069
Abstract
For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance [...] Read more.
For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance not only of government agencies around the world, but also have an effect on climate change. Forest fires have become more intense, more destructive, and more deadly; these are known as megafires. They can cause major economic and ecological problems, especially in the summer months (dry season). However, humanity has developed a tool that can predict fire events, to detect them in time, but also to predict their duration. This tool is artificial intelligence, specifically, machine learning, which is one part of AI. Consequently, this paper briefly mentions several methods of machine learning as used in predicting forest fires and in early detection, submitting an overall review of current models. Our main overall objective is to venture into a new field: predicting the duration of ongoing forest fires. Our contribution offers a new way to manage forest fires, using accessible open data, available from the Hellenic Fire Service. In particular, we imported over 72,000 data from a 10-year period (2014–2023) using machine learning techniques. The experimental and validation results are more than encouraging, with Random Forest achieving the lowest value for the error range (8–13%), meaning it was 87–92% accurate on the prediction of forest fire duration. Finally, some future directions in which to extend this research are presented. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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33 pages, 4252 KiB  
Article
Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
by Igor Kabashkin and Leonid Shoshin
Future Internet 2024, 16(8), 276; https://doi.org/10.3390/fi16080276 - 2 Aug 2024
Cited by 3 | Viewed by 4713
Abstract
The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. [...] Read more.
The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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21 pages, 11273 KiB  
Article
Technical, Qualitative and Energy Analysis of Wireless Control Modules for Distributed Smart Home Systems
by Andrzej Ożadowicz
Future Internet 2023, 15(9), 316; https://doi.org/10.3390/fi15090316 - 20 Sep 2023
Cited by 1 | Viewed by 2135
Abstract
Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it [...] Read more.
Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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17 pages, 2434 KiB  
Article
Blockchain Solution for Buildings’ Multi-Energy Flexibility Trading Using Multi-Token Standards
by Oana Marin, Tudor Cioara and Ionut Anghel
Future Internet 2023, 15(5), 177; https://doi.org/10.3390/fi15050177 - 10 May 2023
Cited by 7 | Viewed by 2774
Abstract
Buildings can become a significant contributor to an energy system’s resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-carrier optimization (i.e., electricity), aiming to achieve Zero [...] Read more.
Buildings can become a significant contributor to an energy system’s resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-carrier optimization (i.e., electricity), aiming to achieve Zero Energy Buildings, and miss the significant flexibility that buildings may offer through multi-energy coupling. In this paper, we propose to use blockchain technology and ERC-1155 tokens to digitize the heat and electrical energy flexibility of buildings, transforming them into active flexibility assets within integrated multi-energy grids, allowing them to trade both heat and electricity within community-level marketplaces. The solution increases the level of interoperability and integration of the buildings with community multi-energy grids and brings advantages from a transactive perspective. It permits digitizing multi-carrier energy using the same token and a single transaction to transfer both types of energy, processing transaction batches between the sender and receiver addresses, and holding both fungible and non-fungible tokens in smart contracts to support energy markets’ financial payments and energy transactions’ settlement. The results show the potential of our solution to support buildings in trading heat and electricity flexibility in the same market session, increasing their interoperability with energy markets while decreasing the transactional overhead and gas consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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Review

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43 pages, 3225 KiB  
Review
Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions
by Shrouk A. Ali, Shaimaa Ahmed Elsaid, Abdelhamied A. Ateya, Mohammed ElAffendi and Ahmed A. Abd El-Latif
Future Internet 2023, 15(12), 398; https://doi.org/10.3390/fi15120398 - 9 Dec 2023
Cited by 9 | Viewed by 9379
Abstract
The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling [...] Read more.
The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling technologies that will shape their development. This work reviews the leading technologies driving the future of smart cities. The work begins by introducing the main requirements of different smart city applications; then, the enabling technologies are presented. This work highlights the transformative potential of the Internet of things (IoT) to facilitate data collection and analysis to improve urban infrastructure and services. As a complementary technology, distributed edge computing brings computational power closer to devices, reducing the reliance on centralized data centers. Another key technology is virtualization, which optimizes resource utilization, enabling multiple virtual environments to run efficiently on shared hardware. Software-defined networking (SDN) emerges as a pivotal technology that brings flexibility and scalability to smart city networks, allowing for dynamic network management and resource allocation. Artificial intelligence (AI) is another approach for managing smart cities by enabling predictive analytics, automation, and smart decision making based on vast amounts of data. Lastly, the blockchain is introduced as a promising approach for smart cities to achieve the required security. The review concludes by identifying potential research directions to address the challenges and complexities brought about by integrating these key enabling technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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