Chameleon : Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 32130336
- Type
- D - Journal article
- DOI
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10.1109/tvt.2019.2924911
- Title of journal
- IEEE Transactions on Vehicular Technology
- Article number
- -
- First page
- 9038
- Volume
- 68
- Issue
- 9
- ISSN
- 0018-9545
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- 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)
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F - Cyber Security and Network Systems (CyberNets)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research proposes Chameleon, a novel computational task offloading solution applicable in visual-based assisted driving scenarios. It is a latency and resolution-aware solution which takes into account the spatio-temporal variation of service demand and supply in a vehicular network. The results of this work have been verified via measurements with visual transmission and processing time and on a real-world vehicular dataset from the city of Helsinki in Finland. Supported by Academy of Finland, this research has further led to a presentation talk in Mobile Networking, Analytics and Edge Intelligence, a Joint Finland-China-Germany workshop held in China in August 2019 (http://fi.ee.tsinghua.edu.cn/~fcg2019/index.html).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -