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IoT Application for Smart Cities
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IoT Application 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 (20 January 2023) | Viewed by 12203

Special Issue Editor

Dr. Yeh Hsi-Jen James

E-Mail Website
Guest Editor
Engineering and Computer Science, Azusa Pacific University, Azusa, USA
Interests: Internet-of-Things; Wireless Communications; Radars; Embedded Systems; Biometrics

Special Issue Information

Dear colleagues,

Cities and metropolises are facing great challenges as they grow in both number and size. In the developing world, rapid economic growth and urbanization drive problems in transportation, communications, sanitation, environmental quality, and crime. With climate change, air quality and water management become larger issues, as recently exemplified by dust storms, wildfires floods, and droughts affecting both developing and industrialized cities.

Internet-of-Things holds great promise in solving many of the problems that cities are struggling with. The ability to densely monitor changing conditions through low-cost sensor networks over a large geographic allows governments, corporations, and individuals to react and create plans with unprecedented precision. The IoT network is greatly enhanced by powerful data analytics coupled with data visualization and user interface over Web or mobile applications. Densely populated IoT actuators further form control networks that provide manipulation with exceptionally fine granularity, and promise much better outcomes than previously possible.

The Special Issue “IoT Applications for Smart Cities” calls for submissions of recent, high-impact advances and/or deployments in this important area.

Dr. Yeh Hsi-Jen James
Guest Editor

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.

Keywords

  • IoT sensor network
  • IoT data analytics
  • Sensor data visualization and user interface
  • Smart city monitoring, including transportation, communications, sanitation, environmental quality, crime, etc.
  • IoT control network

Published Papers (4 papers)

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Research

15 pages, 1173 KiB  
Article
Hierarchical Attention Neural Network for Event Types to Improve Event Detection
by Yanliang Jin, Jinjin Ye, Liquan Shen, Yong Xiong, Lele Fan and Qingfu Zang
Sensors 2022, 22(11), 4202; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114202 - 31 May 2022
Cited by 1 | Viewed by 1546
Abstract
Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbalance problems in small-scale [...] Read more.
Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbalance problems in small-scale datasets. For this reason, the correlation information of event types can be used to alleviate the above problems. In this paper, we design a Hierarchical Attention Neural Network for Event Types (HANN-ET). Specifically, we select Long Short-Term Memory (LSTM) as the semantic encoder and utilize dynamic multi-pooling and the Graph Attention Network (GAT) to enrich the sentence feature. Meanwhile, we build several upper-level event type modules and employ a weighted attention aggregation mechanism to integrate these modules to obtain the correlation event type information. Each upper-level module is completed by a Neural Module Network (NMNs), event types within the same upper-level module can share information, and an attention aggregation mechanism can provide effective bias scores for the trigger word classifier. We conduct extensive experiments on the ACE2005 and the MAVEN datasets, and the results show that our approach outperforms previous state-of-the-art methods and achieves the competitive F1 scores of 78.9% on the ACE2005 dataset and 68.8% on the MAVEN dataset. Full article
(This article belongs to the Special Issue IoT Application for Smart Cities)
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25 pages, 1447 KiB  
Article
Fuzzy System to Assess Dangerous Driving: A Multidisciplinary Approach
by Carlos Javier Ronquillo-Cana, Pablo Pancardo, Martha Silva, José Adán Hernández-Nolasco and Matias Garcia-Constantino
Sensors 2022, 22(10), 3655; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103655 - 11 May 2022
Cited by 2 | Viewed by 2306
Abstract
Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or [...] Read more.
Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or sensors that provide objective variables (acceleration, turns and speed), and (ii) analyzing responses to questionnaires from behavioral science that provide subjective variables (driving thoughts, opinions and perceptions from the driver). However, we believe that a holistic and more realistic assessment requires a combination of both types of variables. Therefore, we propose a three-phase fuzzy system with a multidisciplinary (computer science and behavioral sciences) approach that draws on the strengths of sensors embedded in smartphones and questionnaires to evaluate driver behavior and social desirability. Our proposal combines objective and subjective variables while mitigating the weaknesses of the disciplines used (sensor reading errors and lack of honesty from respondents, respectively). The methods used are of proven reliability in each discipline, and their outputs feed a combined fuzzy system used to handle the vagueness of the input variables, obtaining a personalized result for each driver. The results obtained using the proposed system in a real scenario were efficient at 84.21%, and were validated with mobility experts’ opinions. The presented fuzzy system can support intelligent transportation systems, driving safety, or personnel selection. Full article
(This article belongs to the Special Issue IoT Application for Smart Cities)
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42 pages, 12547 KiB  
Article
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
by Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach and Gavin Taylor
Sensors 2021, 21(23), 8009; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238009 - 30 Nov 2021
Cited by 4 | Viewed by 2253
Abstract
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty [...] Read more.
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions. Full article
(This article belongs to the Special Issue IoT Application for Smart Cities)
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20 pages, 7824 KiB  
Article
From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
by Tiago Veiga, Arne Munch-Ellingsen, Christoforos Papastergiopoulos, Dimitrios Tzovaras, Ilias Kalamaras, Kerstin Bach, Konstantinos Votis and Sigmund Akselsen
Sensors 2021, 21(9), 3190; https://0-doi-org.brum.beds.ac.uk/10.3390/s21093190 - 05 May 2021
Cited by 12 | Viewed by 4995
Abstract
Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on [...] Read more.
Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality. Full article
(This article belongs to the Special Issue IoT Application for Smart Cities)
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