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Sensor-Fed and Human-Centric Artificial Intelligence for Smart Cities

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

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 6337

Special Issue Editors


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Guest Editor
Computer Languages and Systems Department, Jaume I University, Castellón de la Plana 12071, Spain
Interests: machine learning; indoor positioning and navigation; sensor networks; internet of things
Special Issues, Collections and Topics in MDPI journals
Cardiff University, Cardiff, UK
Interests: machine learning; data science; anomaly/novelty detection; semantic similarity analysis; neural networks/deep learning and condition monitoring

E-Mail Website
Guest Editor
University of the Basque Country, Bilbao, Spain
Interests: parallel computing; high performance computing; big data

Special Issue Information

Dear Colleagues,

Computer systems based on Artificial Intelligence (AI) are able to perform tasks that resemble, or even improve, the performance achieved by humans. The inclusion of AI has been proven to be effective in tasks such as autonomous driving, gaming, objects, person identification, and speech recognition, just to cite a few examples.

Machine Learning (ML), as a subset of AI, corresponds to a set of techniques that derive models by learning from data. Some fields where ML has been successfully applied are market analysis, recommendation systems, spam detection, and sentiment analysis.

The latest research into Smart Cities has been targeted at improving the way that city infrastructure is managed, with a focus on being more efficient, reliable, and respectful to the environment. Another aim has been to offer updated information to its citizens for them to be aware of and to make better, more informed decisions on the use of urban resources. This has been possible thanks to the use of Information and Communication Technologies (ICTs) and the Internet of Things (IoT) ecosystems. The data acquired by IoT devices, provided by citizens, institutions, and the government, are used to improve citizen services and for better managing urban infrastructures.

In this context, AI and ML play an important role, e.g., in learning from data, creating models based on this data, and reasoning based on new incoming data. However, important issues arise in the context of Smart Cities, such as managing big amounts of heterogeneous data, updating of models based on new trends present in the data, data presentation to final users, and model selection, among others.

This Special Issue seeks cutting edge research from academia, industry, and practitioners, with an emphasis on original, novel, innovative, and impact-oriented research providing insights into making “Sensor-Fed and Human-Centric Artificial Intelligence for Smart Cities” a reality. An area of particular interest to this Special Issue is research that considers how Collective Intelligence can meet Machine Intelligence in order to give place to more user-centric and user-driven intelligence to enable Smart Cities where humans and Internet-connected devices cooperate and influence their behavior in order to promote autonomous life, better energy sustainability, or more sustainable and inclusive cities.

Specifically, this Special Issue welcomes two categories of papers: (1) high quality and significantly extended papers from the International Conference on Smart and Sustainable Technologies 2020 (SpliTech 2020) and 14th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2020); and (2) papers contributed as a result of this open call which address any of the below list of topics. Topics of interest include research on how to enable Sensor-Fed and Human-Centric Artificial Intelligence for Smart Cities, such as the following non-exhaustive list of topics:

  • Behavior Change Analysis;
  • Internet of Sensing, Thinking, and Creation;
  • Human-Centric Computing and Cyber–Physical–Social Systems;
  • Crowd Sensing and Human Intelligence;
  • Biometric Sensors and Activity Recognition;
  • Brain Information Sensing and Processing;
  • Personal Big Data Analytics;
  • Sentiment Analysis and Affective Computing;
  • AI-Powered Smart Devices;
  • Ambient-Assisted Cities;
  • Personal Internet-Based Healthcare, Wellbeing, and Wellness;
  • Ontologies and Knowledge Graphs for Smart Cities;
  • IoT Big Data Processing and Urban Computing and Analytics;
  • Symbolic AI vs. Machine Learning Approaches for Smart Cities;
  • Explainable, Auditable, and Transparent AI;
  • Disruptive Technologies for Open Government and Smarter Public Services.

Each paper that is submitted will be reviewed by at least three independent experts. We also recommend submission of supplementary material with each paper, including test data and multimedia, as it significantly increases the visibility, downloads, and citations of articles.

To solicit papers, we will advertise the call at both SplitTech 2020 and UCAmI 2020 conferences, and through mailing lists and our colleagues, including particular colleagues working in the areas related to this Special Issue, to invite submissions of high quality.

Selection and Evaluation Criteria

  • Relevance to the topics of this Special Issue
  • Research novelty (e.g., new techniques) and potential impact
  • Content quality and readability
Dr. Diego López-de-Ipiña
Dr. Oscar Belmonte
Dr. Yuhua Li
Dr. Unai López Novoa
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.

Published Papers (2 papers)

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Research

15 pages, 699 KiB  
Article
Behavior Modeling for a Beacon-Based Indoor Location System
by Aritz Bilbao-Jayo, Aitor Almeida, Ilaria Sergi, Teodoro Montanaro, Luca Fasano, Mikel Emaldi and Luigi Patrono
Sensors 2021, 21(14), 4839; https://doi.org/10.3390/s21144839 - 15 Jul 2021
Cited by 19 | Viewed by 2763
Abstract
In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture [...] Read more.
In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy. Full article
(This article belongs to the Special Issue Sensor-Fed and Human-Centric Artificial Intelligence for Smart Cities)
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28 pages, 753 KiB  
Article
PADL: A Modeling and Deployment Language for Advanced Analytical Services
by Josu Díaz-de-Arcaya, Raúl Miñón, Ana I. Torre-Bastida, Javier Del Ser and Aitor Almeida
Sensors 2020, 20(23), 6712; https://doi.org/10.3390/s20236712 - 24 Nov 2020
Cited by 6 | Viewed by 2748
Abstract
In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of [...] Read more.
In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments. Full article
(This article belongs to the Special Issue Sensor-Fed and Human-Centric Artificial Intelligence for Smart Cities)
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