In today's computing environment, where Artificial Intelligence (AI) and data
processing are moving toward the Internet of Things (IoT) and the Edge
computing paradigm, benchmarking resource-constrained devices is a critical
task to evaluate their suitability and performance. The literature has
extensively explored the performance of IoT devices when running high-level
benchmarks specialized in particular application scenarios, such as AI or
medical applications. However, lower-level benchmarking applications and
datasets that analyze the hardware components of each device are needed. This
low-level device understanding enables new AI solutions for network, system and
service management based on device performance, such as individual device
identification, so it is an area worth exploring more in detail. In this paper,
we present 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 different application scenarios. Finally, to
demonstrate the inter-scenario capability of the created dataset, a series of
AI-enabled use cases about device identification and context impact on
performance are presented as examples and exploration of the published data