Sustainable Wireless Sensor Networks: Leveraging Data Reduction to Enhance Performance and Longevity

Wireless sensor networks are vital technologies in environmental monitoring systems, but they face challenges related to energy consumption and data transmission efficiency, especially when dealing with huge amounts of constantly changing weather data. This research aims to ‎improve the efficiency of wireless sensor networks and performance by proposing a hybrid approach to data reduction through redundancy ‎detection and data compression using the LZW algorithm, which is a lossless compression algorithm used to reduce data size while maintaining ‎data accuracy. The proposed model consists of two main stages: 1) Duplicate detection, in which duplicate or temporally close values are ‎removed to reduce the size of the transmitted data, and 2) compressing non-duplicate data using the LZW algorithm to reduce energy consumption. The proposed model was tested on simulated weather data including temperature, humidity, visibility, pressure, ‎wind speed, and wind bearing. The results showed that the pressure sensor achieved the highest throughput (0.02 kb/s) in the traditional ‎method due to its large volume (9000bytes) of data, when applying the proposed method, the pressure sensor continued to achieve the high-‎est throughput after compression, while the vision sensor recorded the lowest energy consumption, as it decreased from (0.0042 J) in the ‎traditional method to (0.0000084 J) using the proposed method. The results confirm the efficiency of the network and the extension of the ‎network’s life in environmental monitoring through the effectiveness of combining redundancy removal and data compression.