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Technical Advances in Internet of Things for Smart Cities
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Technical Advances in Internet of Things for Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12940

Special Issue Editors

Dr. Seong-eun Yoo

E-Mail Website
Guest Editor
School of Computer and Communication Engineering, Daegu University, Gyeongsan 712-714, Republic of Korea
Interests: wireless sensor networks; industrial IoT; localization
Special Issues, Collections and Topics in MDPI journals
Dr. Taehong Kim

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Republic of Korea
Interests: edge computing; container orchestration; Internet of Things; SDN/NFV; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Youngsoo Kim

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Jeonju University, Jeonju 55069, Republic of Korea
Interests: artificial intelligence; machine learning; deep learning; Internet of Things; wireless sensor network; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the help of technical advances in information and communication technology (ICT), the Internet of Things (IoT) has been building up smart cities which are envisioned to improve the quality of life for the civilians with ICT. The applications (e.g., environmental monitoring, utility monitoring, asset tracking and localization, and so on) of the IoT in smart cities are diverse and continually spreading in everyday life, as well as workspaces such as offices, factories, and so on. In many applications, IoT devices gather physical data with sensors and send the data to an edge gateway or fog system. Depending on the application requirements, different IoT network standards (e.g., LR-WPAN, LPWAN, Wi-Fi, and so on) provide IoT connectivities with different devices in the network. In some cases, devices in the network can process and analyze the data to obtain useful information and provide earlier decision making at the IoT device or edge node level. Obviously, the cloud system can manage the received data from the field for further processing. To realize smart cities, research and development on every technical aspect of IoT systems are required.

This Special Issue focuses on the latest research, application, and adoption of IoT networks in smart cities from the perspective of protocols and applications, requiring a different level of reliability and real-time packet delivery. In such a context, this Special Issue invites contributions in the following topics (though without being limited to them):

  • Diverse IoT applications in smart cities;
  • Protocols and technologies for connected cars and smart transportation;
  • Testbeds and experimental results in smart cities;
  • Localization and tracking;
  • Data processing and analysis/machine learning and AI;
  • Edge and fog computing in smart cities;
  • IoT network protocols and standards.

Dr. Seong-eun Yoo
Dr. Taehong Kim
Dr. Youngsoo Kim
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. Sensors 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 2600 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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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

18 pages, 2380 KiB  
Article
Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments
by Jaime Govea, Walter Gaibor-Naranjo, Santiago Sanchez-Viteri and William Villegas-Ch
Sensors 2024, 24(2), 311; https://doi.org/10.3390/s24020311 - 5 Jan 2024
Cited by 2 | Viewed by 2196
Abstract
This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach [...] Read more.
This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management. Full article
(This article belongs to the Special Issue Technical Advances in Internet of Things for Smart Cities)
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17 pages, 2645 KiB  
Article
A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus
by Jamal Toutouh and Enrique Alba
Sensors 2022, 22(17), 6585; https://doi.org/10.3390/s22176585 - 31 Aug 2022
Cited by 9 | Viewed by 2020
Abstract
Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors [...] Read more.
Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value. Full article
(This article belongs to the Special Issue Technical Advances in Internet of Things for Smart Cities)
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16 pages, 794 KiB  
Article
Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy
by Quang-Minh Nguyen, Linh-An Phan and Taehong Kim
Sensors 2022, 22(8), 2869; https://doi.org/10.3390/s22082869 - 8 Apr 2022
Cited by 17 | Viewed by 7608
Abstract
Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the [...] Read more.
Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s. Full article
(This article belongs to the Special Issue Technical Advances in Internet of Things for Smart Cities)
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