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Smart City Innovation and Resilience in the Era of Artificial Intelligence

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 13144

Special Issue Editors

Dr. Antonio De Nicola

E-Mail Website
Guest Editor
ENEA-Centro Ricerche Casaccia, Via Anguillarese 301, 00123 Rome, Italy
Interests: Artificial Intelligence; computational creativity; linked data; ontology; ontology engineering; crisis management; resilience; risk assessment; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Paola Rizzi

E-Mail Website
Guest Editor
1. DADU, University of Sassari, Pou Salit, Piazza Duomo 6, 07041 Alghero, Italy
2. DICEAA, University of L’Aquila, Via G. Gronchi 18, 67100 L’Aquila, Italy
Interests: cultural heritage; decision making; disaster mitigation; spatial planning; urban design
Dr. Maria Luisa Villani

E-Mail Website
Guest Editor
ENEA National Agency for New Technologies, Energy and Sustainable Economic Development, 00196 Rome, Italy
Interests: tools for risk assessment and resilience of critical infrastructures to natural hazards; ontologies; knowledge graphs; IoT system architectures for public security; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities aim at improving the quality of life of citizens by accounting for environmental sustainability. However, the increasing complexity and interrelationships of smart city services pose several issues on smart city resilience that are among the challenges of current research. Artificial Intelligence (AI) techniques, such as machine learning, deep learning, computational creativity, and semantics, are indeed used to improve the methods and tools devoted to collecting and analyzing citizens’ opinions, sensing people’s behaviors, organizing knowledge, predicting and forecasting consequences of hazardous events, and, more in general, supporting decisional processes. However, other than enabling and facilitating these activities, AI is also transforming the way people live.

The goal of this Special Issue is to stimulate the debate on both AI applications and impacts for smart cities, including the expected benefits and the possible risks originating from their use. The intent is to provide a deep account for novel and disruptive AI approaches to smart city innovation and resilience of urban areas and communities. Focus on the resilience of AI systems themselves, such as designing dependable systems, is also appreciated. Furthermore, the Special Issue welcomes visionary papers related to AI safety, such as handling automation bias and sustaining human performance, and social aspects like handover and human–machine communication.

Dr. Antonio De Nicola
Dr. Paola Rizzi
Dr. Maria Luisa Villani
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. Sustainability 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 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.

Keywords

  • AI applications of smart sensor data
  • AI-driven open innovation for smart cities
  • AI for crisis management
  • AI for decision making
  • AI for environmental sustainability
  • AI for resilient communities
  • AI for risk assessment
  • AI for smart city safety
  • AI for urban resilience
  • AI opportunities for smart city innovation
  • AI safety issues in smart city applications
  • co-creation approaches of smart city knowledge
  • computational creativity approaches for rethinking urban areas
  • critical infrastructure resilience
  • explainable artificial intelligence for smart city resilience applications
  • machine learning and/or deep learning approaches for smart city innovation
  • semantic technologies for smart cities
  • smart city ontologies
  • social media analysis for smart city resilience

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Published Papers (2 papers)

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19 pages, 8647 KiB  
Article
Distributed Deep Features Extraction Model for Air Quality Forecasting
by Axel Gedeon Mengara Mengara, Younghak Kim, Younghwan Yoo and Jaehun Ahn
Sustainability 2020, 12(19), 8014; https://doi.org/10.3390/su12198014 - 28 Sep 2020
Cited by 7 | Viewed by 3437
Abstract
Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) [...] Read more.
Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) autoencoder model trained using a distributed architecture to predict the concentration of the air quality particles (PM2.5 and PM10). The proposed deep learning model can automatically learn the intrinsic correlation among the pollutants in different location. Also, the meteorological and the pollution gas information at each location are fully utilized, which is beneficial for the performance of the model. We used multiple one-dimension convolutional neural network (CNN) layers to extract the local spatial features and a stacked Bi-LSTM layer to learn the spatiotemporal correlation of air quality particles. In addition, we used a stacked deep autoencoder to encode the essential transformation patterns of the pollution gas and the meteorological data, since they are very important for providing useful information that can significantly improve the prediction of the air quality particles. Finally, in order to reduce the training time and the resource consumption, we used a distributed deep leaning approach called data parallelism, which has never been used to tackle the problem of air quality forecasting. We evaluated our approach with extensive experiments based on the data collected in Busan metropolitan city. The results reveal the superiority of our framework over ten baseline models and display how the distributed deep learning model can significantly improve the training time and even the prediction accuracy. Full article
(This article belongs to the Special Issue Smart City Innovation and Resilience in the Era of Artificial Intelligence)
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Review

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40 pages, 877 KiB  
Review
Smart City Ontologies and Their Applications: A Systematic Literature Review
by Antonio De Nicola and Maria Luisa Villani
Sustainability 2021, 13(10), 5578; https://doi.org/10.3390/su13105578 - 17 May 2021
Cited by 44 | Viewed by 8410
Abstract
The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for [...] Read more.
The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems. Full article
(This article belongs to the Special Issue Smart City Innovation and Resilience in the Era of Artificial Intelligence)
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