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Special Issue "Smart Cities of the Future: A Cyber Physical System Perspective"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Antonio Puliafito
Website
Guest Editor
Department of Engineering, University of Messina, 98122 Messina, Italy
Interests: cloud; IoT; smart cities; embedded systems; cyberphysical systems
Special Issues and Collections in MDPI journals
Prof. Dr. Symeon Papavassiliou
Website
Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, Athens, 15780, Greece
Interests: complex networks; wireless systems; ad hoc and sensor networks; software-defined radios and software-defined networks; online social networks; network modeling and optimization; network economics; cyber physical systems; Internet of Things; future internet research experimentation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) integrates networked sensors such as WSNs and, more in general, physical objects (i.e., things) in a ubiquitous cyberspace by interconnecting these systems to the Internet at large, making them also available over the Web. At the same time, the evolution and combination of 5G with IoT have pushed researchers and industries to be looking at the technological transformation to move towards an environment, where multiple devices will be able to connect, share information, interpret, and deliver a seamless experience for users.

The integration of the physical world with the cyber one is commonly refered to as a cyberphysical system (CPS), where cyber interactions, physical perceptions, and social connections are combined into a ubiquitous hyperspace that remarkably enriches and broadens the interactions and connections among human-to-human, human-to-object, and object-to-object. When also strenthening the focus of social interactions and humann in the loop considerations, cyberphsyical social systems (CPSS) are also emerging, as an extension of the broader class of CPSs.

Based on the significant development of high variety of rich-soured IoT sensing devices, cyberphysical–social sensing and computing technologies, together with some advanced networking and communications technologies, we can obtain an integrated set of data, information, and knowledge from the physical world, human society, as well as the virtual world.

A smart city represents an improvement of today’s cities—both functionally and structurally—that strategically utilizes many smart factors, such as Information and Communications Technology (ICT), to increase the city’s sustainable growth and strengthen city functions, while ensuring citizens’ quality of life and health. Cities can be viewed as a microcosm of “objects” with which citizens interact daily and represent an extremely interesting example of cyberphysical systems, where a continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure.

Each object in the City can be both the collector and distributor of information regarding mobility, energy consumption, air pollution, as well as potentially offering cultural and tourist information. As a consequence, cyber and real worlds are strongly linked in a smart city. New services can be deployed when needed and evaluation mechanisms are set up to assess the health and success of a smart city. In particular, the objectives of creating ICT-enabled smart city environments aim to: improve city services; optimize decision-making; create smart urban infrastructures; orchestrate cyber and physical resources; address challenging urban issues such as environmental pollution, transportation management, energy usage and public health; optimize use and benefit of next generation (5G and beyond) communication; capitalize on social networks and their analysis; support Tactile Internet applications; inspire urban citizens to improve their quality of life; etc.

However, large-scale deployment of cyberphysical–social systems will face a series of challenges and issues (e.g., energy efficiency requirements, architecture, protocol stack design, implementation, security), which requires more smart sensing and computing methods, advanced networking, and communications technologies to provide more pervasive cyberphysical–social services for people.

This Special Issue is soliciting conceptual, theoretical, and experimental contributions, discussing and treating challenges, state-of-the-art, and solutions to a set of currently unresolved key questions related to CPSs and smart cities. The authors from both academia and industry are welcome to contribute and demonstrate the latest research results with the design, implementation, deployment, operation, and evaluation of smart sensing and computing models, networking methodologies, and communications tools and platforms for CPS, as well as describe and present relevant services and applications.

Topics of interest include but are not limited to the following:

  • Multifunctional IoT sensing devices
  • Networked smart cyberphysical–social sensing system and platform
  • Modeling of CPS
  • Energy-efficient cyberphysical–social sensing architectures
  • Green computing and sustainable computing for IoT and CPS
  • Cloud computing, fog computing, and edge computing
  • Routing protocols, data dissemination, and offloading algorithms
  • Community detection and network evolution analysis for CPS
  • Localization and node mobility models
  • Construction technology of dynamics of social groups
  • Methods for data collection, convergence, and storage
  • Schemes of data mining, processing, and analysis
  • Techniques of data visualization
  • Quality of experience and quality of service in CPS
  • Social network analysis and social influence analysis
  • Crowdsourcing, crowdsensing, participatory sensing
  • Low-power, distributed data processing in sensor applications
  • Smart worlds, smart cities, and smart healthcare
  • Security, privacy, and trust for the IoT
  • Energy-harvesting communications and networks
  • Machine learning/deep learning/artificial intelligent approaches
  • Applications and testbeds of CPS

Both review articles and original research papers relating to sensors and smart cities are solicited. There is particular interest in papers with advances towards practical experiences and services overcoming the adoption barriers for sensors and smart cities.

Prof. Dr. Antonio Puliafito
Prof. Dr. Symeon Papavassiliou
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

  • cloud
  • IoT
  • smart cities
  • embedded systems
  • wireless systems
  • cyberphysical systems
  • online social networks
  • software-defined networks
  • network modeling and optimization
  • network economics
  • data management
  • Artificial Intelligence

Published Papers (1 paper)

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Research

Open AccessArticle
Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique
Sensors 2020, 20(20), 5845; https://doi.org/10.3390/s20205845 - 16 Oct 2020
Abstract
In recent years, advanced threats against Cyber–Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To [...] Read more.
In recent years, advanced threats against Cyber–Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier. Full article
(This article belongs to the Special Issue Smart Cities of the Future: A Cyber Physical System Perspective)
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