SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things
This addresses data storage scalability for IoT applications, representing an incremental improvement in DNA storage technology.
The paper tackles the challenge of DNA storage for IoT data by introducing a Semantic AI-enhanced DNA storage paradigm, achieving a 2.61 dB PSNR gain and 0.13 SSIM improvement over conventional deep learning-based methods.
In the wake of the swift evolution of technologies such as the Internet of Things (IoT), the global data landscape undergoes an exponential surge, propelling DNA storage into the spotlight as a prospective medium for contemporary cloud storage applications. This paper introduces a Semantic Artificial Intelligence-enhanced DNA storage (SemAI-DNA) paradigm, distinguishing itself from prevalent deep learning-based methodologies through two key modifications: 1) embedding a semantic extraction module at the encoding terminus, facilitating the meticulous encoding and storage of nuanced semantic information; 2) conceiving a forethoughtful multi-reads filtering model at the decoding terminus, leveraging the inherent multi-copy propensity of DNA molecules to bolster system fault tolerance, coupled with a strategically optimized decoder's architectural framework. Numerical results demonstrate the SemAI-DNA's efficacy, attaining 2.61 dB Peak Signal-to-Noise Ratio (PSNR) gain and 0.13 improvement in Structural Similarity Index (SSIM) over conventional deep learning-based approaches.