(untitled) (Informatica, 2023)
WSNs are used in different applications and the enormous volume of data they collect and broadcast across the network overburdens the sensor nodes and this issue can be mitigated by compressing the data before transmitting it over the network. Singular Value Decomposition, a state-of-the-art non-transform based compression method, primarily for dimensionality reduction in any type of data, is utilized in this study. In this, the difference between the adjacent pixel values is computed as a preprocessing step, and then compressed, which is represented by three singular matrices: two orthonormal matrices (X, Y), and one diagonal matrix called rank matrix. The resultant data is then applied through a Run Length Encoding step and transmitted. By compressing the image with different thresholds, the rank value is altered and since the pixel differences are only encoded and in terms of a relatively small number of bits, the outcome is represented with a compression ratio of approximately 12% and also the reconstructed image at the receiver exhibits good Peak Signal to Noise Ratio (PSNR). The use of this strategy in WSNs is also justified by analyzing the amount of energy savings and the nodes’ energy usage using standard energy models and the percentage of energy saving varies from 25% to 53 % with the decrease in the rank values respectively.