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Electronics
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Enabling Technologies for Internet of Things
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Enabling Technologies for Internet of Things

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 22561

Special Issue Editor

Prof. Dr. Muhammad Zeeshan Shakir

E-Mail Website
Guest Editor
School of Computing Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Interests: wireless communications; 5G/6G networks; AI/ML; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The next generation of communications networks such as Internet of Things, cellular networks and vehicular ad hoc networks, are considered as complex systems due to the ever-increasing number of technologies and connected devices. Large scale deployment of these complex systems requires enabling 5G and beyond technologies to facilitate autonomous operations by providing superior communication services and hyper-connectivity for urban and rural applications equally.

In this Special Issue, we are particularly interested in 5G and beyond enabled technologies for large scale complex IoT networks and emerging vertical networks such as transportation, health, smart grid, and smart city, connecting the unconnected systems, supporting the centralized or distributed intelligent decision making in these complex systems, defining new applications and solutions with an IoT prototype and benchmarking the performance with reference to system and consumer requirements. 

At the time of organisation of this special issue, the countries around the world have been affected by Covid-19 pandemic. Role of wireless communication technologies has become more important than ever before to respond to the pandemic and facilitate the economies, businesses and citizens. We welcome contributions on 5G technologies and their use case, applications and services to fight with pandemic situation.

Dr. Muhammad Zeeshan Shakir
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. 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 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

  • 5G/6G networks
  • Advanced data analytics
  • Sensor technologies
  • AI/ML for automation in IoT
  • Edge and fog computing
  • Cloud computing
  • Blockchain technology
  • UAVs for sensing and communications

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Published Papers (5 papers)

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Research

12 pages, 1370 KiB  
Article
LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring
by Yingbo Zhu, Shahriar Abdullah Al-Ahmed, Muhammad Zeeshan Shakir and Joanna Isabelle Olszewska
Electronics 2023, 12(1), 107; https://doi.org/10.3390/electronics12010107 - 27 Dec 2022
Cited by 21 | Viewed by 3382
Abstract
Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be [...] Read more.
Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%. Full article
(This article belongs to the Special Issue Enabling Technologies for Internet of Things)
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21 pages, 596 KiB  
Article
A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
by Ayesha Ayub, Sobia Jangsher, M. Majid Butt, Abdur Rahman Maud and Farrukh A. Bhatti
Electronics 2021, 10(12), 1493; https://doi.org/10.3390/electronics10121493 - 21 Jun 2021
Cited by 4 | Viewed by 2258
Abstract
Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G [...] Read more.
Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area. Full article
(This article belongs to the Special Issue Enabling Technologies for Internet of Things)
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24 pages, 848 KiB  
Article
Artificial Neural Network (ANN) Enabled Internet of Things (IoT) Architecture for Music Therapy
by Shama Siddiqui, Rory Nesbitt, Muhammad Zeeshan Shakir, Anwar Ahmed Khan, Ausaf Ahmed Khan, Karima Karam Khan and Naeem Ramzan
Electronics 2020, 9(12), 2019; https://doi.org/10.3390/electronics9122019 - 29 Nov 2020
Cited by 12 | Viewed by 5627
Abstract
Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child [...] Read more.
Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music. Full article
(This article belongs to the Special Issue Enabling Technologies for Internet of Things)
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20 pages, 923 KiB  
Article
Distributed Fog Computing for Internet of Things (IoT) Based Ambient Data Processing and Analysis
by Mehreen Ahmed, Rafia Mumtaz, Syed Mohammad Hassan Zaidi, Maryam Hafeez, Syed Ali Raza Zaidi and Muneer Ahmad
Electronics 2020, 9(11), 1756; https://doi.org/10.3390/electronics9111756 - 22 Oct 2020
Cited by 25 | Viewed by 3878
Abstract
Urban centers across the globe are under immense environmental distress due to an increase in air pollution, industrialization, and elevated living standards. The unmanageable and mushroom growth of industries and an exponential soar in population has made the ascent of air pollution intractable. [...] Read more.
Urban centers across the globe are under immense environmental distress due to an increase in air pollution, industrialization, and elevated living standards. The unmanageable and mushroom growth of industries and an exponential soar in population has made the ascent of air pollution intractable. To this end, the solutions that are based on the latest technologies, such as the Internet of things (IoT) and Artificial Intelligence (AI) are becoming increasingly popular and they have capabilities to monitor the extent and scale of air contaminants and would be subsequently useful for containing them. With centralized cloud-based IoT platforms, the ubiquitous and continuous monitoring of air quality and data processing can be facilitated for the identification of air pollution hot spots. However, owing to the inherent characteristics of cloud, such as large end-to-end delay and bandwidth constraint, handling the high velocity and large volume of data that are generated by distributed IoT sensors would not be feasible in the longer run. To address these issues, fog computing is a powerful paradigm, where the data are processed and filtered near the end of the IoT nodes and it is useful for improving the quality of service (QoS) of IoT network. To further improve the QoS, a conceptual model of distributed fog computing and a machine learning based data processing and analysis model is proposed for the optimal utilization of cloud resources. The proposed model provides a classification accuracy of 99% while using a Support Vector Machines (SVM) classifier. This model is also simulated in iFogSim toolkit. It affords many advantages, such as reduced load on the central server by locally processing the data and reporting the quality of air. Additionally, it would offer the scalability of the system by integrating more air quality monitoring nodes in the IoT network. Full article
(This article belongs to the Special Issue Enabling Technologies for Internet of Things)
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18 pages, 997 KiB  
Article
DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking
by Tuan Anh Tang, Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, Mounir Ghogho and Fadi El Moussa
Electronics 2020, 9(9), 1533; https://doi.org/10.3390/electronics9091533 - 19 Sep 2020
Cited by 60 | Viewed by 6659
Abstract
Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN [...] Read more.
Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach. Full article
(This article belongs to the Special Issue Enabling Technologies for Internet of Things)
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