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020 _a9783031794537
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024 7 _a10.1007/978-3-031-79453-7
_2doi
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082 0 4 _a510
_223
100 1 _aYan, Zhixian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983535
245 1 0 _aSemantics in Mobile Sensing
_h[electronic resource] /
_cby Zhixian Yan, Dipanjan Chakraborty.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXI, 131 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aSynthesis Lectures on Data, Semantics, and Knowledge,
_x2691-2031
505 0 _aAcknowledgments -- Introduction -- Semantic Trajectories from Positioning Sensors -- Semantic Activities from Motion Sensors -- Energy-Efficient Computation of Semantics from Sensors -- Conclusion -- Bibliography -- Authors' Biographies .
520 _aThe dramatic progress of smartphone technologies has ushered in a new era of mobile sensing, where traditional wearable on-body sensors are being rapidly superseded by various embedded sensors in our smartphones. For example, a typical smartphone today, has at the very least a GPS, WiFi, Bluetooth, triaxial accelerometer, and gyroscope. Alongside, new accessories are emerging such as proximity, magnetometer, barometer, temperature, and pressure sensors. Even the default microphone can act as an acoustic sensor to track noise exposure for example. These sensors act as a ""lens"" to understand the user's context along different dimensions. Data can be passively collected from these sensors without interrupting the user. As a result, this new era of mobile sensing has fueled significant interest in understanding what can be extracted from such sensor data both instantaneously as well as considering volumes of time series from these sensors. For example, GPS logs can be used to determine automatically the significant places associated to a user's life (e.g., home, office, shopping areas). The logs may also reveal travel patterns, and how a user moves from one place to another (e.g., driving or using public transport). These may be used to proactively inform the user about delays, relevant promotions from shops, in his ""regular"" route. Similarly, accelerometer logs can be used to measure a user's average walking speed, compute step counts, gait identification, and estimate calories burnt per day. The key objective is to provide better services to end users. The objective of this book is to inform the reader of the methodologies and techniques for extracting meaningful information (called ""semantics"") from sensors on our smartphones. These techniques form the cornerstone of several application areas utilizing smartphone sensor data. We discuss technical challenges and algorithmic solutions for modeling and mining knowledge from smartphone-resident sensor data streams. This book devotes two chapters to dive deep into a set of highly available, commoditized sensors---the positioning sensor (GPS) and motion sensor (accelerometer). Furthermore, this book has a chapter devoted to energy-efficient computation of semantics, as battery life is a major concern on user experience.
650 0 _aMathematics.
_911584
650 0 _aInternet programming.
_935503
650 0 _aApplication software.
_983536
650 0 _aComputer networks .
_931572
650 0 _aOntology.
_95277
650 1 4 _aMathematics.
_911584
650 2 4 _aWeb Development.
_935505
650 2 4 _aComputer and Information Systems Applications.
_983540
650 2 4 _aComputer Communication Networks.
_983541
650 2 4 _aOntology.
_95277
700 1 _aChakraborty, Dipanjan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983542
710 2 _aSpringerLink (Online service)
_983545
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031794520
776 0 8 _iPrinted edition:
_z9783031794544
830 0 _aSynthesis Lectures on Data, Semantics, and Knowledge,
_x2691-2031
_983546
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79453-7
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