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.

翻译：小型设备在农业、环境和工业监测领域的数据采集为物联网应用提供了支持。鉴于其在数据收集中的关键作用，需要优化技术以改进设备端数据处理能力。边缘设备计算使得数据能在采集位置附近进行处理，从而减少网络传输量。B树已在服务器和固态硬盘的闪存存储方面获得优化，但这些优化通常需要嵌入式设备所不具备的硬件和内存资源。本研究的贡献在于针对内存受限嵌入式设备开发并实验评估了多种B树变体。实验结果表明，即使是最小型的设备也能执行高效的B树索引操作，且采用存储专用优化方案能带来显著的性能优势。