Blockchain for IoT-Based Smart Cities: Advances, Requirements, and Future Challenges

A special issue of Telecom (ISSN 2673-4001).

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5649

Special Issue Editors


E-Mail Website
Guest Editor
Department of Information Security, Cryptology, and Mathematics, Kookmin University, Seoul 02707, Korea
Interests: network security; data security; blockchain; IoT; smart city; AI
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Convergence Science, Kongju National University, Gongju 32588, Republic of Korea
Interests: AI; webometrics; open data; data security; SNS security; SNS analysis; knowledge management; digital convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Blockchain technology in smart cities can serve as a platform for exchanging data with a high degree of reliability and transparency without the need for a centralized entity. In recent years, there has been a notable increase in interest in IoT-based smart cities from both academia and industries. In this Special Issue, we aim to highlight the role of blockchain technology in various aspects, including smart transportation, smart healthcare, information security, infrastructure security, various facilities, communication networks, cloud computing, supply chain protection, distributed computing, scalability of blockchain, blockchain-backed IoT-based election systems, data storage, etc. We encourage researchers from various domains to foster collaboration among such interdisciplinary areas and to deliver their contributions on various topics related to blockchain for IoT-based smart cities. This Special Issue covers recent advancements in blockchain for IoT-based smart cities. It will also cover the requirements of blockchain and future challenges. It will cover but is not limited to the following:

  • Applications of blockchain for IoT-based smart cities;
  • Current research advancements in blockchain for IoT-based smart cities;
  • Blockchain-based cyber-physical systems for smart cities;
  • Blockchain-based information hiding/encryption in smart cities;
  • Blockchain-based lightweight algorithms and protocols for the IoT;
  • security and privacy solutions for IoT-based smart cities using blockchain;
  • Smart contract and distributed ledger for IoT-based smart cities;
  • Security, privacy, and trust issues;
  • Blockchain for the smart healthcare system in IoT-based smart cities.

Dr. Eunmok Yang
Dr. Srijana Acharya
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. Telecom is an international peer-reviewed open access quarterly 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 1200 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

  • IoT
  • smart cities
  • blockchain
  • security
  • privacy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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

Research

17 pages, 16219 KiB  
Article
Development of an Analog Gauge Reading Solution Based on Computer Vision and Deep Learning for an IoT Application
by João Peixoto, João Sousa, Ricardo Carvalho, Gonçalo Santos, Joaquim Mendes, Ricardo Cardoso and Ana Reis
Telecom 2022, 3(4), 564-580; https://doi.org/10.3390/telecom3040032 - 14 Oct 2022
Cited by 8 | Viewed by 5068
Abstract
In many industries, analog gauges are monitored manually, thus posing problems, especially in large facilities where gauges are often placed in hard-to-access or dangerous locations. This work proposes a solution based on a microcontroller (ESP32-CAM) and a camera (OV2640 with a 65° FOV [...] Read more.
In many industries, analog gauges are monitored manually, thus posing problems, especially in large facilities where gauges are often placed in hard-to-access or dangerous locations. This work proposes a solution based on a microcontroller (ESP32-CAM) and a camera (OV2640 with a 65° FOV lent) to capture a gauge image and send it to a local computer where it is processed, and the results are presented in a dashboard accessible through the web. This was achieved by first applying a Convolutional Neural Network (CNN) to detect the gauge with the CenterNet HourGlass104 model. After locating the dial, it is segmented using the circle Hough transform, followed by a polar transformation to determine the pointer angle using the pixel projection. In the end, the indicating value is determined using the angle method. The dataset used was composed of 204 gauge images split into train and test sets using a 70:30 ratio. Due to the small size of the dataset, a diverse set of data augmentations were applied to obtain high accuracy and a well-generalized gauge detection model. Additionally, the experimental results demonstrated adequate robustness and accuracy for industrial environments achieving an average relative error of 0.95%. Full article
Show Figures

Figure 1

Back to TopTop