Density-aware compressive crowdsensing
- Submitting institution
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University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9919
- Type
- E - Conference contribution
- DOI
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10.1145/3055031.3055081
- Title of conference / published proceedings
- Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks
- First page
- 29
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- April
- Year of publication
- 2017
- 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
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4
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper won the Best Paper award at IPSN'17. It contains a theoretical result that shows it is possible for the i.d.d assumption of compressed sensing to be relaxed under certain applications of extreme practical importance (e.g., traffic monitoring within a city). This result enabled the design of new distributed sensing systems that can successfully recover real-world signals (traffic density, air quality, noise levels) even if sampling only at periods when the sensor energy cost is minimized as opposed to the traditional uniform sampling requirement for high fidelity recovery.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -