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Mobile Context Awareness pp 97–113Cite as

The Case for Context-Aware Resources Management in Mobile Operating Systems

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Abstract

Efficient management of mobile resources from an energy perspective in modern smart-phones is paramount nowadays. Today’s mobile phones are equipped with a wide range of sensing, computational, storage and communication resources. The diverse range of sensors such as microphones, cameras, accelerometers, gyroscopes, GPS, digital compass and proximity sensors allow mobile apps to be context-aware whereas the ability to have connectivity almost everywhere has bootstrapped the birth of rich and interactive mobile applications and the integration of cloud services. However, the intense use of those resources can easily be translated into power-hungry applications. The way users interact with their mobile handsets and the availability of mobile resources is context dependent. Consequently, understanding how users interact with their applications and integrating context-aware resources management techniques in the core features of a mobile operating system can provide benefits such as energy savings and usability. This chapter describes how context drives the way users interact with their handsets and how it determines the availability and state of hardware resources in order to explain different context-aware resources management systems and the different attempts to incorporate this feature in mobile operating systems.

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Notes

  1. 1.

    Battery discharging rate might arguably not be the best indicator to measure energy consumption in mobile handsets. This signal is very noisy since it depends on hardware and users’ habits and requires complex methods to be properly calibrated [12].

  2. 2.

    If the GPS chip has not been used in a long time, then the Time To First Fix (TTFF) can be longer because it needs to download the satellites ephemeris and almanac before it can make the calculations. Usually, the GPS-receiver also needs 4 satellites to accurately fix its location. This is usually referred to as cold start. In cases when the chip was recently used (in the order of minutes or even few hours), the time to fix would be even faster (i.e. warm start and hot start phases).

  3. 3.

    The energy consumption becomes even more significant if multiple applications are requesting location reads independently. Zhuang et al. [33] are the only ones who applie this technique. Android OS Location Providers follow a similar philosophy [34].

  4. 4.

    The system only supports pedestrians as possible movement model and uses accelerometer to infer users’ mobility.

  5. 5.

    ErdOS is conceived as an Android OS extension.

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Authors and Affiliations

  1. Computer Lab, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK

    Narseo Vallina-Rodriguez & Jon Crowcroft

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  1. Narseo Vallina-Rodriguez
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  2. Jon Crowcroft
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Correspondence to Narseo Vallina-Rodriguez .

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Editors and Affiliations

  1. Department of Computer Science, University of Bath / Vodafone Group R&D, Claverton Down, Bath, BA2 7AY, Bath&NthEstSomerset, United Kingdom

    Tom Lovett

  2. Department of Computer Science, University of Bath, Claverton Down, Bath, BA2 7AY, Bath&NthEstSomerset, United Kingdom

    Eamonn O'Neill

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Vallina-Rodriguez, N., Crowcroft, J. (2012). The Case for Context-Aware Resources Management in Mobile Operating Systems. In: Lovett, T., O'Neill, E. (eds) Mobile Context Awareness. Springer, London. https://doi.org/10.1007/978-0-85729-625-2_6

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  • DOI: https://doi.org/10.1007/978-0-85729-625-2_6

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