<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k &lt; 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.</p>
</abstract>

In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k < 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.

Label big data compression in Internet of things based on piecewise linear regression

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k  178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.

In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely...

Label big data compression in Internet of things based on piecewise linear regression

Pubblicato online: 15 lug 2022

Pagine: 1477 - 1486

Ricevuto: 14 mar 2022

Accettato: 27 mag 2022

DOI: https://doi.org/10.2478/amns.2022.2.0136

Parole chiave
Wireless sensor networks, Data compression, Energy efficiency evaluation, Source program energy consumption optimization

© 2023 Ming Su et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Label big data compression in Internet of things based on piecewise linear regression

Pubblicato online: 15 lug 2022

Pagine: 1477 - 1486

Ricevuto: 14 mar 2022

Accettato: 27 mag 2022

DOI: https://doi.org/10.2478/amns.2022.2.0136

Parole chiaveWireless sensor networks, Data compression, Energy efficiency evaluation, Source program energy consumption optimization

© 2023 Ming Su et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Wireless sensor networks, Data compression, Energy efficiency evaluation, Source program energy consumption optimization