Enhancing Compression Efficiency in Weather WSNs: A Frequency-Based LZW Approach for Reduced Data Transmission

Abstract

One of the most critical constraints in weather wireless sensor networks (WSNs) is the amount of transmitted data. To address this, a method is proposed to focus on compressing weather data (temperature, humidity, visibility, pressure, wind bearing, and wind speed) using the LZW compression algorithm. Our approach involves analyzing the frequency of sensor data every second to optimize compression efficiency. The results demonstrate that the proposed method achieves a significantly higher compression ratio (CR) compared to the traditional LZW method. For instance, the highest CR observed with our method was 0.26981 for temperature data, whereas the traditional method yielded lower CR values across all weather conditions. Additionally, we evaluated the size reduction ratio (SR). The traditional method achieved an SR of 0.99201 for visibility data, while the proposed method achieved a lower SR of 0.75898, indicating more efficient data reduction.