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Applied Sciences
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Machine Learning for Blockchain and IoT Systems in Smart Cities
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Machine Learning for Blockchain and IoT Systems in Smart Cities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 8203

Special Issue Editor

Dr. Faisal Jamil

E-Mail Website
Guest Editor
Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
Interests: Internet of Things; blockchain; big data; machine learning; indoor navigation; digital twins; metaverse
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) technology allows machines to learn, think, and make intelligent decisions autonomously. The fundamental approach of ML is building efficient algorithms that are capable of predicting the future learned through experience. Blockchain, on the other hand, is distributed ledger technology that is immutable, decentralized, and provides secure storage of data without the need for a trusted third party. The convergence of ML and blockchain will complement each other to produce a greater impact and availability of different services, including healthcare, supply chain, transportation, and power sectors. These services include a large number of network elements and edge devices that generate a huge amount of data that raise potential security concerns and data optimization issues. Further, with the emergence of the Internet of Things (IoT), the nature of interactions and attacks has become more sophisticated to generate falsified identities and control over the blockchain consensus. This Special Issue aims to highlight advances in the open research topics in this field, which include but are not limited to the following:

  • Optimize existing machine learning architecture for embedded IoT devices;
  • Lightweight machine learning architecture and frameworks;
  • Distributed predictive optimization;
  • Positioning systems and infrastructures;
  • Energy-saving and energy-harvesting methods and techniques;
  • Blockchain for security and privacy;
  • Data collection and management methods (big data and data retrieval);
  • Lightweight intelligent IoT service orchestration;
  • Intelligent IoT for lightweight driver-assistance systems in electric vehicles;
  • Knowledge-based techniques for the IoT;
  • Optimization methods for IoTAI-enabled scalable blockchain for IoT.

Dr. Faisal Jamil
Guest Editor

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. Applied Sciences 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

  • blockchain
  • Internet of Things
  • indoor localization
  • service orchestration
  • virtualization
  • digital twin
  • big data
  • machine learning
  • edge computing

Published Papers (3 papers)

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Research

11 pages, 2139 KiB  
Article
Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT
by Ankita, Shalli Rani, Aman Singh, Dalia H. Elkamchouchi and Irene Delgado Noya
Appl. Sci. 2022, 12(13), 6442; https://doi.org/10.3390/app12136442 - 24 Jun 2022
Cited by 4 | Viewed by 1539
Abstract
Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as [...] Read more.
Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart Cities)
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17 pages, 4004 KiB  
Article
Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment
by A. Al-Qarafi, Fadwa Alrowais, Saud S. Alotaibi, Nadhem Nemri, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Radwa Marzouk, Mahmoud Othman and M. Al-Shabi
Appl. Sci. 2022, 12(12), 5893; https://doi.org/10.3390/app12125893 - 09 Jun 2022
Cited by 31 | Viewed by 3050
Abstract
Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several [...] Read more.
Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart Cities)
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22 pages, 1652 KiB  
Article
Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach
by Sadiqa Jafari, Zeinab Shahbazi and Yung-Cheol Byun
Appl. Sci. 2022, 12(4), 1992; https://doi.org/10.3390/app12041992 - 14 Feb 2022
Cited by 13 | Viewed by 2441
Abstract
As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance [...] Read more.
As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, a novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection’s stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart Cities)
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