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  • 1.
    Dufbäck, Dennis
    et al.
    Linköping University, Department of Computer and Information Science.
    Håkansson, Fredrik
    Linköping University, Department of Computer and Information Science.
    Adapting network interactions of a rescue service mobile application for improved battery life2017Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesis
    Abstract [en]

    Today, it is not unusual that smartphone devices can’t survive even one day of regular use until the battery needs to be recharged. The batteries are drained while using power hungry applications made by developers who haven’t taken their application’s energy impact into consideration. In this thesis we study network transmissions as made by a mobile application, and the impact these have on the battery life. The application was developed with the local rescue and emergency service as a hypothetical target group. We test how the mobile network technologies 3G and WiFi together with the device’s current signal strength and battery level affect the energy usage of the battery when uploading data to a server. We develop an adaptation mechanism on application level which uses a mathematical model for calculating a suitable adaptation of scheduling of network interactions. The adaptation mechanism makes use of burst buffering of packets, and adjusts for 3G tail times as well as for different priorities of incoming requests. Custom packet scheduling profiles are made to make consistent measurements, and with this implementation we are able to reduce the amount of energy consumed using 3G and WiFi with 67 % and 39 % respectively during tests.

  • 2.
    Håkansson, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science.
    Larsson, Carl-Johan
    Linköping University, Department of Computer and Information Science.
    User-Based Predictive Caching of Streaming Media2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Streaming media is a growing market all over the world which sets a strict requirement on mobile connectivity. The foundation for a good user experience when supplying a streaming media service on a mobile device is to ensure that the user can access the requested content. Due to the varying availability of mobile connectivity measures has to be taken to remove as much dependency as possible on the quality of the connection. This thesis investigates the use of a Long Short-Term Memory machine learning model for predicting a future geographical location for a mobile device. The predicted location in combination with information about cellular connectivity in the geographical area is used to schedule prefetching of media content in order to improve user experience and to reduce mobile data usage. The Long Short-Term Memory model suggested in this thesis achieves an accuracy of 85.15% averaged over 20000 routes and the predictive caching managed to retain user experience while decreasing the amount of data consumed.

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Citation style
  • apa
  • ieee
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  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
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