Evaluating correctness, performance and energy footprint of semantic reasoners in mobile edge computing

• Automated analysis methods for semantic reasoners in Mobile Edge Computing. • On an Android device, 6 reasoners analyzed on correctness, time and memory usage. • Correctness, time, memory and energy footprint of 3 reasoners tested on a single-board computer. • Hardware-based and software-based energy profilers have been used and compared. • Multiplatform automated benchmarking tool for semantic reasoners upgraded. The integration of Semantic Web technologies into Mobile Edge Computing (MEC) platforms is enhancing the capabilities of real-time, context-aware applications across diverse domains. MEC brings processing closer to the network edge, reducing latency and allowing for the improvement of data privacy, while Semantic Web technologies provide machine-interpretable knowledge representation and reasoning capabilities. Despite their potential, deploying semantic reasoners on edge devices is challenging due to their resource-intensive nature, which requires significant memory availability, computational power, and energy. Furthermore, correctness, performance and energy consumption are simultaneously important, as MEC semantics-based applications often call for real-time queries for autonomous agent decision or user-oriented decision support. This paper presents an extensive experimental evaluation of Web Ontology Language (OWL) reasoners deployed in MEC environments, assessing correctness, processing time, memory usage, and energy consumption across both a reference tablet and a single-board computer. For energy measurement, both software profiling and hardware monitoring have been exploited and compared. The study is supported by a modular, cross-platform benchmarking framework that automates data collection and ensures reproducibility. The findings highlight the trade-offs between reasoning capabilities and resource consumption, offering valuable insights for refining testing methodologies as well as optimizing semantic reasoners in MEC settings.