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Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 October 2020) | Viewed by 60545

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Prof. Dr. Yousaf Bin Zikria

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Korea
Interests: IoT, 5G, wireless communications and networks, opportunistic communications, wireless sensor networks, routing protocols, cognitive radio ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Dr. Rashid Ali

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Guest Editor

School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gunja-dong, Gwangjin-gu, Seoul, Republic of Korea
Interests: reinforcement learning; federated reinforcement learning; wireless networks; network performance analysis; IP routing; IoTs; tactile internet; 5G; URLLC
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Muhammad Khalil Afzal

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Guest Editor

COMSATS Institute of Information Technology, GT Road, Wah Cantt, Pakistan
Interests: wireless sensor networks; ad hoc networks; Smart Cities; 5G; IoT
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sung Won Kim

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
Interests: resource management; wireless networks; mobile networks; performance evaluation; embedded systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is flourishing and facilitating the livelihood of the people. Recent advancements in technologies are making it possible to adopt this technology around the globe. However, the user's quality of experience (QoE) and applications’ quality of service (QoS) requirements are increasing drastically. To cop with these phenomena, practitioners and researchers continuously need to innovate and develop new techniques. IoT applications are incredibly diverse, and include real-time multimedia, smart health, smart city, smart agriculture, smart home, and industrial IoT. Satisfying application requirements, along with the security, is a crucial task. Health-related applications require reliable and timely delivery of information. Living in an epidemic makes fast data analysis and prediction techniques to timely diffuse the situations critical. Spectrum scarcity is another major concern, and sharing among different users operating at different frequencies is the way forward. Employing machine learning-based solutions to make technologies robust and selfadaptive is a new norm.

This Special Issue aims to bring together academia and industrial researchers to explore the opportunities for next-generation IoT, study its impact on the solution of the challenges mentioned above, and propose viable solutions. We solicit papers covering various topics of interest that include but not limited to the following:

Next-generation IoT-based smart health;
Next-generation IoT-based smart cities;
Next-generation IoT-based smart agriculture;
Next-generation IoT-based data analytics;
Next-generation IoT-based industrial IoT;
Next-generation IoT-based multimedia;
Next-generation IoT-based spectrum sharing techniques;
Next-generation IoT-based security and privacy techniques;
Next-generation IoT-based cross-layer protocols.

Prof. Dr. Yousaf Bin Zikria
Prof. Dr. Rashid Ali
Prof. Dr. Muhammad Khalil Afzal
Prof. Dr. Sung Won Kim
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. Sensors 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 2600 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.

Published Papers (8 papers)

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Editorial

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7 pages, 350 KiB

Open AccessEditorial

Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions

by Yousaf Bin Zikria, Rashid Ali, Muhammad Khalil Afzal and Sung Won Kim

Sensors 2021, 21(4), 1174; https://doi.org/10.3390/s21041174 - 07 Feb 2021

Cited by 74 | Viewed by 11639

Abstract

It is predicted that by 2025, all devices will be connected to the Internet, subsequently causing the number of devices connected with the Internet to rise [...] Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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Research

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15 pages, 1641 KiB

Open AccessArticle

Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning

by Motahareh Mobasheri, Yangwoo Kim and Woongsup Kim

Sensors 2020, 20(23), 6942; https://doi.org/10.3390/s20236942 - 04 Dec 2020

Cited by 3 | Viewed by 1560

Abstract

The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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24 pages, 4726 KiB

Open AccessArticle

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

by Luís Fabrício de Freitas Souza, Iágson Carlos Lima Silva, Adriell Gomes Marques, Francisco Hércules dos S. Silva, Virgínia Xavier Nunes, Mohammad Mehedi Hassan, Victor Hugo C. de Albuquerque and Pedro P. Rebouças Filho

Sensors 2020, 20(23), 6711; https://doi.org/10.3390/s20236711 - 24 Nov 2020

Cited by 16 | Viewed by 6823

Abstract

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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21 pages, 4028 KiB

Open AccessArticle

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

by Syed Rizwan Hassan, Ishtiaq Ahmad, Shafiq Ahmad, Abdullah Alfaify and Muhammad Shafiq

Sensors 2020, 20(22), 6574; https://doi.org/10.3390/s20226574 - 18 Nov 2020

Cited by 45 | Viewed by 4963

Abstract

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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21 pages, 8471 KiB

Open AccessArticle

A Compact and Flexible UHF RFID Tag Antenna for Massive IoT Devices in 5G System

by Muhammad Hussain, Yasar Amin and Kyung-Geun Lee

Sensors 2020, 20(19), 5713; https://doi.org/10.3390/s20195713 - 08 Oct 2020

Cited by 21 | Viewed by 6950

Abstract

Upcoming 5th-generation (5G) systems incorporate physical objects (referred to as things), which sense the presence of components such as gears, gadgets, and sensors. They may transmit many kinds of states in the smart city context, such as new deals at malls, safe distances on roads, patient heart rhythms (especially in hospitals), and logistic control at aerodromes and seaports around the world. These serve to form the so-called future internet of things (IoT). From this futuristic perspective, everything should have its own identity. In this context, radio frequency identification (RFID) plays a specific role, which provides wireless communications in a secure manner. Passive RFID tags carry out work using the energy harvested among massive systems. RFID has been habitually realized as a prerequisite for IoT, the combination of which is called IoT RFID (I-RFID). For the current scenario, such tags should be productive, low-profile, compact, easily mountable, and have eco-friendly features. The presently available tags are not cost-effective and have not been proven as green tags for environmentally friendly IoT in 5G systems nor are they suitable for long-range communications in 5G systems. The proposed I-RFID tag uses the meandering angle technique (MAT) to construct a design that satisfies the features of a lower-cost printed antenna over the worldwide UHF RFID band standard (860–960 MHz). In our research, tag MAT antennas are fabricated on paper-based Korsnäs by screen- and flexo-printing, which have lowest simulated effective outcomes with dielectric variation due to humidity and have a plausible read range (RR) for European (EU; 866–868 MHz) and North American (NA; 902–928 MHz) UHF band standards. The I-RFID tag size is reduced by 36% to 38% w.r.t. a previously published case, the tag gain has been improved by 23.6% to 33.12%, and its read range has been enhanced by 50.9% and 59.6% for EU and NA UHF bands, respectively. It provides impressive performance on some platforms (e.g., plastic, paper, and glass), thereby providing a new state-of-the-art I-RFID tag with better qualities in 5G systems. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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20 pages, 5355 KiB

Open AccessArticle

Fault-Tolerant Network-On-Chip Router Architecture Design for Heterogeneous Computing Systems in the Context of Internet of Things

by Muhammad Rashid, Naveed Khan Baloch, Muhammad Akmal Shafique, Fawad Hussain, Shahroon Saleem, Yousaf Bin Zikria and Heejung Yu

Sensors 2020, 20(18), 5355; https://doi.org/10.3390/s20185355 - 18 Sep 2020

Cited by 8 | Viewed by 3359

Abstract

Network-on-chip (NoC) architectures have become a popular communication platform for heterogeneous computing systems owing to their scalability and high performance. Aggressive technology scaling makes these architectures prone to both permanent and transient faults. This study focuses on the tolerance of a NoC router to permanent faults. A permanent fault in a NoC router severely impacts the performance of the entire network. Thus, it is necessary to incorporate component-level protection techniques in a router. In the proposed scheme, the input port utilizes a bypass path, virtual channel (VC) queuing, and VC closing strategies. Moreover, the routing computation stage utilizes spatial redundancy and double routing strategies, and the VC allocation stage utilizes spatial redundancy. The switch allocation stage utilizes run-time arbiter selection. The crossbar stage utilizes a triple bypass bus. The proposed router is highly fault-tolerant compared with the existing state-of-the-art fault-tolerant routers. The reliability of the proposed router is 7.98 times higher than that of the unprotected baseline router in terms of the mean-time-to-failure metric. The silicon protection factor metric is used to calculate the protection ability of the proposed router. Consequently, it is confirmed that the proposed router has a greater protection ability than the conventional fault-tolerant routers. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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22 pages, 2920 KiB

Open AccessArticle

TrustWalker: An Efficient Trust Assessment in Vehicular Internet of Things (VIoT) with Security Consideration

by Muhammad Sohail, Rashid Ali, Muhammad Kashif, Sher Ali, Sumet Mehta, Yousaf Bin Zikria and Heejung Yu

Sensors 2020, 20(14), 3945; https://doi.org/10.3390/s20143945 - 16 Jul 2020

Cited by 13 | Viewed by 3550

Abstract

The Internet of Things (IoT) is a world of connected networks and modern technology devices, among them vehicular networks considered more challenging due to high speed and network dynamics. Future trends in IoT allow these inter networks to share information. Also, the previous security solutions to vehicular IoT (VIoT) much emphasize on privacy protection and security related issues using public keys infrastructure. However, the primary concern about efficient trust assessment, authorized users malfunctioning, and secure information dissemination in vehicular wireless networks have not been explored. To cope with these challenges, we propose a trust enhanced on-demand routing (TER) scheme, which adopts TrustWalker (TW) algorithm for efficient trust assessment and route search technique in VIoT. TER comprised of novel three-valued subjective logic (3VSL), TW algorithm, and ad hoc on-demand distance vector (AODV) routing protocol. The simulated results validate the accuracy of the proposed scheme in term of throughput, packet drop ratio (PDR), and end to end (E2E) delay. In the simulation, the execution time of the TW algorithm is analyzed and compared with another route search algorithm, i.e., Assess-Trust (AT), by considering real-world online datasets such as Pretty Good Privacy and Advogato. The accuracy and efficiency of the TW algorithm, even with a large number of vehicle users, are also demonstrated through simulations. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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Review

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22 pages, 1035 KiB

Open AccessEditor’s ChoiceReview

Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0

by Tahera Kalsoom, Naeem Ramzan, Shehzad Ahmed and Masood Ur-Rehman

Sensors 2020, 20(23), 6783; https://doi.org/10.3390/s20236783 - 27 Nov 2020

Cited by 123 | Viewed by 19239

Abstract

The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories. Full article

(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)

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