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Future Internet
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Distributed Systems and Artificial Intelligence
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Distributed Systems and Artificial Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 23871

Special Issue Editors

Dr. Nicolae Goga

E-Mail Website1 Website2
Guest Editor
1. Department of Engineering in Foreign Language, Politehnica University of Bucharest, Bucharest, Romania
2. Molecular Dynamics Group, University of Groningen, Groningen, The Netherlands
Interests: distributed systems; parallel computing; molecular dynamics; medical informatics; software engineering
Dr. Dan Garlasu

E-Mail Website
Guest Editor
Oracle Coorporation, Romania
Interests: big data; parallel computing; distributed architectures; databases; e-learning

Special Issue Information

Dear Colleagues,

This Special Issue of MDPI’s Future Internet journal, entitled “Distributed Systems and Artificial Intelligence”, is devoted to topics related to recent trends and progress made in the field of distributed systems and artificial intelligence. However, this Special Issue is not limited to papers that consider only these two fields and their connection. Original and innovative contributions that include big data, security, computer modeling, data science, and distributed e-learning experiments are also invited.

For the past few years, research and development at the levels of academia, industry, business, and government have been devoted to the development of distributed systems and artificial intelligence, which are at the core of many things integrated in our daily life, such as the Internet, Facebook, Amazon, and mobile applications, to name just a few. Here, we aim to emphasize the connection between the two fields of distributed systems and artificial intelligence. However, papers belonging to each separate domain are also welcome. The topics that will be considered in this Special Issue include but are not limited to software engineering issues related to distributed systems, deep learning, neuronal networks, distributed artificial intelligence, and parallel computing; applications in various fields, such as industry, bioinformatics, chemistry, and physics; mathematics issues related to distributed systems and artificial intelligence; and other issues concerning big data, security, distributed e-learning experiments, and data science. All these prospective topics bring with them numerous research challenges and opportunities. This is an open call for contributions to the Special Issue “Distributed Systems and Artificial Intelligence”.

Prof. Dr. Nicolae Goga
Dr. Dan Garlasu
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • distributed systems
  • deep learning
  • neuronal networks
  • big data
  • security
  • distributed e-learning experiments
  • data science

Published Papers (5 papers)

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Research

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16 pages, 16085 KiB  
Article
Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
by Urmila Pilania, Rohit Tanwar, Mazdak Zamani and Azizah Abdul Manaf
Future Internet 2022, 14(9), 254; https://doi.org/10.3390/fi14090254 - 25 Aug 2022
Cited by 7 | Viewed by 1596
Abstract
In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and [...] Read more.
In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and forwarding of embedded personal information specifically in public places is gaining more attention day by day. In this research work, the Integer Wavelet Transform technique along with JPEG (Joint Photograph Expert Group) compression is proposed to overcome some of the issues associated with steganography techniques. Video cover files and JPEG compression improve concealing capacity because of their intrinsic properties. Integer Wavelet Transform is used to improve the imperceptibility and robustness of the proposed technique. The Imperceptibility of the proposed work is analyzed through evaluation parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structure Similarity Metric), and CC (Correlation Coefficient). Robustness is validated through some image processing attacks. Complexity is calculated in terms of concealing and retrieval time along with the amount of secret information hidden. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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26 pages, 6629 KiB  
Article
Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster
by Nicholas James, Lee-Yeng Ong and Meng-Chew Leow
Future Internet 2022, 14(8), 220; https://doi.org/10.3390/fi14080220 - 25 Jul 2022
Cited by 4 | Viewed by 3069
Abstract
Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in [...] Read more.
Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in TensorFlow. Specifically, it investigates the performance difference between a 2-node Pi 4B Spark cluster and other systems, including a single Pi 4B and a mid-end desktop computer. Enhancements for the Pi 4B were studied and compared against the Spark cluster to identify the more effective method in increasing the Pi 4B’s DL performance. Three experiments involving DL inference, which in turn involve image classification and face detection tasks, were carried out. Results showed that enhancing the Pi 4B was faster than using a cluster as there was no significant performance difference between using the cluster and a single Pi 4B. The difference between the mid-end computer and a single Pi 4B was between 6 and 15 times in the experiments. In the meantime, enhancing the Pi 4B is the more effective approach for increasing the DL performance, and more work needs to be done for scalable distributed DL inference to eventuate. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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19 pages, 2805 KiB  
Article
Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment
by May Altulyan, Lina Yao, Chaoran Huang, Xianzhi Wang and Salil S. Kanhere
Future Internet 2021, 13(12), 305; https://doi.org/10.3390/fi13120305 - 28 Nov 2021
Cited by 3 | Viewed by 2472
Abstract
Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. [...] Read more.
Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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14 pages, 2616 KiB  
Article
Deep Model Poisoning Attack on Federated Learning
by Xingchen Zhou, Ming Xu, Yiming Wu and Ning Zheng
Future Internet 2021, 13(3), 73; https://doi.org/10.3390/fi13030073 - 14 Mar 2021
Cited by 61 | Viewed by 8796
Abstract
Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, [...] Read more.
Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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Review

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18 pages, 2186 KiB  
Review
Scientific Development of Educational Artificial Intelligence in Web of Science
by Antonio-José Moreno-Guerrero, Jesús López-Belmonte, José-Antonio Marín-Marín and Rebeca Soler-Costa
Future Internet 2020, 12(8), 124; https://doi.org/10.3390/fi12080124 - 24 Jul 2020
Cited by 44 | Viewed by 6838
Abstract
The social and technological changes that society is undergoing in this century are having a global influence on important aspects such as the economy, health and education. An example of this is the inclusion of artificial intelligence in the teaching–learning processes. The objective [...] Read more.
The social and technological changes that society is undergoing in this century are having a global influence on important aspects such as the economy, health and education. An example of this is the inclusion of artificial intelligence in the teaching–learning processes. The objective of this study was to analyze the importance and the projection that artificial intelligence has acquired in the scientific literature in the Web of Science categories related to the field of education. For this, scientific mapping of the reported documents was carried out. Different bibliometric indicators were analyzed and a word analysis was carried out. We worked with an analysis unit of 379 publications. The results show that scientific production is irregular from its beginnings in 1956 to the present. The language of greatest development is English. The most significant publication area is Education Educational Research, with conference papers as document types. The underlying organization is the Open University UK. It can be concluded that there is an evolution in artificial intelligence (AI) research in the educational field, focusing in the last years on the performance and influence of AI in the educational processes. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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