1. Proceedings
  2. I3M
  3. 2019
  4. SESDE
  5. 10.46354/i3m.2019.sesde.005

Optimizing energy efficiency in distributed mHealth networks

  • a Philipp Skowron  , 
  • b Michael Aleithe  , 
  • c Bogdan Franczyk 
    • abLeipzig University, Germany
    • cWroclaw University of Economics, Poland
    Cite as
    Skowron P., Aleithe M., Franczyk B. (2019). Optimizing energy efficiency in distributed mHealth networks. Proceedings of the 7th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2019), pp. 30-36. DOI: https://doi.org/10.46354/i3m.2019.sesde.005

    Abstract

    Energy efficiency in mobile health applications is a relevant problem for long-term monitoring and user acceptance. Various parameters influence the runtime of the system to some degree. One of the parameters is the sampling rate of the individual distributed sensors. Increasing the sampling rate can lead to an increase in energy consumption within the system. By contrast, a reduction can lead to a loss of the data quality, which reduces the informative value of the results of algorithms that use this data. Using optimization methods from reinforcement learning and deep learning to adaptive adjust the sampling rates during runtime, energy efficiency could be improved in only 40 training runs without losing data quality during sampling.

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