Special Issue "IoT Based Intelligent and Green Communications: Modelling, Practice and Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 July 2022 | Viewed by 9656

Special Issue Editors

Dr. Tawfik Al-Hadhrami
E-Mail Website
Guest Editor
School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: Internet of Things (IoT); applications and protocols; mobile networking & applications; 5G/6G; Big Data; IoT industry; wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Faisal Saeed
E-Mail Website
Guest Editor
Information Systems Department, Taibah University, Medina, Saudi Arabia
Interests: data mining; information retrieval and machine learning
Dr. Mukesh Prasad
E-Mail Website
Guest Editor
School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
Interests: machine learning; computational intelligence; image processing; data analytics; big data; natural language processing; brain–computer interface
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yue Cao
E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Wuhan University, Wuhan, China
Interests: transport & energy: e-mobility, autonomous valet parking, smart grids; networking & communication: cloud/edge computing, V2X, ICN, SDN; cyber security & localization: privacy, trust, navigation
Dr. Korhan Cengiz
E-Mail Website
Guest Editor
Department of Electrical-Electronics Engineering, Trakya University, Edirne, Turkey
Interests: 4G; LTE; 5G; network function virtualization (NFV); software defined networking (SDN); indoor positioning systems (IPS); spatial modulation

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) has emerged as a promising technology to facilitate the transition to a digital future and smart cities. A number of challenges and issues associated with IoT have arisen, including intelligent-based system modeling and green communications, smart function modeling, the design of IoT systems, and practical smart applications. It is extremely important to produce and design smart solutions for these issues and challenges, by utilizing the smart devices, new techniques, and new technologies developed as a result of IoT-based intelligent systems and green communications. Due to the recent advances in smart IoT systems and green communication technologies, IoT is shifting towards IoT-based intelligent systems (IoT-BIS). The IoT-BIS systems have contributed to several fields, including IoT-health, AI-IoT, next-generation wireless systems for IoT, and IoT industry and applications.  

We invite you to submit an unpublished original research work related to the theme of “IoT Based Intelligent and Green Communications: Modelling, Practice and Applications” in IoT intelligent system and green communication network environments. 

Dr. Tawfik Al-Hadhrami
Dr. Faisal Saeed
Dr. Mukesh Prasad
Prof. Dr. Yue Cao
Dr. Korhan Cengiz
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. Electronics 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 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

  • IoT-based health monitoring in smart environments
  • Artificial intelligence (AI) and IoT applications
  • IoT-based oil and gas communication systems
  • IoT in Industry 4.0 focusing on reliable and green communication
  • Big data analytics on practical IoT applications
  • Big data in healthcare
  • Machine learning in healthcare
  • Intelligent medical informatics
  • IoT for healthcare
  • Trust path discovery in IoT-based networks
  • Smart environments
  • IoT security
  • QoS communications in IoT ecosystems
  • Intelligent methods for ad hoc and sensor networks
  • Intelligent vehicular networks
  • Security in IoT systems considering scalable system implementation
  • Trusted computing
  • Data and network security and safety
  • Intelligent and green wireless sensor networks
  • Management of IoT devices in smart environments focusing on resource optimization
  • End IoT devices connectivity measurements
  • Genetic communications considering multivalued optimization
  • Blockchain and IoT ecosystems for distributed modelling and practice
  • Protocols and standardization for IoT environment
  • Drone-enabled IoT application and network management frameworks
  • Next-generation wireless systems for IoT such as SWIPT and NOMA
  • Physical layer security for IoT systems

Published Papers (9 papers)

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Research

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Article
Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique
Electronics 2022, 11(1), 103; https://doi.org/10.3390/electronics11010103 - 29 Dec 2021
Cited by 3 | Viewed by 548
Abstract
Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes [...] Read more.
Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%. Full article
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Article
Internet of Drones Intrusion Detection Using Deep Learning
Electronics 2021, 10(21), 2633; https://doi.org/10.3390/electronics10212633 - 28 Oct 2021
Cited by 1 | Viewed by 818
Abstract
Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET [...] Read more.
Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches. Full article
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Article
Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation
Electronics 2021, 10(16), 1916; https://doi.org/10.3390/electronics10161916 - 10 Aug 2021
Viewed by 528
Abstract
Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, [...] Read more.
Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, new forms of a connection must be explored to ensure the system’s availability and reliability as a whole. Unmanned aerial vehicles (UAVs) are becoming increasingly empowered in terms of processing power and autonomy. UAVs can be used as a bridge between IoT devices and remote servers, such as edge or cloud computing. UAVs can collect data from mobile devices and process them, if possible. If there is no processing power in the UAV, the data are sent and processed on servers at the edge or in the cloud. Data offloading throughout UAVs is a reality today, but one with many challenges, mainly due to unavailability constraints. This work proposes stochastic Petri net (SPN) models and reliability block diagrams (RBDs) to evaluate a distributed architecture, with UAVs focusing on the system’s availability and reliability. Among the various existing methodologies, stochastic Petri nets (SPN) provide models that represent complex systems with different characteristics. UAVs are used to route data from IoT devices to the edge or the cloud through a base station. The base station receives data from UAVs and retransmits them to the cloud. The data are processed in the cloud, and the responses are returned to the IoT devices. A sensitivity analysis through Design of Experiments (DoE) showed key points of improvement for the base model, which was enhanced. A numerical analysis indicated the components with the most significant impact on availability. For example, the cloud proved to be a very relevant component for the availability of the architecture. The final results could prove the effectiveness of improving the base model. The present work can help system architects develop distributed architectures with more optimized UAVs and low evaluation costs. Full article
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Article
Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution
Electronics 2021, 10(11), 1314; https://doi.org/10.3390/electronics10111314 - 30 May 2021
Cited by 2 | Viewed by 1096
Abstract
The volume of data generated worldwide is rapidly growing. Cloud computing, fog computing, and the Internet of things (IoT) technologies have been adapted to compute and process this high data volume. In coming years information technology will enable extensive developments in the field [...] Read more.
The volume of data generated worldwide is rapidly growing. Cloud computing, fog computing, and the Internet of things (IoT) technologies have been adapted to compute and process this high data volume. In coming years information technology will enable extensive developments in the field of healthcare and offer health care providers and patients broadened opportunities to enhance their healthcare experiences and services owing to heightened availability and enriched services through real-time data exchange. As promising as these technological innovations are, security issues such as data integrity and data consistency remain widely unaddressed. Therefore, it is important to engineer a solution to these issues. Developing a damage assessment and recovery control model for fog computing is critical. This paper proposes two models for using fog computing in healthcare: one for private fog computing distribution and one for public fog computing distribution. For each model, we propose a unique scheme to assess the damage caused by malicious attack, to accurately identify affected transactions and recover damaged data if needed. A transaction-dependency graph technique is used for both models to observe and monitor all transactions in the whole system. We conducted a simulation study to assess the applicability and efficacy of the proposed models. The evaluation rendered these models practicable and effective. Full article
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Article
An Optimized Stacking Ensemble Model for Phishing Websites Detection
Electronics 2021, 10(11), 1285; https://doi.org/10.3390/electronics10111285 - 28 May 2021
Cited by 2 | Viewed by 1113
Abstract
Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, [...] Read more.
Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively. Full article
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Article
CoLL-IoT: A Collaborative Intruder Detection System for Internet of Things Devices
Electronics 2021, 10(7), 848; https://doi.org/10.3390/electronics10070848 - 02 Apr 2021
Cited by 3 | Viewed by 874
Abstract
The Internet of Things (IoT) and its applications are becoming popular among many users nowadays, as it makes their life easier. Because of its popularity, attacks that target these devices have increased dramatically, which might cause the entire system to be unavailable. Some [...] Read more.
The Internet of Things (IoT) and its applications are becoming popular among many users nowadays, as it makes their life easier. Because of its popularity, attacks that target these devices have increased dramatically, which might cause the entire system to be unavailable. Some of these attacks are denial of service attack, sybil attack, man in the middle attack, and replay attack. Therefore, as the attacks have increased, the detection solutions to detect malware in the IoT have also increased. Most of the current solutions often have very serious limitations, and malware is becoming more apt in taking advantage of them. Therefore, it is important to develop a tool to overcome the existing limitations of current detection systems. This paper presents CoLL-IoT, a CoLLaborative intruder detection system that detects malicious activities in IoT devices. CoLL-IoT consists of the following four main layers: IoT layer, network layer, fog layer, and cloud layer. All of the layers work collaboratively by monitoring and analyzing all of the network traffic generated and received by IoT devices. CoLL-IoT brings the detection system close to the IoT devices by taking the advantage of edge computing and fog computing paradigms. The proposed system was evaluated on the UNSW-NB15 dataset that has more than 175,000 records and achieved an accuracy of up to 98% with low type II error rate of 0.01. The evaluation results showed that CoLL-IoT outperformed the other existing tools, such as Dendron, which was also evaluated on the UNSW-NB15 dataset. Full article
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Review

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Review
IoT as a Backbone of Intelligent Homestead Automation
Electronics 2022, 11(7), 1004; https://doi.org/10.3390/electronics11071004 - 24 Mar 2022
Viewed by 435
Abstract
The concepts of smart agriculture, with the aim of highly automated industrial mass production leaning towards self-farming, can be scaled down to the level of small farms and homesteads, with the use of more affordable electronic components and open-source software. The backbone of [...] Read more.
The concepts of smart agriculture, with the aim of highly automated industrial mass production leaning towards self-farming, can be scaled down to the level of small farms and homesteads, with the use of more affordable electronic components and open-source software. The backbone of smart agriculture, in both cases, is the Internet of Things (IoT). Single-board computers (SBCs) such as a Raspberry Pi, working under Linux or Windows IoT operating systems, make affordable platform for smart devices with modular architecture, suitable for automation of various tasks by using machine learning (ML), artificial intelligence (AI) and computer vision (CV). Similarly, the Arduino microcontroller enables the building of nodes in the IoT network, capable of reading various physical values, wirelessly sending them to other computers for processing and furthermore, controlling electronic elements and machines in the physical world based on the received data. This review gives a limited overview of currently available technologies for smart automation of industrial agricultural production and of alternative, smaller-scale projects applicable in homesteads, based on Arduino and Raspberry Pi hardware, as well as a draft proposal of an integrated homestead automation system based on the IoT. Full article
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Review
Masked Face Recognition Using Deep Learning: A Review
Electronics 2021, 10(21), 2666; https://doi.org/10.3390/electronics10212666 - 31 Oct 2021
Cited by 2 | Viewed by 1163
Abstract
A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance [...] Read more.
A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR. Full article
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Review
A Survey of the Tactile Internet: Design Issues and Challenges, Applications, and Future Directions
Electronics 2021, 10(17), 2171; https://doi.org/10.3390/electronics10172171 - 06 Sep 2021
Cited by 4 | Viewed by 936
Abstract
The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next [...] Read more.
The Tactile Internet (TI) is an emerging area of research involving 5G and beyond (B5G) communications to enable real-time interaction of haptic data over the Internet between tactile ends, with audio-visual data as feedback. This emerging TI technology is viewed as the next evolutionary step for the Internet of Things (IoT) and is expected to bring about a massive change in Healthcare 4.0, Industry 4.0 and autonomous vehicles to resolve complicated issues in modern society. This vision of TI makes a dream into a reality. This article aims to provide a comprehensive survey of TI, focussing on design architecture, key application areas, potential enabling technologies, current issues, and challenges to realise it. To illustrate the novelty of our work, we present a brainstorming mind-map of all the topics discussed in this article. We emphasise the design aspects of the TI and discuss the three main sections of the TI, i.e., master, network, and slave sections, with a focus on the proposed application-centric design architecture. With the help of the proposed illustrative diagrams of use cases, we discuss and tabulate the possible applications of the TI with a 5G framework and its requirements. Then, we extensively address the currently identified issues and challenges with promising potential enablers of the TI. Moreover, a comprehensive review focussing on related articles on enabling technologies is explored, including Fifth Generation (5G), Software-Defined Networking (SDN), Network Function Virtualisation (NFV), Cloud/Edge/Fog Computing, Multiple Access, and Network Coding. Finally, we conclude the survey with several research issues that are open for further investigation. Thus, the survey provides insights into the TI that can help network researchers and engineers to contribute further towards developing the next-generation Internet. Full article
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