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Evolution of Smart Cities and Societies Using Emerging Technologies
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Evolution of Smart Cities and Societies Using Emerging Technologies

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (25 June 2022) | Viewed by 9099

Special Issue Editor

Dr. Ashutosh Sharma

E-Mail Website
Guest Editor
Institute of Computer Technology and Information Security, Southern Federal University, 344006 Rostov-on-Don, Russia.
Interests: Network Reliability; Network Mangement; VANET; Security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Communities living in urban areas are naturally affected by advances in issues related to society, science, morphology and various others. The rationale behind Smart Cities is to bolster such advances by combining the technology of sensors with Big Data through the Internet of Things (IoT). The resultant increase in the amount of data available provides more information for researchers to study aspects of urban areas including the economy.

Big Data prepared through Artificial Intelligence (AI) can contribute significantly to urban environments, and researchers ought not to ignore the potential impact this will have on sensibility and liveability estimations. As shown by UN data, global population is expected to reach up to 9.7 billion before the end of 2050. It is presumed that 70% of that people will live in an urban environment, with over 10m people living in some urban areas. As this number becomes a reality, we will have to face challenges with respect to resources and living conditions, which are imperative to those living in urban environments. Further important issues will be faced by administrators to prevent problems with sanitation, traffic, bad route behaviour, etc.

Largely, these issues can be addressed with the use of AI-enabled IoT. Technological progress can support urban inhabitants, making their experience of living in Smart Cities more fulfilling and secure. This has led to a rise in the popularity of the idea of Smart Cities. A Smart City is a city that uses information and technology to improve the quality and execution of urban organizations (such as essential infrastructure and transportation), thus reducing wastage and significant expense. Smart Cities make use of ICT in a way that emphatically and positively impacts the inhabitants.

Artificial Intelligence offers an intensive evaluation of how the features of a Smart City are produced at different levels through automation, for instance, geospatial information, data examination, data portrayal, smart-related data, and quick natural frameworks. Advances in electronics and technology bring us closer to producing a consistent model of human-made structures, from urban regions and transportation establishments to utility frameworks. This continuous living model empowers us all to manage and improve these dynamic working structures. Smart Cities and Artificial Intelligence provide a multidisciplinary approach to the evaluation of Smart Cities, using speculative and applied information.

Topics of interest to this Special Issue include, but are not limited to:

  • A system of smart things (sensors, cameras, actuators, etc) for aggregation of data;
  • Smart parking and traffic management and application of virtual reality;
  • Improving the driving safety in urban communities and GIS mapping;
  • Smart Classrooms, Smart Agriculture, Smart Industries;
  • Tools and techniques to analyse and visualize aggregated data by sensors;
  • Data storage technique, getting insight into data;
  • Streaming data processor for aggregating data streams and distributing them to a data storage device and control applications;
  • Helping authorities get familiar with how individuals use urban areas;
  • Trust management schemes for smart cities using emerging technologies;
  • Security, privacy, and reliability of smart city applications using Edge Computing and Blockchain.

Dr. Ashutosh Sharma
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. Information 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 1600 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

13 pages, 1135 KiB  
Article
Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches
by Ahatsham Hayat, Fernando Morgado-Dias, Bikram Pratim Bhuyan and Ravi Tomar
Information 2022, 13(6), 275; https://0-doi-org.brum.beds.ac.uk/10.3390/info13060275 - 26 May 2022
Cited by 34 | Viewed by 5882 | Correction
Abstract
There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to [...] Read more.
There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation. Full article
(This article belongs to the Special Issue Evolution of Smart Cities and Societies Using Emerging Technologies)
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12 pages, 2139 KiB  
Article
Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities
by Nemury Silega, Eliani Varén, Alfredo Varén, Yury I. Rogozov, Vyacheslav S. Lapshin and Skolupin A. Alekseevich
Information 2022, 13(1), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/info13010040 - 14 Jan 2022
Cited by 2 | Viewed by 2390
Abstract
The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy [...] Read more.
The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19. Full article
(This article belongs to the Special Issue Evolution of Smart Cities and Societies Using Emerging Technologies)
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