Machine Learning for Blockchain and IoT Systems in Smart City

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 8065

Special Issue Editors


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Guest Editor
School of Engineering, University of Mount Union, Alliance, OH 44601-3993, USA
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
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Special Issue Information

Dear Colleagues,

The role of machine learning in implementing and facilitating the design and development of smart cities cannot be overstated. The design of secured smart cities requires a comprehensive analysis of complex Internet of Things (IoT) data. Traditional analytical tools lack the desired accuracy, consume huge energy resources, and are cost prohibitive. Further, smart city data are transmitted over open wireless channels, posing huge security risks. In order to address these problems, machine learning tools and Blockchain technology have been gainfully exploited. Machine learning supports the application of IoT systems to enhance smart cities socially, economically and environmentally. Blockchain complements IoT for enhanced interoperability, reliability, and scalability, with guaranteed security and trust. However, the integration of Blockchain and IoT in smart cities has not been widely considered in the literature. This Special Issue presents machine learning for Blockchain and IoT systems in smart cities. The Special Issue calls for original contributions on designing and developing sustainable machine learning techniques for Blockchain and IoT systems in emerging smart cities.

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

  • Machine learning tools for Blockchain and IoT systems in smart cities
  • Sustainable ML models for Blockchain and IoT systems in smart cities
  • Digital twins enabling Blockchain and IoT systems in smart cities
  • Blockchain and IoT systems enabling energy-efficient smart cities
  • Harnessing machine learning for human mobility prediction in smart cities
  • Novel architectures for ML-assisted Blockchain and IoT systems in smart cities
  • Hardware implementation of ML-empowered Blockchain and IoT systems in smart cities
  • Leveraging machine learning in reducing carbon footprint in smart cities
  • Machine learning aiding visualization of IoT-assisted smart cities
  • Security and privacy challenges for Blockchain and IoT systems in smart cities
  • Experimentation and deployment of Blockchain-based technology for smart cities
  • Blockchain facilitates cloud, rain, and fog computing in IoT-enabled smart cities
  • Location-aware and location-based services for Blockchain and IoT-assisted smart cities
  • Performance analysis and real measurement for Blockchain-based IoT applications
  • Machine learning and Blockchain-enabled smart cities and sustainable environments
  • Social and economic policies on Blockchain and IoT systems in smart cities
  • Case studies and recommendations for designing and developing emerging smart cities

This Special Issue will provide novel contributions that will drive cutting-edge research, leading to the development of smart cities of the future, leveraging Blockchain technology and the Internet of Things. Quality submissions from academia and industry are welcome.

Finally, I would like to thank Agbotiname Lucky Imoize and his valuable work for assisting me with this Special Issue.

Prof. Dr. Cheng-Chi Lee
Dr. Dinh-Thuan Do
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

  • machine learning
  • internet of things
  • blockchain technology
  • smart cities
  • security and privacy
  • wireless communication
  • artificial intelligence
  • energy efficiency

Published Papers (5 papers)

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Research

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33 pages, 5150 KiB  
Article
Securing Critical User Information over the Internet of Medical Things Platforms Using a Hybrid Cryptography Scheme
by Oluwakemi Christiana Abikoye, Esau Taiwo Oladipupo, Agbotiname Lucky Imoize, Joseph Bamidele Awotunde, Cheng-Chi Lee and Chun-Ta Li
Future Internet 2023, 15(3), 99; https://doi.org/10.3390/fi15030099 - 28 Feb 2023
Cited by 5 | Viewed by 1496
Abstract
The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully [...] Read more.
The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully maximized. Undoubtedly, a secure IoMT model that preserves individual users’ privacy will enhance the wide acceptability of IoMT technology. However, existing works that have attempted to solve these privacy and insecurity problems are not space-conservative, computationally intensive, and also vulnerable to security attacks. In this paper, an IoMT-based model that conserves the privacy of the data, is less computationally intensive, and is resistant to various cryptanalysis attacks is proposed. Specifically, an efficient privacy-preserving technique where an efficient searching algorithm through encrypted data was used and a hybrid cryptography algorithm that combines the modification of the Caesar cipher with the Elliptic Curve Diffie Hellman (ECDH) and Digital Signature Algorithm (DSA) were projected to achieve user data security and privacy preservation of the patient. Furthermore, the modified algorithm can secure messages during transmission, perform key exchanges between clients and healthcare centres, and guarantee user authentication by authorized healthcare centres. The proposed IoMT model, leveraging the hybrid cryptography algorithm, was analysed and compared against different security attacks. The analysis results revealed that the model is secure, preserves the privacy of critical user information, and shows robust resistance against different cryptanalysis attacks. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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21 pages, 1988 KiB  
Article
Identification of Risk Factors Using ANFIS-Based Security Risk Assessment Model for SDLC Phases
by Rasheed Gbenga Jimoh, Olayinka Olufunmilayo Olusanya, Joseph Bamidele Awotunde, Agbotiname Lucky Imoize and Cheng-Chi Lee
Future Internet 2022, 14(11), 305; https://doi.org/10.3390/fi14110305 - 26 Oct 2022
Cited by 2 | Viewed by 1843
Abstract
In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each [...] Read more.
In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each phase of SDLC is faced with its individual type of risk rather than considering it as a general risk. Therefore, this study proposes an adaptive neuro-fuzzy inference system (ANFIS) for selection of appropriate risk factors in each stages of software development process. Existing studies viewed the SDLC’s Security risk assessment (SRA) as a single integrated process that did not offer a thorough SRA at each stage of the SDLC process, which resulted in unsecure software development. Hence, this study identify and validate the risk factors needed for assessing security risk at each phase of SDLC. For each phase, an SRA model based on an ANFIS was suggested, using the identified risk factors as inputs. For the logical representation of the fuzzification as an input and output variables of the SRA risk factors for the ANFIS-based model employing the triangular membership functions. The proposed model utilized two triangular membership functions to represent each risk factor’s label, while four membership functions were used to represent the labels of the target SRA value. Software developers chose the SRA risk factors that were pertinent in their situation from the proposed taxonomy for each level of the SDLC process as revealed by the results. As revealed from the study’s findings, knowledge of the identified risk factors may be valuable for evaluating the security risk throughout the SDLC process. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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Review

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20 pages, 7164 KiB  
Review
A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions
by Md Motiur Rahman, Deepti Gupta, Smriti Bhatt, Shiva Shokouhmand and Miad Faezipour
Future Internet 2024, 16(4), 139; https://doi.org/10.3390/fi16040139 - 19 Apr 2024
Viewed by 827
Abstract
Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task [...] Read more.
Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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21 pages, 386 KiB  
Review
All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
by Georgios Koukis, Konstantina Safouri and Vassilis Tsaoussidis
Future Internet 2024, 16(4), 129; https://doi.org/10.3390/fi16040129 - 9 Apr 2024
Viewed by 1156
Abstract
Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array [...] Read more.
Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array of applications where DTN technologies have proven successful, with a focus on emerging and novel application paradigms. In particular, we focus on the contributions of DTN in the Future Internet, including its contribution to space applications, smart cities and the Internet of Things, but also to underwater communications. We also discuss its potential to be used jointly with information-centric networks to change the internet communication paradigm in the future. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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Other

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21 pages, 445 KiB  
Systematic Review
Factors Affecting Trust and Acceptance for Blockchain Adoption in Digital Payment Systems: A Systematic Review
by Tenzin Norbu, Joo Yeon Park, Kok Wai Wong and Hui Cui
Future Internet 2024, 16(3), 106; https://doi.org/10.3390/fi16030106 - 21 Mar 2024
Viewed by 1583
Abstract
Blockchain technology has become significant for financial sectors, especially digital payment systems, offering enhanced security, transparency, and efficiency. However, there is limited research on the factors influencing user trust in and acceptance of blockchain adoption in digital payment systems. This systematic review provides [...] Read more.
Blockchain technology has become significant for financial sectors, especially digital payment systems, offering enhanced security, transparency, and efficiency. However, there is limited research on the factors influencing user trust in and acceptance of blockchain adoption in digital payment systems. This systematic review provides insight into the key factors impacting consumers’ perceptions and behaviours towards embracing blockchain technology. A total of 1859 studies were collected, with 48 meeting the criteria for comprehensive analysis. The results showed that security, privacy, transparency, and regulation are the most significant factors influencing trust for blockchain adoption. The most influential factors identified in the Unified Theory of Acceptance and Use of Technology (UTAUT) model include performance expectancy, effort expectancy, social influence, and facilitating conditions. Incorporating a trust and acceptance model could be a viable approach to tackling obstacles and ensuring the successful integration of blockchain technology into digital payment systems. Understanding these factors is crucial for creating a favourable atmosphere for adopting blockchain technology in digital payments. User-perspective research on blockchain adoption in digital payment systems is still insufficient, and this aspect still requires further investigation. Blockchain adoption in digital payment systems has not been sufficiently conducted from the user’s perspective, and there is a scope for it to be carried out. This review aims to shed light on the factors of trust in and acceptance of blockchain adoption in digital payment systems so that the full potential of blockchain technology can be realised. Understanding these factors and their intricate connections is imperative in fostering a conducive environment for the widespread acceptance of blockchain technology in digital payments. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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