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.
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Information & Contributors
Information
Published In
February 2020
294 pages
ISBN:9780994988645
- General Chairs:
- Fabrice Valois,
- Christine Julien,
- Program Chairs:
- Amy L. Murphy,
- Omprakash Gnawali
Sponsors
- EWSN: International Conference on Embedded Wireless Systems and Networks
In-Cooperation
Publisher
Junction Publishing
United States
Publication History
Published: 29 May 2020
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EWSN '20
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- EWSN
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Overall Acceptance Rate 81 of 195 submissions, 42%
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