Special Issue "Cloud-Based Big Data Analytics in the Internet of Things and Smart Cities"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2280

Special Issue Editors

Learning, Data and Robotics Laboratory, ESIEA Graduate Engineering School, 75005 Paris, France
Interests: Internet of Things; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
ISEP Graduate Engineering School in Digital Sciences and Technologies, Paris, France
Interests: big data; Internet of Things; middleware and virtualization
Special Issues, Collections and Topics in MDPI journals
School of Computing and Informatics, Universiti Teknologi Brunei (UTB), Brunei
Interests: edge computing; Internet of Things; green networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) and, in particular, smart cities (i.e., as one of the main use cases of IoT), have recently provided a new application domain for data analytics, where relatively few studies have been reported in the literature.

The IoT often generates a large amount of data (Big Data). However, Big Data analytics for IoT is quite complex due to a variety of issues, such as: i) the requirements of cross-thematic applications (e.g., energy, transport, water and urban), ii) multiple data sources providing unstructured, semi-structured or structured data, and iii) the trustworthiness of data.

On the other hand, a review of the state of the art shows very promising insights about utilizing cloud computing resources for IoT-based Big Data analytics. It can provide solutions to facilitate storing, processing, and analyzing Big Data for information and knowledge generation with artificial intelligence (AI) techniques.

This Special Issue aims to explore the potential of cloud computing (i.e., including all cloud-related technologies including edge/fog computing) and Big Data analytics in IoT and  smart cities by going beyond the existing conventional approaches and architectures.

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Architecture for cloud-based data analytics: from a centralized to fully distributed architecture;
  • Data-driven IoT applications (e.g., smart cities, industrial, healthcare applications, etc.);
  • AI techniques for autonomous IoT applications;
  • Cloud computing technologies for IoT (e.g., Native Cloud, Serverless Cloud, etc.);
  • Edge/Fog/ROOF computing, edge intelligence in IoT and smart cities;
  • Hierarchical computing, centralized/distributed/decentralized computing for IoT;
  • 5G MEC-based IoT and smart cities;
  • Security, privacy and trust in IoT, Big Data and AI;
  • Support of emerging technologies (i.e., digital twin, blockchain, etc.).

Prof. Gyu Myoung Lee
Dr. Ehsan Ahvar
Dr. Shohreh Ahvar
Dr. S. H. Shah Newaz
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. Electronics 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.


  • Cloud computing
  • Edge computing
  • Internet of Things
  • Smart cities
  • Big Data
  • Data analytics

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach
Electronics 2022, 11(4), 608; https://doi.org/10.3390/electronics11040608 - 16 Feb 2022
Cited by 2 | Viewed by 1392
Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the [...] Read more.
Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. Full article
Show Figures

Figure 1

Back to TopTop