Advances in Communication Systems, IoT and Blockchain

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7294

Special Issue Editors


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Guest Editor
Department of Computer Science and Digital Technology, University of East London, University Way, London E16 2RD, UK
Interests: blockchain; federated machine learning; Internet of Things; e-healthcare
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computational Modeling and Simulation Engineering, Old Dominion University, Suffolk, VA 23435, USA
Interests: edge computing; machine learning; internet of things; smart grid

Special Issue Information

Dear Colleagues,

Today, blockchain technology (BCT), internet of things (IoT), and artificial intelligence (AI) are recognized as innovations that have the potential to improve current business processes, create new business models, and disrupt whole industries. These technologies are independently impacting the research world significantly. The integration of these technologies can add a new dimension to Industry 4.0.

IoT devices in smart systems, such as smart homes, industries, cars, grids, healthcare, etc., typically generate massive amounts of data. These data are often stored and processed in a centralized server, where the data format is not standardized. Blockchain technology could help with data standardization by setting up a harmonized digital platform for IoT data accessible to multiple parties. Due to the use of hash functions, data on blockchain systems is typically stored in one data format. Consequently, data management could be optimized by increased interoperability of stored data. Today, it is one of the IoT's main limitations in storing and managing extensive data and related communication. Converging technologies such as BCT and/or AI/ML with IoT systems could make data management more scalable, secure, and efficient. Blockchain technology can improve the data management of IoT devices due to its transparency, trust, truthfulness, immutability, security, and privacy features. In addition, AI offers an intelligent learning mechanism from the massive amount of time-series real-life data. Combined with AI and/or Blockchain, any smart system can address the many existing challenges such as interoperability, scalability, automation, satisfactory future prediction, etc.

This Special Issue is dedicated to novel articles covering BCT, AI, and ML methodologies in smart systems. Topics of interest include but are not strictly limited to the following:

  • Blockchain and AI for IoT.
  • Blockchain technology for IoT system’s secure management.
  • IoT applications based on blockchain technology.
  • Evaluation and experimental analysis of blockchain IoT applications.
  • Blockchain, security, privacy and AI for IoT healthcare systems.
  • AI-based technologies for security and privacy of future IoT.
  • Smart sensing system for Industry 4.0.
  • Smart industrial IoT.
  • AI-based smart system automation.
  • Machine learning and artificial intelligence for the agriculture industry.
  • Machine learning and artificial intelligence for smart homes.
  • Medical data acquisition, cleaning and integration using AI methodologies.
  • Automated/computer-aided diagnosis using artificial intelligence or knowledge engineering.
  • Smart grid system using Blockchain and 5G.
  • Scalable blockchain consensus algorithm for big data.
  • Privacy and security in healthcare using blockchain.
  • Mathematical proof of concept for the blockchain concept.

Dr. Sujit Biswas
Dr. Md. Shirajum Munir
Guest Editors

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Keywords

  • blockchain
  • IoT
  • machine learning
  • healthcare systems
  • medical data

Published Papers (5 papers)

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Research

16 pages, 1935 KiB  
Article
An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT
by Zhaocheng Wang, Xueying Liu, Xinming Shao, Abdullah Alghamdi, Mesfer Alrizq, Md. Shirajum Munir and Sujit Biswas
Mathematics 2023, 11(23), 4844; https://doi.org/10.3390/math11234844 - 01 Dec 2023
Cited by 1 | Viewed by 852
Abstract
Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) [...] Read more.
Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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15 pages, 337 KiB  
Article
Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks
by Ahmed S. Alfakeeh and Muhammad Awais Javed
Mathematics 2023, 11(17), 3798; https://doi.org/10.3390/math11173798 - 04 Sep 2023
Cited by 1 | Viewed by 781
Abstract
Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such [...] Read more.
Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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17 pages, 1426 KiB  
Article
Efficient Index Modulation-Based MIMO OFDM Data Transmission and Detection for V2V Highly Dispersive Channels
by J. Alberto Del Puerto-Flores, Francisco R. Castillo-Soria, Carlos A. Gutiérrez and Fernando Peña-Campos
Mathematics 2023, 11(12), 2773; https://doi.org/10.3390/math11122773 - 20 Jun 2023
Cited by 2 | Viewed by 1332
Abstract
Vehicle-to-vehicle (V2V) communication networks are based on vehicles that wirelessly exchange data, traffic congestion, and safety warnings between them. The design of new V2V systems requires increasingly energetically and spectrally efficient systems. Conventional multiple-input–multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have been [...] Read more.
Vehicle-to-vehicle (V2V) communication networks are based on vehicles that wirelessly exchange data, traffic congestion, and safety warnings between them. The design of new V2V systems requires increasingly energetically and spectrally efficient systems. Conventional multiple-input–multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have been used successfully for the last decade. However, MIMO-OFDM systems need to be improved to face future communication networks in high-mobility environments. This article proposes an efficient index modulation (IM)-based MIMO-OFDM system for V2V channels. The proposed transmission system is evaluated in high Doppler-spread channels. The results demonstrate that the proposed scheme reduces the required computational complexity in data detection and exhibits gains of up to 3 dB in bit error rate (BER) performance when compared to the conventional MIMO-OFDM system under the same conditions and parameters, along with achieving superior spectral efficiency. The results show the viability of implementing the proposed system in practical applications for high-transmission-rate V2V channels. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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24 pages, 10714 KiB  
Article
Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI
by Promila Ghosh, Amit Kumar Mondal, Sajib Chatterjee, Mehedi Masud, Hossam Meshref and Anupam Kumar Bairagi
Mathematics 2023, 11(10), 2241; https://doi.org/10.3390/math11102241 - 10 May 2023
Cited by 9 | Viewed by 2174
Abstract
Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional [...] Read more.
Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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16 pages, 4818 KiB  
Article
Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment
by Iyad Katib and Mahmoud Ragab
Mathematics 2023, 11(8), 1887; https://doi.org/10.3390/math11081887 - 16 Apr 2023
Cited by 10 | Viewed by 1202
Abstract
The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and [...] Read more.
The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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