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Energy-Accuracy Trade-offs of Sensor Sampling in Smart Phone Based Sensing Systems

  • Kiran K. RachuriEmail author
  • Cecilia Mascolo
  • Mirco Musolesi
Chapter

Abstract

A large number of context-inference applications run on off-the-shelf smart phones and infer context from the data acquired by sensing from the sensors embedded in these devices. The use of efficient and effective sampling techniques is of key importance for these applications. Aggressive sampling can ensure a more fine-grained and accurate reconstruction of context information but, at the same time, continuous querying of sensor data might lead to rapid battery depletion. In this chapter, we present a design methodology to evaluate energy-accuracy trade-offs for querying sensor data in continuous sensing mobile systems, and an adaptive sensor sampling methodology that relies on dynamic selection of sampling functions depending on history of context events. We also report on the experimental evaluation of a set of functions that control the rate at which the data are sensed from the accelerometer, Bluetooth, and microphone sensors, and we show that a dynamic adaptation mechanism provides a better energy-accuracy trade-offs compared to simpler function based rate control methods. Furthermore, we show that the suitability of these mechanisms varies for each of the sensors.

Keywords

Energy-accuracy Trade-off Microphone Sensor Dynamic Adaptation Method Querying Sensor Data Bluetooth Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported through the Gates Cambridge Trust at the University of Cambridge, and the EPSRC grants EP/C544773, EP/F033176, and EP/D077273.

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Kiran K. Rachuri
    • 1
    Email author
  • Cecilia Mascolo
    • 1
  • Mirco Musolesi
    • 2
  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.School of Computer ScienceUniversity of BirminghamBirminghamUK

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