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Advances in Lightweight AI for Internet of Things Devices for Smart Cities

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 24408

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Special Issue Editors

Dr. Faisal Jamil

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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

Dr. Shabir Ahmad

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Guest Editor

Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Republic of Korea
Interests: Internet of Things; machine learning; data science; big data; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has been playing a vital role in adding value to human lives. In recent years, IoT applications have been coupled with machine learning techniques to form intelligent IoT applications. However, for intelligent IoT nodes, the machine learning technologies should be lightweight to meet the constrained capabilities of the embedded hardware. 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.

Dr. Faisal Jamil
Dr. Shabir Ahmad
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 papers will be 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. Processes 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 2000 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
internet of things
indoor localization
blockchain
service orchestration
virtualization
digital twin
big data

Published Papers (6 papers)

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Research

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18 pages, 1399 KiB

Open AccessArticle

Factor Model for Online Education during the COVID-19 Pandemic Using the IoT

by Faheem Khan, Ilhan Tarimer and Whangbo Taekeun

Processes 2022, 10(7), 1419; https://doi.org/10.3390/pr10071419 - 21 Jul 2022

Cited by 7 | Viewed by 2014

Abstract

Coronavirus disease (COVID-19) has spread quickly around the globe. COVID-19 has affected the education sector due to partial or complete lockdowns that were implemented throughout the world between 2019 and 2022. This pandemic severely affected the education sectors in developing countries such as Pakistan. All the educational institutions in Pakistan turned to online education. However, the education sector lacked the teaching experts, digital experts, the Internet of Things (IoT), and resources needed for online education. The shift from traditional to online education has created many challenges for developing countries during a pandemic such as COVID-19, for example, access to the IoT. This paper aims to introduce the factor model (F model), which will provide guidelines for the government and universities for minimizing the deficiencies related to online education. The F-model will identify all the factors that affect the performance and guide the user about their importance. This will allow the user to resolve that issue and improve the performance of their department or institution. Thus, the F model will benefit the education sector by mitigating the challenges related to online education. The F model is not only confined to online education but can be operated in the fields of science and industry for data extraction and the calculation of results. First, the data is collected physically and online through a student survey related to the challenges of online education during a pandemic. The data extraction and the calculation of the results are carried out using the F model. The results of the survey are alarming and the government has a lot of work to do to improve online education using the IoT. According to the F model, the government should take serious action to improve the performance of students, teachers, and all education sectors not only during the COVID-19 pandemic but also for possible future pandemics. Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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14 pages, 454 KiB

Open AccessFeature PaperArticle

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

by Sadiqa Jafari, Zeinab Shahbazi and Yung-Cheol Byun

Processes 2021, 9(12), 2205; https://doi.org/10.3390/pr9122205 - 07 Dec 2021

Cited by 8 | Viewed by 5677

Abstract

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller. Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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18 pages, 888 KiB

Open AccessArticle

An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

by Dinesh Karunanidy, Rajakumar Ramalingam, Ankur Dumka, Rajesh Singh, Ibrahim Alsukayti, Divya Anand, Habib Hamam and Muhammad Ibrahim

Processes 2021, 9(12), 2171; https://doi.org/10.3390/pr9122171 - 02 Dec 2021

Cited by 7 | Viewed by 2004

Abstract

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA). Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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20 pages, 3223 KiB

Open AccessArticle

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

by Zeinab Shahbazi, Yung-Cheol Byun and Ho-Young Kwak

Processes 2021, 9(9), 1593; https://doi.org/10.3390/pr9091593 - 05 Sep 2021

Cited by 18 | Viewed by 4060

Abstract

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes. Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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15 pages, 1002 KiB

Open AccessArticle

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

by Aroosa Mubeen, Muhammad Ibrahim, Nargis Bibi, Mohammad Baz, Habib Hamam and Omar Cheikhrouhou

Processes 2021, 9(9), 1514; https://doi.org/10.3390/pr9091514 - 26 Aug 2021

Cited by 19 | Viewed by 2872

Abstract

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches. Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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Review

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31 pages, 3400 KiB

Open AccessReview

A Systematic Literature Review on the Automatic Creation of Tactile Graphics for the Blind and Visually Impaired

by Mukhriddin Mukhiddinov and Soon-Young Kim

Processes 2021, 9(10), 1726; https://doi.org/10.3390/pr9101726 - 26 Sep 2021

Cited by 17 | Viewed by 6353

Abstract

Currently, a large amount of information is presented graphically. However, visually impaired individuals do not have access to visual information. Instead, they depend on tactile illustrations—raised lines, textures, and elevated graphics that are felt through touch—to perceive geometric and various other objects in textbooks. Tactile graphics are considered an important factor for students in the science, technology, engineering, and mathematics fields seeking a quality education because teaching materials in these fields are frequently conveyed with diagrams and geometric figures. In this paper, we conducted a systematic literature review to identify the current state of research in the field of automatic tactile graphics generation. Over 250 original research papers were screened and the most appropriate studies on automatic tactile graphic generation over the last six years were classified. The reviewed studies explained numerous current solutions in static and dynamic tactile graphics generation using conventional computer vision and artificial intelligence algorithms, such as refreshable tactile displays for education and machine learning models for tactile graphics classification. However, the price of refreshable tactile displays is still prohibitively expensive for low- and middle-income users, and the lack of training datasets for the machine learning model remains a problem. Full article

(This article belongs to the Special Issue Advances in Lightweight AI for Internet of Things Devices for Smart Cities)

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