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 4542

Special Issue Editors


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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

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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

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Keywords

  • IoT
  • smart cities
  • blockchain
  • security
  • privacy

Published Papers (1 paper)

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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 2 | Viewed by 4059
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
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