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Sensors Journal - Special Issue "Sensors Application on Early Warning System"
The focus and scope of this Special Issue (SI) is on advances in sensor applications for Early Warning Systems (EWS).
EWS are a core type of information system used for a wide range of applications including environmental disaster risk management, command and control centres for emergency responses, intelligence analysis during criminal and terrorist events and prediction, and warning or infrastructure failures of critical infrastructure such as transport systems.
EWS help prevent loss of life and/or reduces the economic and material impact of events such as disasters or major infrastructure failures.
A complete EWS is distinct from other types of ICT monitoring systems in that it supports four main functions: (1) risk analysis of predefined hazards and vulnerabilities, (2) monitoring and warning by means of relevant parameters used for forecasts to generate accurate and timely warnings, (3) dissemination, sensemaking, and communication of risk information and warnings to those at risk, and (4) response capability built upon response plans that leverage local capabilities and preparation with regard to warnings.
This SI targets innovations that support any of these four EWS functions. It concerns new system algorithms, models, and designs to integrate these four functions based upon Internet of Things, web and semantic web, cyber physical systems, autonomous distributed systems, user-generated content and citizen science, etc.It includes new methods and designs for EWS processes including risk analysis and security, Web simulation, sensor data acquisition, user-generated content acting as a data feed, EWS data fusion, data analytics, and data science and data visualisation for decision support. Although early warning systems have been traditionally targeted to physical environment disaster applications, they are increasingly being deployed to detect and warn people about a wider range of events, such as vehicular collisions, train track failures, disease outbreaks, and security threats at critical sites such as airports. Such applications are also applicable to this SI.
Guest Editors
Dr. Stefan Poslad, Queen Mary University of London
Dr. Stuart E. Middleton, University of Southampton
Dr. Öcal Necmioğlu, Boğaziçi University
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.
Keywords
• Early warning system
• Internet of Things
• Cyber physical system
• Distributed system
• Risk management
• Environment sensing
• User generated content
• Citizen Science
Deadline for manuscript submissions: 15 September 2019
Sensors journal: Impact Factor: 2.475 (2017) ; 5-Year Impact Factor: 3.014 (2017)
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