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Applied Sciences
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Scalable Distributed Systems Based on Intelligent IoTs
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Scalable Distributed Systems Based on Intelligent IoTs

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8454

Special Issue Editors

Dr. Muhammad Shiraz

E-Mail Website
Guest Editor
Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44081, Pakistan
Interests: distributed applications design for ubiquitous networks; distributed systems; lightweight applications; smart client applications and optimization strategies; mobile cloud computing; edge computing and fog computing
Dr. Qaisar Shaheen

E-Mail Website
Guest Editor
Department of Computer Science and IT, The Islamia University of Bahawalpur, Rahim Yar Khan Campus, Rahim Yar Khan 64200, Pakistan
Interests: Internet of Things; fog/cloud computing; edge computing; AI/ML; security

Special Issue Information

Dear Colleagues,

Advances in Information Communication Technologies (ICTs) have made interoperability possible, such that everyday devices at home, in the city, hospitals, educational institutes and industries can be networked to give the inhabitants new and unexpected possibilities. Particularly, the high penetration rate of the Intelligent Internet of Things (IIoTs) paradigm in every environment allows for the introduction of the new concepts of “Intelligent home, Intelligent/Smart city, Intelligent e-Health, Intelligent e-learning and Intelligent/smart industries”. Thanks to the latest sensor technologies and Distributed Computing, the theological environment monitors everything to improve their overall quality of life.

International Data Corporation (IDC) research estimates that by 2025, there will be more than 41.6 billion connected IoT devices, and that the amount of data produced by these devices will increase at an annual growth rate of 28.7% between 2018 and 2025. Thus, it is essential that we establish methods to handle the enormous amounts of heterogeneous data generated by IoT devices and to integrate and scale the applications that run distributed computing components, systems, and platforms.

Therefore, Distributed Computing (DC) applications along with the IoT field are essential technical directions that enable e-health, hospitals/intelligent homes/cities, intelligent industries, intelligent supply chains, automation, and a variety of other computing and networking environments.

How to design efficient system architectures, transmission strategies, and protocols for DC-based IoT and how to efficiently analyze and evaluate the system performance are important issues to address. This Special Issue intends to collect research from academia and industry, with an emphasis on addressing all these topics and inviting contributions from worldwide leading researchers. Both original research and review articles are welcome.

Potential topics include but are not limited to the following:

  1. Quality of Experience/Quality of Service for IIoT-based DC.
  2. Cloud, edge, fog, and dew computing for IIoT.
  3. Networked computing architectures and infrastructures for IIoT-based DC.
  4. Location Aware/Energy-efficient solutions for IIoT-based DC.
  5. Mobility management in DC.
  6. Security, Privacy, and Trust in scalable DC.
  7. Transmission strategies in IIoT-based DC.
  8. Resource allocation in IIoT-based DC.
  9. Advances in Soft-computing for DC-based IIoT Applications.
  10. Offloading/Pre-Push strategies in DC-based IIoT.
  11. New data collection schemes and enhanced monitoring using DC.

Dr. Muhammad Shiraz
Dr. Qaisar Shaheen
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. Applied Sciences 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 2400 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.

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Published Papers (2 papers)

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Research

19 pages, 3148 KiB  
Article
A Data Analytic Monitoring with IoT System of the Reproductive Conditions of the Red Worm as a Product Diversification Strategy
by Karla Yohana Sánchez-Mojica, Luis Asunción Pérez-Domínguez, Julián Gutiérrez Londoño and Darwin Orlando Cardozo Sarmiento
Appl. Sci. 2023, 13(18), 10522; https://doi.org/10.3390/app131810522 - 21 Sep 2023
Cited by 1 | Viewed by 1777
Abstract
The Internet of Things (IoT) is becoming increasingly important due to the ability to collect data in real time and monitor the performance of systems. In this sense, the objective of the project is to create an IoT system to monitor and enhance [...] Read more.
The Internet of Things (IoT) is becoming increasingly important due to the ability to collect data in real time and monitor the performance of systems. In this sense, the objective of the project is to create an IoT system to monitor and enhance red boll worm farming conditions in California as part of a strategy to diversify annelid-based goods. Therefore, the goal is to expand this animal’s productivity so that additional items can be made from California red worms. Furthermore, the method used implies a research design that uses an experimental approach to obtain data based on the variable conditions identified in the literature review. The analysis of the data will allow determination of the factors that result in optimization of production, and at the same time creation of a production estimation in the network platform. Finally, this project proposes to facilitate the monitoring and control of the variables that interfere in the earthworm reproduction process to increase the production of annelids in pursuit of product diversification. In addition, we put it into practice in real life to demonstrate its applicability and efficacy. In this mode, the results indicate potential findings about IoT application in agriculture situations. Full article
(This article belongs to the Special Issue Scalable Distributed Systems Based on Intelligent IoTs)
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17 pages, 3074 KiB  
Article
IoT-Based Smart Surveillance System for High-Security Areas
by Hina Afreen, Muhammad Kashif, Qaisar Shaheen, Yousef H. Alfaifi and Muhammad Ayaz
Appl. Sci. 2023, 13(15), 8936; https://doi.org/10.3390/app13158936 - 3 Aug 2023
Cited by 7 | Viewed by 5852
Abstract
The world we live in today is becoming increasingly less tethered, with many applications depending on wireless signals to ensure safety and security. Proactive security measures can help prevent the loss of property due to actions such as larceny/theft and burglary. An IoT-based [...] Read more.
The world we live in today is becoming increasingly less tethered, with many applications depending on wireless signals to ensure safety and security. Proactive security measures can help prevent the loss of property due to actions such as larceny/theft and burglary. An IoT-based smart Surveillance System for High-Security Areas (SS-HSA) has been developed to address this issue effectively. This system utilizes a Gravity Microwave Sensor (GMS), which is highly effective due to its ability to penetrate nonmetallic obstructions. Combining GMS with Arduino UNO is a highly effective technique for detecting suspected objects behind walls. The GMS can also be integrated with the global system for mobile (GSM) communications, making it an IoT-based solution. The SS-HSA system utilizes machine learning AI algorithms operating at a GMS frequency to analyze and calculate accuracy, precision, F1-Scores, and Recall. After a thorough evaluation, it was determined that the Random Forest Classifier achieved an accuracy rate of 95%, while the Gradient Boost Classifier achieved an accuracy rate of 94%. The Naïve Bayes Classifier followed closely behind with a rate of 93%, while the K Nearest Neighbor and Support Vector Machine both achieved an accuracy rate of 96%. Finally, the Decision Tree algorithm outperformed the others in terms of accuracy, presenting a value of 97%. Furthermore, in the studied machine learning AI algorithms, it was observed that the Decision Tree was optimal for SS-HSA. Full article
(This article belongs to the Special Issue Scalable Distributed Systems Based on Intelligent IoTs)
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