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008			220601s2014 sz | o |||| 0|eng d
020			_a9783031794537
024	7		_a10.1007/978-3-031-79453-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.59 _b2014 EB
100	1		_aYan, Zhixian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688317
245	1	0	_aSemantics in Mobile Sensing _cby Zhixian Yan, Dipanjan Chakraborty
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XI, 131 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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technologies has ushered in a new era of mobile sensing, where traditional wearable on-body sensors are being rapidly superseded by various embedded sensors in our smartphones.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.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9476230 _aInformática móvil
700	1		_aChakraborty, Dipanjan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688318
776	0	8	_iPrinted edition: _z9783031794520
776	0	8	_iPrinted edition: _z9783031794544
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79453-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eb _zSI