Springer Nature,


DOI:10.1007/978-3-319-77525-8_191, Dimensions: pub.1112608791,



  1. (1) Chalmers University of Technology, grid.5371.0
  2. (2) Mälardalen University, grid.411579.f
  3. (3) Nanyang Technological University, grid.59025.3b
  4. (4) National University of Singapore, grid.4280.e
  5. (5) Ampool, Inc., Santa Clara, CA, USA
  6. (6) Hewlett Packard Enterprise (United States), grid.474602.3
  7. (7) University of Würzburg, grid.8379.5
  8. (8) CNR-ICAR, Rende, Italy
  9. (9) University of California, Santa Barbara, grid.133342.4
  10. (10) Aalborg University, grid.5117.2, AAU
  11. (11) Vienna University of Economics and Business, grid.15788.33
  12. (12) Birla Institute of Technology and Science, Pilani, grid.418391.6
  13. (13) Conservatoire National des Arts et Métiers, grid.36823.3c
  14. (14) IDEMIA Colombes, Colombes, France


Data generation has increased drastically over the past few years. Processing large amounts of data requires huge compute and storage infrastructures, which consume substantial amounts of energy. Moreover, another important aspect to consider is that more and more the data is analyzed on-board battery operated mobile devices like smart-phones and sensors. Therefore, data processing techniques are required to operate while meeting resource constraints such as memory and power to prolong a mobile device network’s lifetime. This chapter reviews representative methods used for energy efficient Big Data analysis, providing first a generic overview of the issue of energy conservation and then presenting a more detailed analysis of the issue of energy efficiency in mobile and sensor networks.

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