AI-Driven Storage Optimization in Embedded Systems: Techniques, Models, and Real-World Applications - Selvakumar Venkatasubbu; Subhan Baba Mohammed; Monish Katari (Journal of Science & Technology, 2023)
The ever-growing complexity of embedded systems necessitates efficient storage utilization due to their inherent limitations in processing power and memory capacity. Traditional storage management techniques often prove inadequate in handling the increasing volume and diversity of data generated by these systems. This paper delves into the burgeoning field of AI-driven storage optimization for embedded systems, exploring its potential to revolutionize how data is stored, accessed, and managed. The initial sections provide a comprehensive background on embedded systems, highlighting their distinct characteristics, resource constraints, and real-time processing demands. We delve into the limitations of conventional storage management approaches in embedded environments, emphasizing their inability to adapt to dynamic data patterns and evolving storage needs. This paves the way for the introduction of AI as a transformative force in storage optimization. The core of the paper focuses on the various AI-driven techniques employed for storage optimization in embedded systems. We explore the application of machine learning (ML) algorithms, specifically focusing on supervised and unsupervised learning paradigms. Supervised learning techniques, such as Support Vector Machines (SVMs) and decision trees, can be leveraged to predict future storage requirements and proactively allocate resources. Unsupervised learning, exemplified by k-means clustering, facilitates the identification of data patterns and the subsequent organization of data based on these patterns for improved access efficiency. Furthermore, the paper investigates the power of deep learning (DL) for storage optimization in embedded systems. Convolutional Neural Networks (CNNs) demonstrate remarkable efficacy in data compression, a crucial aspect of storage optimization. CNNs can be trained to identify redundant information within data and remove it effectively, leading to a significant reduction in storage footprint without compromising data integrity. Recurrent Neural Networks (RNNs) exhibit exceptional capabilities in time-series data analysis, prevalent in many embedded systems applications. By analyzing temporal patterns in data, RNNs can predict future storage demands and optimize data placement for real-time processing needs. A critical aspect of this paper is the exploration of model development and validation for AI-driven storage optimization in resource-constrained embedded systems. We discuss the challenges associated with training complex AI models on devices with limited computational power and memory. Techniques such as model compression, pruning, and quantization are addressed as potential solutions to mitigate these challenges. Model compression reduces the size of AI models by eliminating redundant parameters, while pruning selectively removes unnecessary connections within the network. Quantization involves converting high-precision weights to lower precision formats, enabling efficient storage and inference on embedded hardware. The paper also emphasizes the importance of model validation in ensuring the reliability and efficacy of AI-driven storage optimization techniques. We delve into various validation methodologies, including statistical analysis, cross-validation, and real-world deployment testing. Rigorous validation procedures are essential to guarantee that AI models perform as intended in the resource-constrained environment of embedded systems. To solidify the theoretical framework, the paper presents a comprehensive review of real-world applications of AI-driven storage optimization in diverse embedded system domains. We explore its implementation in Internet of Things (IoT) devices, where efficient storage management is paramount for handling large volumes of sensor data. The paper further examines the application of AI-driven storage optimization in wearable devices, where limited storage capacity necessitates intelligent data compression techniques. Additionally, we discuss the potential of AI for storage optimization in smart grid systems and autonomous vehicles, where real-time data processing and efficient storage management are critical for system performance and safety. The concluding section of the paper summarizes the key findings and emphasizes the transformative potential of AI-driven storage optimization for embedded systems. It acknowledges the ongoing research efforts aimed at further refining existing techniques and exploring new avenues for AI-powered storage management. Additionally, the paper highlights the need for continued research in developing lightweight and efficient AI models specifically tailored for the resource-constrained nature of embedded systems. Finally, the concluding remarks address the future directions of AI-driven storage optimization in this rapidly evolving field, including the integration with edge computing paradigms for distributed intelligence and collaborative storage management across interconnected embedded devices.