A Semantic IoT Early Warning System for Natural Environment Crisis Management
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 411
- Type
- D - Journal article
- DOI
-
10.1109/TETC.2015.2432742
- Title of journal
- IEEE Transactions on Emerging Topics in Computing
- Article number
- -
- First page
- 246
- Volume
- 3
- Issue
- 2
- ISSN
- 2168-6750
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a major result of the core research I led as Systems & architecture Work package leader of the EU FP7 Collaborative, Complex and Critical Decision-Support in Evolving Crises (TRIDEC) project (grant no. 258723) that resulted in the project winning the Institute of Risk Management IRM's inaugural Global Risk Award 2013 for the category Managing risk across boundaries (https://www.theirm.org/). It has resulting in a special issue of the high ranked Sensors Journal on early warning system (EWS) as editor for 2020 (see https://www.mdpi.com/journal/sensors/special_issues/EWS).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -