Space-efficient B-tree Implementation for Memory-Constrained Flash Embedded Devices

2026-03-05

AI Summary

This study addresses the challenge of efficiently deploying B-trees on resource-constrained embedded flash devices, where conventional implementations suffer from suboptimal performance due to stringent memory limitations and the unique characteristics of flash storage, thereby hindering edge data processing capabilities. To overcome this, the work presents the first systematic design of a B-tree tailored specifically for ultra-low-end embedded systems, introducing a lightweight B-tree variant, a flash-aware write strategy, and memory compression techniques that jointly optimize the index structure for minimal memory footprint and flash compatibility. Experimental results demonstrate that the proposed approach significantly outperforms general-purpose alternatives, establishing that even highly constrained devices can support efficient B-tree indexing and enabling practical, high-performance local data management for edge computing scenarios.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Small devices collecting data for agricultural, environmental, and industrial monitoring enable Internet of Things (IoT) applications. Given their critical role in data collection, there is a need for optimizations to improve on-device data processing. Edge device computing allows processing of the data closer to where it is collected and reduces the amount of network transmissions. The B-tree has been optimized for flash storage on servers and solid-state drives, but these optimizations often require hardware and memory resources not available on embedded devices. The contribution of this work is the development and experimental evaluation of multiple variants for B-trees on memory-constrained embedded devices. Experimental results demonstrate that even the smallest devices can perform efficient B-tree indexing, and there is a significant performance advantage for using storage-specific optimizations.

Problem

• B-tree
• memory-constrained
• embedded devices
• flash storage
• IoT

Innovation

• space-efficient B-tree
• memory-constrained embedded devices
• flash storage optimization
• edge computing
• IoT data indexing