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Special Issue "Selected Papers from ISC2 2018"

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

Deadline for manuscript submissions: closed (15 December 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Baek-Young Choi

School of Computing and Engineering, University of Missouri – Kansas City, 550D Flarsheim Hall, 5110 Rockhill Road, Kansas City, MO 64110, USA
Website | E-Mail
Interests: cloud computing and software-defined networks; network algorithms and protocols; data storage and management systems; network traffic/performance/security: measurement, analysis and modeling; smart device technologies, Internet of Things
Guest Editor
Prof. Dr. Victor M. Larios

University of Guadalajara, Módulo L-305. Los Belenes, Zapopan 45100, Jalisco, México
Website | E-Mail
Interests: smart cities; distributed systems; data visualization
Guest Editor
Prof. Dr. Andrea Molinari

University of Trento, Via Inama, 5-38122 Trento, Italy
Website | E-Mail
Interests: e-learning; information systems; semantic technologies; software project management
Guest Editor
Prof. Dr. Xiao-Jun Wu

Institute of Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu, China
Website | E-Mail
Interests: pattern recognition; computer vision; fuzzy systems; neural networks; intelligent systems

Special Issue Information

Dear Colleagues,

The Fourth IEEE Annual International Smart Cities Conference (ISC2 2018) will be held in Kansas City, Missouri, USA, 16–19 September, 2018. The IEEE ISC2 will contribute to further develop technical best practices across broad application and functional domains within the context of urban infrastructure systems, and disseminate them to the broad stakeholders.

We invite ISC authors to contribute extended research articles related to sensors, as well as reviews, to this Special Issue. The journal submission must contain new results of substantive research significance and impact beyond the conference version. Sensors is an international peer-reviewed journal. The submitted manuscripts will be reviewed again and the acceptance will be decided by review reports and academic editor's decision.

Potential topics include, but are not limited to:

  1. Community and Governance:
    - Healthcare
    - Smart environment and ecosystems
    - Mobility and transportation
    - Digital city and smart growth
    - Energy efficiency
  2. Infrastructure and Technology:
    - Smart grids for smart cities
    - Smart buildings
    - Transportation and traffic systems
    - Smart city theory, modeling, and simulation
    - Vehicle-to-infrastructure integration
    - Intelligent infrastructure
    - Sensors and Intelligent Electronic Devices
    - Systems Integration
    - Management and Control Platforms
    - Energy systems
  3. Data, Privacy and Security:
    - Computational intelligence
    - Open data and big data analytics
    - Internet of Things (IoT)
    - Pattern recognition
    - Cybersecurity
    - Networks and communications
    - Security and regulations
    - Data collection
    - Data security and privacy
    - Data visualization
    - Intelligence and Data Analytics

* Important dates:

(Extended version) submission deadline: 30 November 2018
Decision notification: 15 January 2018
Final manuscript submission: 30 January 2018
Publication: early 2019

Prof. Dr. Baek-Young Choi
Prof. Dr. Victor M. Larios
Prof. Dr. Andrea Molinari
Prof. Dr. Xiao-Jun Wu
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 1800 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 (3 papers)

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Research

Open AccessArticle ICE-MoCha: Intelligent Crowd Engineering using Mobility Characterization and Analytics
Sensors 2019, 19(5), 1025; https://doi.org/10.3390/s19051025
Received: 30 November 2018 / Revised: 25 February 2019 / Accepted: 25 February 2019 / Published: 28 February 2019
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Abstract
Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. [...] Read more.
Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. Although many smart video surveillance tools, inspired by the recent advanced artificial intelligence (AI) technology and machine learning (ML) algorithms, enable object detection and identification, it is still challenging to predict the crowd mobility in real-time for preventing potential disasters. In this paper, we propose an intelligent crowd engineering platform using mobility characterization and analytics named ICE-MoCha. ICE-MoCha is to assist safety management for mobile crowd events by predicting and thus helping to prevent potential disasters through real-time radio frequency (RF) data characterization and analysis. The existing video surveillance based approaches lack scalability thus have limitations in its capability for wide open areas of crowd events. Via effectively integrating RF signal analysis, our approach can enhance safety management for mobile crowd. We particularly tackle the problems of identification, speed, and direction detection for the mobile group, among various crowd mobility characteristics. We then apply those group semantics to track the crowd status and predict any potential accidents and disasters. Taking the advantages of power-efficiency, cost-effectiveness, and ubiquitous availability, we specifically use and analyze a Bluetooth low energy (BLE) signal. We have conducted experiments of ICE-MoCha in a real crowd event as well as controlled indoor and outdoor lab environments. The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents. Full article
(This article belongs to the Special Issue Selected Papers from ISC2 2018)
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Open AccessArticle Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities
Sensors 2019, 19(3), 562; https://doi.org/10.3390/s19030562
Received: 28 November 2018 / Revised: 10 January 2019 / Accepted: 24 January 2019 / Published: 29 January 2019
PDF Full-text (4780 KB) | HTML Full-text | XML Full-text
Abstract
Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and [...] Read more.
Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and is stored in databases and even simple files. To make well-informed decisions, it is essential to have a proper data integration strategy, which must allow working with heterogeneous data sources and platforms in interoperable ways. In this paper, we present a new lightweight web service called InterSensor Service allowing to simply connect to multiple IoT platforms, simulation specific data, databases, and simple files and retrieving their observations without worrying about data storage and the multitude of different APIs. The service encodes these observations “on-the-fly” according to the standardized external interfaces such as the OGC Sensor Observation Service and OGC SensorThings API. In this way, the heterogeneous observations can be analyzed and visualized in a unified way. The service can be deployed not only by the users to connect to different sources but also by providers and stakeholders to simply add further interfaces to their platforms realizing interoperability according to international standards. We have developed a Java-based implementation of the InterSensor Service, which is being offered free as open source software. The service is already being used in smart city projects and one application for the district Queen Elizabeth Olympic Park in London is shown in this paper. Full article
(This article belongs to the Special Issue Selected Papers from ISC2 2018)
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Open AccessArticle Multi-Type Sensor Placements in Gaussian Spatial Fields for Environmental Monitoring
Sensors 2019, 19(1), 189; https://doi.org/10.3390/s19010189
Received: 30 November 2018 / Revised: 27 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
PDF Full-text (1455 KB) | HTML Full-text | XML Full-text
Abstract
As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to [...] Read more.
As citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to the limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple types of environmental characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluated the proposed approach via an application in air quality monitoring scenario in Hong Kong and experimental results demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Selected Papers from ISC2 2018)
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