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Special Issue "Applications of IoT and Machine Learning in Smart Cities"

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

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Dr. Ashfaq Ahmad
Website
Guest Editor
School of Electrical Engineering and Computing, the University of Newcastle, Callaghan NSW 2308, Australia
Interests: wireless networks (terrestrial; body area and underwater); Internet-of-Things; machine learning; optimization; smart grid
Prof. Dr. Jamil Yusuf Khan
Website
Guest Editor
School of Electrical Engineering and Computing, the University of Newcastle, Callaghan NSW 2308, Australia
Interests: cooperative networks; sensor networks; telecommunication networks; wireless networks; smart grid; Internet-of-Things
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

A smart city enhances the quality-of-experience of its stakeholders (service providers and customers) by providing ease of access to application oriented ubiquitous services. In essence, Internet-of-Things (IoT)-enabled devices can potentially serve as sources of information of interest (i.e., data) to the stakeholders.

With the rise in IoT, more smart devices will be integrated into smart cities, generating an enormous amount of real-time data. As the volume of the generated data increases, machine learning techniques (i.e., supervised, unsupervised, semisupervised, and reinforcement learning) can be employed to further enhance the intelligence level and the capabilities of various smart city applications (SCAs).

Recent research tendencies in IoT and machine learning for the development of various SCAs have demonstrated rich and diverse prospects, deserving further investigation. Thus, this Special Issue welcomes original contributions and review papers on applications of IoT and machine learning for smart cities, in the following potential areas:

  • Smart (electricity) grids
  • Smart health-care systems
  • Smart transportation systems
  • Smart security and surveillance systems
  • Smart logistics and supply chain management systems

Dr. Ashfaq Ahmad
Prof. Dr. Jamil Yusuf Khan
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 papers will be 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 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.

Keywords

  • Internet-of-Things
  • machine learning
  • smart city applications
  • quality-of-experience
  • monitoring
  • control
  • management

Published Papers (2 papers)

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Research

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Open AccessArticle
MoreAir: A Low-Cost Urban Air Pollution Monitoring System
Sensors 2020, 20(4), 998; https://doi.org/10.3390/s20040998 - 13 Feb 2020
Abstract
MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is [...] Read more.
MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. Full article
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
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Review

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Open AccessReview
Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation
Sensors 2020, 20(1), 43; https://doi.org/10.3390/s20010043 - 19 Dec 2019
Abstract
Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the [...] Read more.
Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT). Full article
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
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