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Systems
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AI, IoT, and Edge Computing for Sustainable Smart Cities
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AI, IoT, and Edge Computing for Sustainable Smart Cities

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 9965

Special Issue Editors

Dr. In-kee Kim

E-Mail Website
Guest Editor
School of Computing, University Of Georgia, Athens, GA 30602, USA
Interests: smart city; IoT; edge computing and edge AI; applied machine learning; cloud computing and distributed systems
Dr. Avinash Kalyanaraman

E-Mail Website
Guest Editor
Cisco Innovation Labs, San Jose, CA 95134, USA
Interests: smart building and home; IoT; applied machine learning; wireless systems; mobile computing and cyber-physical systems
Prof. Dr. Lakshmish Ramaswamy

E-Mail Website
Guest Editor
Department of Computer Science, University of Georgia, Athens, GA 30602, USA
Interests: smart city; IoT; cyber-physical systems; big data; machine learning engineering; health informatics; edge computing; cloud and distributed systems

Special Issue Information

Dear Colleagues,

The smart city concept is an emerging interdisciplinary research area that combines artificial intelligence (AI), computer science, communication systems, civil and transportation engineering, and geography/environmental science. Due to global warming and ever-increasing energy demand, highly sustainable, energy-efficient, smart technologies need to be urgently developed for and applied to our living environments. At the same time, due to the notable success of AI and machine learning, our life is becoming much more convenient with lightweight and real-time inferences on smart devices and communication systems. Therefore, this Special Issue invites state-of-the-art research articles that advance sustainability and intelligence in smart cities and smart homes. Specifically, this Special Issue is interested in research topics including but not limited to:

  • AI, IoT, edge and fog computing for smart city and home;
  • Data collection, data streaming, and big data analytics for smart city;
  • Energy/power management and harvesting techniques for sensors, devices, and smart applications;
  • Emerging smart city applications, including smart building, transportation, and environmental sensing;
  • Deployment experiences, case studies, and lessons learned in smart city and home;
  • Emerging standards for data collection, energy control, and interoperability of disparate systems in smart city;
  • Deep learning, applied machine learning, and optimization for smart city;
  • Sustainable and failure resilience techniques for smart city;
  • Sensing, modeling, and prediction technologies for smart city infrastructures;
  • Security and privacy in smart city
  • Precision agriculture and smart farming

Dr. In-kee Kim
Dr. Avinash Kalyanaraman
Prof. Dr. Lakshmish Ramaswamy
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. Systems is an international peer-reviewed open access monthly 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 (4 papers)

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Research

25 pages, 4981 KiB  
Article
VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions
by Ganeshkumar Perumal, Gopalakrishnan Subburayalu, Qaisar Abbas, Syed Muhammad Naqi and Imran Qureshi
Systems 2023, 11(8), 436; https://doi.org/10.3390/systems11080436 - 21 Aug 2023
Cited by 16 | Viewed by 1793
Abstract
Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful [...] Read more.
Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems. Full article
(This article belongs to the Special Issue AI, IoT, and Edge Computing for Sustainable Smart Cities)
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17 pages, 2493 KiB  
Article
Official Statistics and Big Data Processing with Artificial Intelligence: Capacity Indicators for Public Sector Organizations
by Syed Wasim Abbas, Muhammad Hamid, Reem Alkanhel and Hanaa A. Abdallah
Systems 2023, 11(8), 424; https://doi.org/10.3390/systems11080424 - 13 Aug 2023
Cited by 1 | Viewed by 2487
Abstract
Efficient monitoring and achievement of the Sustainable Development Goals (SDGs) has increased the need for a variety of data and statistics. The massive increase in data gathering through social networks, traditional business systems, and Internet of Things (IoT)-based sensor devices raises real questions [...] Read more.
Efficient monitoring and achievement of the Sustainable Development Goals (SDGs) has increased the need for a variety of data and statistics. The massive increase in data gathering through social networks, traditional business systems, and Internet of Things (IoT)-based sensor devices raises real questions regarding the capacity of national statistical systems (NSS) for utilizing big data sources. Further, in this current era, big data is captured through sensor-based systems in public sector organizations. To gauge the capacity of public sector institutions in this regard, this work provides an indicator to monitor the processing capacity of the public sector organizations within the country (Pakistan). Some of the indicators related to measuring the capacity of the NSS were captured through a census-based survey. At the same time, convex logistic principal component analysis was used to develop scores and relative capacity indicators. The findings show that most organizations hesitate to disseminate data due to concerns about data privacy and that public sector organizations’ IT personnel are unable to deal with big data sources to generate official statistics. Artificial intelligence (AI) techniques can be used to overcome these challenges, such as automating data processing, improving data privacy and security, and enhancing the capabilities of IT human resources. This research helps to design capacity-building initiatives for public sector organizations in weak dimensions, focusing on leveraging AI to enhance the production of quality and reliable statistics. Full article
(This article belongs to the Special Issue AI, IoT, and Edge Computing for Sustainable Smart Cities)
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23 pages, 1800 KiB  
Article
New Trends in Smart Cities: The Evolutionary Directions Using Topic Modeling and Network Analysis
by Minjeong Oh, Chulok Ahn, Hyundong Nam and Sungyong Choi
Systems 2023, 11(8), 410; https://doi.org/10.3390/systems11080410 - 9 Aug 2023
Cited by 4 | Viewed by 2571
Abstract
The COVID-19 pandemic has affected smart city operations and planning. Smart cities, where digital technologies are concentrated and implemented, face new challenges in becoming sustainable from social, ecological, and economic perspectives. Using text mining methodologies of topic modeling and network analysis, this study [...] Read more.
The COVID-19 pandemic has affected smart city operations and planning. Smart cities, where digital technologies are concentrated and implemented, face new challenges in becoming sustainable from social, ecological, and economic perspectives. Using text mining methodologies of topic modeling and network analysis, this study aims to identify keywords in the field of smart cities after the pandemic and provide a future-oriented perspective on the direction of smart cities. A corpus of 1882 papers was collected from the Web of Science and Scopus databases from December 2019 to November 2022. We identified six categories of potential issues in smart cities using topic modeling: “supply chain”, “resilience”, “culture and tourism”, “population density”, “mobility”, and “zero carbon emission”. This study differs from previous research because it is a quantitative study based on text mining analysis and deals with smart cities, given the prevalence of COVID-19. This study also provides insights into the development of smart city policies and strategies to improve urban resilience during the pandemic by anticipating and addressing related issues. The findings of this study will assist researchers, policymakers, and planners in developing smart city strategies and decision-making in socioeconomic, environmental, and technological areas. Full article
(This article belongs to the Special Issue AI, IoT, and Edge Computing for Sustainable Smart Cities)
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14 pages, 2540 KiB  
Article
Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT
by Moteeb Al Moteri, Surbhi Bhatia Khan and Mohammed Alojail
Systems 2023, 11(6), 308; https://doi.org/10.3390/systems11060308 - 16 Jun 2023
Cited by 5 | Viewed by 1995
Abstract
Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of [...] Read more.
Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively. Full article
(This article belongs to the Special Issue AI, IoT, and Edge Computing for Sustainable Smart Cities)
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