Adaptative sampling for data compression in wireless sensor

With the advent of the digital age, data storage continues to grow rapidly, especially with the development of internet data centers. The environmental impact of this technological revolution has become a problem. As the cost of digital recordings decreases, the amount of unnecessary data stored increases. We have developed a new algorithm for compressing digital data series, which uses a local measure of relevance based on statistical characteristics. This compression produces non-uniform sampling with a density dependent on the relevance of the data, hence the adaptive feature of the algorithm. It works without any additional input and allows to build a data tree with progressive compression. Such a structure can feed multiscale analysis tools as well as selective memory release solutions for efficient archive management. Tests were carried out on two ideal noise-free signals as well as two real-world applications, namely compression of electrocardiograms retrieved from the PhysioNet database and compression of remote measurements provided by the constellation of ESA’s Swarm satellites. Non-sparse type signals have been chosen in order to investigate compression performances in unfavorable conditions. Despite this, the number of samples has been reduced by more than half while maintaining the relevant characteristics of the signals. We compared the Fourier transforms of the original and reconstructed signals and, by reconstructing uniform samplings of the ideal noise-free signals, we concluded that the relevant information is preserved regardless of the application domain. The next step is hardware development: the goal of this project is to integrate this universal compression algorithm into the body of the next generation of wireless sensors.