000 | 03519nam a22005535i 4500 | ||
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001 | 978-981-13-7776-1 | ||
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_a9789811377761 _9978-981-13-7776-1 |
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024 | 7 |
_a10.1007/978-981-13-7776-1 _2doi |
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_a004.167 _223 |
100 | 1 |
_aKong, Linghe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aWhen Compressive Sensing Meets Mobile Crowdsensing _h[electronic resource] / _cby Linghe Kong, Bowen Wang, Guihai Chen. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXII, 127 p. 39 illus., 35 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aIntroduction -- Mathematical Theory of Compressive Sensing -- Basic Compressive Sensing for Data Reconstruction -- Bayesian Compressive Sensing for Task Allocation -- Adaptive Compressive Sensing for Incentive Mechanism -- Encoded Compressive Sensing for Privacy Preservation -- Iterative Compressive Sensing for Fault Detection -- Conclusion. | |
520 | _aThis book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don’t wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution. . | ||
650 | 0 | _aMobile computing. | |
650 | 0 | _aComputer networks . | |
650 | 0 | _aData structures (Computer science). | |
650 | 0 | _aInformation theory. | |
650 | 1 | 4 | _aMobile Computing. |
650 | 2 | 4 | _aComputer Communication Networks. |
650 | 2 | 4 | _aData Structures and Information Theory. |
700 | 1 |
_aWang, Bowen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aChen, Guihai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811377754 |
776 | 0 | 8 |
_iPrinted edition: _z9789811377778 |
776 | 0 | 8 |
_iPrinted edition: _z9789811377785 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-13-7776-1 |
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912 | _aZDB-2-SXCS | ||
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999 |
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