Abstract
The growing need for efficient data transmission and storage makes data compression in Internet of Things (IoT) devices an important field of study. Large volumes of data are produced by IoT devices, which must be sent over restricted network conditions and stored in memory locations that are limited. This study examines lossless data compression strategies designed for smart equipment and assesses how well they perform in terms of compression ratio and computational complexity. The study emphasises how crucial it is to choose the right compression algorithms depending on the unique needs of IoT systems including the demand for storage. Lossless compression is essential for critical applications where data integrity is vital since it guarantees that the original data can be properly reconstructed from the compressed data.
In this research, variables from a smart treadmill were used as a case study. The dataset was from cardiorespiratory measurements taken during a treadmill maximal graded test (GET) at the University of Malaga in Spain. The parameters used in this study were speed, heart rate, oxygen consumption, pulmonary ventilation, temperature and humidity. The data was compressed using delta, Huffman, delta-Huffman and double delta-Huffman compression techniques. These techniques were evaluated under two main metrics which are compression ratio and percentage space saving, in addition to other metrics of compression. According to the study's experimental findings, a good data compression approach was delta-Huffman. It yielded a compression ratio which was approximately four times better than that of the delta compression, twice that of Huffman compression and slightly higher than that of double delta-Huffman compression. The results provide useful guidance for creating effective frameworks for data compression that improve the overall functionality and long-term viability of IoT systems.
To sum up, this research offers a thorough methodology for data compression optimisation in IoT systems, guaranteeing effective storage without sacrificing data integrity. The knowledge gathered from this study is meant to direct the creation of more sophisticated, adaptive compression algorithms that can dynamically adapt to the many and changing requirements of IoT applications, therefore improving the functionality and longevity of these systems.