HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Adaptive Near Sensor Compressing for Energy Savings in Wireless Body Area Sensor Networks

Abstract : Wireless body area networks are essentially constrained by the energy required to send data from one node to another. While most of times the whole data is sent, data compression can be used to decrease the amount of data and therefore the energy consumption. However, the compression is energy consuming and its efficiency depends on data size and data type. To tackle this challenge, the proposed Adaptive Near Sensor Compressing (ANSC) algorithm performs pertinent feature extraction on the sensed data, and selects the best compressor by estimating the energy required by the hardware platform to perform both data compression and transmission. The transmission power is also dynamically tuned to reach a specific quality of service according to node location and propagation channel. ANSC has been evaluated on a real platform dedicated to wireless body area network and has shown an energy gain of up to 49% compared to full transmission.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

Contributor : Matthieu Gautier Connect in order to contact the contributor
Submitted on : Wednesday, April 8, 2020 - 10:26:02 AM
Last modification on : Monday, April 4, 2022 - 9:28:31 AM


Files produced by the author(s)


  • HAL Id : hal-02536433, version 1


Corentin Lavaud, Antoine Courtay, Matthieu Gautier, Olivier Berder. Adaptive Near Sensor Compressing for Energy Savings in Wireless Body Area Sensor Networks. Workshop OBSN, ACM International Conference on Embedded Wireless Systems and Networks (EWSN), Feb 2020, Lyon, France. ⟨hal-02536433⟩



Record views


Files downloads