# SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things

arXiv: https://arxiv.org/abs/2409.12213 · HTML: https://mlsanity.com/paper/2409.12213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [Large AI Model-Based Semantic Communications](https://mlsanity.com/paper/2307.03492/) (2023)
- [Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis](https://mlsanity.com/paper/2205.04603/) (2022)
- [Semantic Communication with Adaptive Universal Transformer](https://mlsanity.com/paper/2108.09119/) (2021)
- [Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme](https://mlsanity.com/paper/2302.13580/) (2023)

## Abstract (arXiv)

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.