International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
www.irjet.net
Revolutionizing IoT Data Storage: Semantic AI-Enhanced DNA Storage Solutions Laxmiparbati Das Assistant Professor, Department of Computer Science and Engineering Gandhi Institute of Excellent Technocrats, Ghangapatna, Bhubaneswar -------------------------------------------------------------------------***-----------------------------------------------------------------------petabytes of data in just one gram, offering potential for Abstract
high-density storage far beyond current technological capabilities. However, DNA storage technologies face significant limitations, such as slow data retrieval times, high costs for synthesis and sequencing, and data integrity issues due to sequencing errors.: Detail the major storage challenges, including the data deluge in IoT, storage bottlenecks, security issues, and energy consumption concerns.
The exponential growth of the Internet of Things (IoT) has led to an unprecedented increase in data generation, necessitating novel approaches to data storage. Traditional digital storage solutions, such as hard drives and cloud storage, are reaching their physical and technical limits in handling this volume of data efficiently. This paper explores SemAI enhanced DNA data storage system, as a solution to address these challenges. By integrating SemAI with DNA storage technology, we propose a system capable of efficiently managing the massive, dynamic, and heterogeneous data produced by IoT devices. This paper discusses the design, architecture, benefits, and potential applications of SemAI-enhanced DNA storage in IoT systems. Additionally, it outlines the challenges currently faced by DNA storage and the role of AI in overcoming these obstacles.
In this paper, we propose the use of Semantic Artificial Intelligence (SemAI) to enhance DNA storage systems, improving data encoding, retrieval, and error correction processes. The integration of SemAI enables intelligent, context-aware management of data and provides the flexibility required to deal with the dynamic nature of IoT data. By leveraging the power of AI, we aim to overcome the current limitations of DNA storage and make it more viable for IoT applications. that combining SemAI with DNA storage can overcome the limitations of both IoT and DNA storage, creating a more efficient and scalable solution.
Keywords: Deoxyribonucleic Acid (DNA), Data Storage,
Semantic Artificial Intelligence (SemAI), Internet of Things (IoT), SemAI-Enhanced DNA Storage.
2. Background
Introduction
2.1 IoT Data Storage Challenges
The Internet of Things (IoT) is transforming industries by connecting billions of devices, generating vast amounts of data from sensors, actuators, cameras, and other intelligent devices. According to estimates, the world will generate around 79.4 zettabytes of data by 2025, much of which will come from IoT devices. Traditional data storage systems are struggling to keep up with the scale, speed, and complexity of data generated by IoT. Challenges such as limited storage capacity, energy consumption, retrieval speeds, and longterm data retention make it difficult to handle the diverse and constantly growing IoT data. Begin by elaborating on the rapid growth of IoT in the last decade. Include statistical data or projections (e.g., the number of IoT devices in 2025). Highlight the range of industries impacted by IoT, from healthcare to manufacturing and smart cities.
IoT devices generate highly varied and large-scale data, including sensor readings, device logs, video streams, and other types of multimedia content. The key challenges in storing and managing this data include: Massive Data Volume: With billions of devices continuously generating data, traditional storage systems are reaching their limits in terms of scalability. Cloudbased systems, while scalable, face issues related to latency, energy consumption, and infrastructure cost. Data Variety: IoT devices generate diverse types of data, such as structured sensor data (e.g., temperature readings), unstructured data (e.g., video streams), and semi-structured data (e.g., logs). These data types require storage systems that can handle both structured and unstructured data, with capabilities for efficient storage and retrieval.
DNA-based data storage presents a promising solution. DNA molecules have an exceptional capacity for storing vast amounts of information. DNA can store 215
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