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Special Issue "Role of AI, Big Data, and Blockchain in IoT Devices"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4367

Special Issue Editors

Dr. Sukhpal Singh Gill
E-Mail Website
Guest Editor
Affiliation: School of Electronic Engineering & Computer Science, Queen Mary University of London, London, UK
Interests: cloud computing; fog computing; software engineering; Internet of things and healthcare
Special Issues, Collections and Topics in MDPI journals
Prof. Priyanka Chawla
E-Mail Website
Guest Editor
School of CSE,Lovely Professional University, Punjab 144411, India
Interests: Big Data analytics; Artificial Intelligence; software engineering; blockchain; Internet of Things; fog computing

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) refers to a system of connected things/devices through a network that have the ability to transfer data without human intervention. IoT devices are increasing day by day, and it is estimated that they may reach 80 billion by 2025. This huge amount of data needs to be handled smartly to deduce the meaning of the data and employ it for decision making to promote growth in business. Big Data analytics and IoT need to go hand in hand. The data generated by IoT devices also need to be protected by providing security mechanisms to avoid their misuse. Data generated by IoT devices are highly susceptible to security hazards due to the complex and diverse nature of devices used to accomplish the task. IoT devices are vulnerable to attacks and, once compromised, can cause damage at higher levels because antivirus software cannot be installed on devices to protect them from malicious activities due to the low-memory and low-power nature of the devices. Blockchain technology is a distributed ledger that allows for the secure transfer of data between parties and can be effectively applied to ensure the security of devices. Artificial Intelligence refers to a set of tools that can mimic the intelligence of human beings when applied to the area of healthcare, retail, manufacturing, and banking. Machines can be enabled to learn from the data generated by IoT devices by building a machine learning model with the help of Artificial Intelligence algorithms. This provides the capability to classify patterns and uncover anomalies in the data generated by the devices and associated sensors.

In this Special Issue, we are open to contributions (research papers and review papers) exploring the role of AI, Big Data, and blockchain in IoT. This includes frameworks and architectures of IoT after merging of AI, Big Data and blockchain, emerging applications of AI, Big Data and blockchain in IoT, and implementation of security in IoT using blockchain technology. Contributions describing the applications of Big Data in alleviating natural disasters, pandemic diseases, ensuring road safety, and mitigating environmental pollution are also welcome.

Dr. Sukhpal Singh Gill
Prof. Priyanka Chawla
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. Sustainability 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

  • implementation of security in the Internet of Things
  • blockchain application
  • Artificial Intelligence
  • Big Data analytics
  • machine learning algorithms
  • deep learning algorithms
  • application of deep learning in IoT devices

Published Papers (2 papers)

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Research

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Article
Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)
Sustainability 2020, 12(24), 10627; https://doi.org/10.3390/su122410627 - 19 Dec 2020
Cited by 6 | Viewed by 1078
Abstract
Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization [...] Read more.
Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree. Full article
(This article belongs to the Special Issue Role of AI, Big Data, and Blockchain in IoT Devices)
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Review

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Review
Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges
Sustainability 2020, 12(12), 5161; https://doi.org/10.3390/su12125161 - 24 Jun 2020
Cited by 27 | Viewed by 2581
Abstract
Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated [...] Read more.
Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated with industrial Internet-of-things (IIoT) networks to help realize the fourth industrial revolution, Industry 4.0. Though blockchain-enabled IIoT networks may have the potential to support the services and demands of next-generation networks, the gap analysis presented in this work highlights some of the areas that need improvement. Based on these observations, the article then promotes the utility of reinforcement learning (RL) techniques to address some of the major issues of blockchain-enabled IIoT networks such as block time minimization and transaction throughput enhancement. This is followed by a comprehensive case study where a Q-learning technique is used for minimizing the occurrence of forking events by reducing the transmission delays for a miner. Extensive simulations have been performed and the results have been obtained for the average transmission delay which relates to the forking events. The obtained results demonstrate that the Q-learning approach outperforms the greedy policy while having a reasonable level of complexity. To further develop the blockchain-enabled IIoT networks, some future research directions are also documented. While this article highlights the applications of RL techniques in blockchain-enabled IIoT networks, the provided insights and results could pave the way for rapid adoption of blockchain technology. Full article
(This article belongs to the Special Issue Role of AI, Big Data, and Blockchain in IoT Devices)
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