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Sustainable Computing Based on Internet of Things Empowered with Artificial Intelligence and Blockchain

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

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 32899

Special Issue Editors


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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: disease recognition using artificial intelligence methods; digital health; multimodal interfaces; biomedical imaging
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Science and Engineering, Hamad Bin Khalifa University, Doha 5825, Qatar
Interests: network analysis using social networking; mobile computing; web services; 4G communication; cloud computing; information security through anomaly detection
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, University of BRADFORD, Bradford BD7 1DP, UK
Interests: evolutionary computing; swarm intelligence; neural networks; image processing; computer vision; and machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
NUST School of Electrical Engineering and Computer Science (SEECS), Islamabad H-12, Pakistan
Interests: Distributed systems; Web of Things and Vehicular Ad Hoc Networks; and Data and Social Engineering for Smart Cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) applications have permeated a broad range of aspects of human life as a result of advances in semiconductor technology. Many new technologies also facilitate this general use, including artificial intelligence (AI) and blockchain. The rapid advancement of AI technologies, such as deep learning, represents an exciting opportunity to extract reliable information from large amounts of raw sensor data in IoT applications. Blockchain is gaining traction in the development of IoT applications to address security and privacy concerns due to its immutability and distributed nature. AI and blockchain have developed into game-changing technologies for accelerating the development of IoT ecosystems with tremendous growth, impact, and promise. Sustainable computing, which provides an environment conducive to energy conservation, is critical for energy-constrained IoT devices. However, AI and blockchain were not designed for this kind of IoT environment. Both methods are computationally intensive and can create significant bandwidth overhead and latency. These stringent performance and power requirements are incompatible with the majority of IoT devices. Although new computing paradigms such as edge computing have been developed to offload computation-intensive activities off low-power IoT devices, many operations must still be performed on IoT devices for security and privacy reasons (i.e., keep data locally). Recent years have seen an increased interest in research on novel computer architectures, lightweight deep learning, and blockchain technologies. The use of lightweight deep learning models with blockchain-based architectures can ensure the long-term viability of the IoT. This Special Issue will include the latest advances and research findings on sustainable computing for upcoming IoT applications driven by AI and blockchain. It intends to offer a forum for academics and practitioners from all over the world to develop innovative solutions to current problems. The topics of interest include, but are not limited to: 

  • Lightweight deep learning models with blockchain-based architectures for IoT.
  • Fusion of artificial intelligence and blockchain for sustainable IoT.
  • Intelligence for sustainable computing in emerging IoT applications.
  • Blockchain-based AI models for sustainable computing in emerging IoT applications.
  • Sustainable IoT networks supported by AI: privacy and accountability.
  • New computing architectures for sustainable IoT systems.
  • Theoretical considerations for AI and blockchain-enabled sustainable IoT designs.
  • Energy-efficient communication protocols for IoT systems driven by AI and blockchain.
  • Security and privacy issues in sustainable computing for IoT applications.
  • Energy-efficient communication protocols for IoT systems driven by AI and blockchain.
  • Case studies in sustainable computing for IoT applications powered by AI and blockchain.

Prof. Dr. Robertas Damaševičius
Dr. M. Poongodi
Dr. Hafiz Tayyab Rauf
Prof. Dr. Hasan Ali Khattak
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.

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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.

Keywords

  • Internet of Things
  • deep learning
  • artificial intelligence
  • blockchain
  • energy-efficient design
  • sustainable computing.

Published Papers (10 papers)

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Research

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23 pages, 5911 KiB  
Article
MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors
by Roseline Oluwaseun Ogundokun, Sanjay Misra, Akinyemi Omololu Akinrotimi and Hasan Ogul
Sensors 2023, 23(2), 656; https://doi.org/10.3390/s23020656 - 06 Jan 2023
Cited by 12 | Viewed by 2968
Abstract
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. [...] Read more.
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time. Full article
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18 pages, 1526 KiB  
Article
User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe
by Vera Pospelova, Inés López-Baldominos, Luis Fernández-Sanz, Ana Castillo-Martínez and Sanjay Misra
Sensors 2023, 23(1), 529; https://doi.org/10.3390/s23010529 - 03 Jan 2023
Cited by 2 | Viewed by 2498
Abstract
The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of [...] Read more.
The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice. Full article
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16 pages, 6261 KiB  
Article
Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features
by K. Suresh Manic, Venkatesan Rajinikanth, Ali Saud Al-Bimani, David Taniar and Seifedine Kadry
Sensors 2023, 23(1), 280; https://doi.org/10.3390/s23010280 - 27 Dec 2022
Cited by 2 | Viewed by 1531
Abstract
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening [...] Read more.
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices. Full article
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13 pages, 1894 KiB  
Article
Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
by Tanzila Saba, Amjad Rehman, Khalid Haseeb, Saeed Ali Bahaj and Robertas Damaševičius
Sensors 2022, 22(20), 7876; https://doi.org/10.3390/s22207876 - 17 Oct 2022
Cited by 5 | Viewed by 1499
Abstract
The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) [...] Read more.
The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions. Full article
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20 pages, 3217 KiB  
Article
SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks
by Sagheer Ahmed Jan, Noor Ul Amin, Junaid Shuja, Assad Abbas, Mohammed Maray and Mazhar Ali
Sensors 2022, 22(11), 4019; https://doi.org/10.3390/s22114019 - 26 May 2022
Cited by 8 | Viewed by 2034
Abstract
In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication [...] Read more.
In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model. Full article
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16 pages, 10682 KiB  
Article
EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm
by Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari, João P. Papa, Mohammed Azmi Al-Betar, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Seifedine Kadry, Orawit Thinnukool and Pattaraporn Khuwuthyakorn
Sensors 2022, 22(6), 2092; https://doi.org/10.3390/s22062092 - 08 Mar 2022
Cited by 12 | Viewed by 2554
Abstract
The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the [...] Read more.
The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and β-Hill Climbing optimizer called FPAβ-hc. The performance of the FPAβ-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPAβ-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods. Full article
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14 pages, 1539 KiB  
Article
An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks
by Shahzad Latif, Suhail Akraam, Tehmina Karamat, Muhammad Attique Khan, Chadi Altrjman, Senghour Mey and Yunyoung Nam
Sensors 2022, 22(2), 451; https://doi.org/10.3390/s22020451 - 07 Jan 2022
Cited by 8 | Viewed by 2097
Abstract
The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within [...] Read more.
The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results. Full article
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20 pages, 1858 KiB  
Article
Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
by Abdulaziz Fatani, Abdelghani Dahou, Mohammed A. A. Al-qaness, Songfeng Lu and Mohamed Abd Elaziz
Sensors 2022, 22(1), 140; https://doi.org/10.3390/s22010140 - 26 Dec 2021
Cited by 87 | Viewed by 6691
Abstract
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity [...] Read more.
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators. Full article
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17 pages, 2608 KiB  
Article
A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence
by Irfan Azhar, Muhammad Sharif, Mudassar Raza, Muhammad Attique Khan and Hwan-Seung Yong
Sensors 2021, 21(24), 8178; https://doi.org/10.3390/s21248178 - 08 Dec 2021
Cited by 7 | Viewed by 2908
Abstract
The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and [...] Read more.
The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain. Full article
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Review

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38 pages, 5964 KiB  
Review
Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
by Nancy A Angel, Dakshanamoorthy Ravindran, P M Durai Raj Vincent, Kathiravan Srinivasan and Yuh-Chung Hu
Sensors 2022, 22(1), 196; https://doi.org/10.3390/s22010196 - 28 Dec 2021
Cited by 40 | Viewed by 5589
Abstract
Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical [...] Read more.
Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects. Full article
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