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http://dx.doi.org/10.18419/opus-2810
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DC Element | Wert | Sprache |
---|---|---|
dc.contributor.author | Berg, Florian | de |
dc.date.accessioned | 2012-05-11 | de |
dc.date.accessioned | 2016-03-31T07:59:27Z | - |
dc.date.available | 2012-05-11 | de |
dc.date.available | 2016-03-31T07:59:27Z | - |
dc.date.issued | 2011 | de |
dc.identifier.other | 368507238 | de |
dc.identifier.uri | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-73683 | de |
dc.identifier.uri | http://elib.uni-stuttgart.de/handle/11682/2827 | - |
dc.identifier.uri | http://dx.doi.org/10.18419/opus-2810 | - |
dc.description.abstract | The recent improvements in smartphones nowadays offer a widespread application of sensor-based services. Each mobile phone is equipped with several sensors like a GPS module, a gyroscope, or a high-resolution camera. As a result of this sensor integration, a whole new way of usage is opened up for the end-user, like a location-based search or people-centric sensing. The main drawback related to a smartphone is an overall high energy consumption, combined with a limited energy capacity. Due to this fact, a continuous and fine grained sensing of the user's context is not possible, as it utilizes at least one acceleration sensor. Furthermore, the captured data is transmitted via a (mobile) communication infrastructure to post the context on the Internet. Both drain the battery very quickly. For that reason, an efficient energy-constrained distribution is required to minimize the update occurrence of a producer, while simultaneously maximizing the accuracy of a consumer. The primary issues to be addressed include a modeling of user behavior as well as a determination of optimal points in time for an update. Therefore, a probabilistic approach is used to forecast the user's context pattern. The prediction is based upon a Markov chain and enables the extraction of meaningful information. The proper times for an update are determined with the help of a constrained optimization problem. Different methods from mathematical optimization are applied like linear and nonlinear programming or a constrained Markov decision process, which obtain an update policy. For a better comparison of the weaknesses and strengths related to the developed methods, dynamic programming is used to achieve the optimal points in time for an update. The evaluation upon a real trace shows that an accuracy gain of more than 30% is achieved by sending the equal amount of messages. | en |
dc.language.iso | en | de |
dc.rights | info:eu-repo/semantics/openAccess | de |
dc.subject.ddc | 004 | de |
dc.title | Efficient energy-constrained distribution of context in mobile systems | en |
dc.type | masterThesis | de |
ubs.fakultaet | Fakultät Informatik, Elektrotechnik und Informationstechnik | de |
ubs.institut | Institut für Parallele und Verteilte Systeme | de |
ubs.opusid | 7368 | de |
ubs.publikation.typ | Abschlussarbeit (Diplom) | de |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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DIP_3207.pdf | 1,23 MB | Adobe PDF | Öffnen/Anzeigen |
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