  In today's computing environment, where Artificial Intelligence (AI) and data
processing are moving toward the Internet of Things (IoT) and Edge computing
paradigms, benchmarking resource-constrained devices is a critical task to
evaluate their suitability and performance. Between the employed devices,
Single-Board Computers arise as multi-purpose and affordable systems. The
literature has explored Single-Board Computers performance when running
high-level benchmarks specialized in particular application scenarios, such as
AI or medical applications. However, lower-level benchmarking applications and
datasets are needed to enable new Edge-based AI solutions for network, system
and service management based on device and component performance, such as
individual device identification. Thus, this paper presents LwHBench, a
low-level hardware benchmarking application for Single-Board Computers that
measures the performance of CPU, GPU, Memory and Storage taking into account
the component constraints in these types of devices. LwHBench has been
implemented for Raspberry Pi devices and run for 100 days on a set of 45
devices to generate an extensive dataset that allows the usage of AI techniques
in scenarios where performance data can help in the device management process.
Besides, to demonstrate the inter-scenario capability of the dataset, a series
of AI-enabled use cases about device identification and context impact on
performance are presented as exploration of the published data. Finally, the
benchmark application has been adapted and applied to an agriculture-focused
scenario where three RockPro64 devices are present.
