GROWN: Local Data Compression in Real-Time To Support Energy Efficiency in WBAN

TL;DR

GROWN is a hybrid local data compression method for WBANs that reduces data redundancy and energy consumption, thereby extending device battery life in real-time health monitoring systems.

Contribution

It introduces a novel hybrid compression approach tailored for WBANs, combining techniques from wireless sensor networks to optimize energy efficiency.

Findings

Energy consumption decreased significantly during experiments.

Network lifetime was extended due to reduced data transmission.

The approach effectively reduces data redundancy in real-time monitoring.

Abstract

The evolution of wireless technologies has enabled the creation of networks for several purposes as health care monitoring. The Wireless Body Area Networks (WBANs) enable continuous and real-time monitoring of physiological signals, but that monitoring leads to an excessive data transmission usage, and drastically affects the power consumption of the devices. Although there are approaches for reducing energy consumption, many of them do not consider information redundancy to reduce the power consumption. This paper proposes a hybrid approach of local data compression, called GROWN, to decrease information redundancy during data transmission and reduce the energy consumption. Our approach combines local data compression methods found in WSN. We have evaluated GROWN by experimentation, and the results show a decrease in energy consumption of the devices and an increase in network lifetime.