A machine learning approach for user localization exploiting connectivity data
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 340
- Type
- D - Journal article
- DOI
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10.1016/j.engappai.2015.12.015
- Title of journal
- Engineering Applications of Artificial Intelligence
- Article number
- -
- First page
- 125
- Volume
- 50
- Issue
- -
- ISSN
- 0952-1976
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
https://www.sciencedirect.com/science/article/pii/S0952197616000063
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Part-funded by Italian Ministry of Education, University and Research, ref. (PAC02L10086.), the findings of this research at DICGIM, University of Palermo were applied by Intelener, a Start-up company funded by the same project, led by the Italtel Collaborative Research team (Resp. giacomo.corvisieri@italtel.com). Intelener adapted the localisation algorithm for an application of automated adjustment of the levels of light intensity emitted by street lighting systems, and produced a prototype light pole (https://www.intelener.com/rail/). Ortolani designed and developed the hybrid WSN simulator used for the experiments and supervised the design of the localisation algorithm.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -