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Do Arduinos Dream of Efficient Reasoners?

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

The Semantic Web of Things enhances the Internet of Things with Web technologies as well as Knowledge Graphs and reasoning. Traditional reasoners are too heavy in terms of memory footprint and/or processing time to be implementable on things. In this work, we present LiRoT, a lightweight incremental reasoner that can be embedded in constrained objects, so that reasoning on them in a fog architecture becomes possible. The focus of this work is to reduce drastically memory footprint while paying attention to processing time, hence usual optimization techniques are not fully adequate. We provide evaluations that (i) compare our system to the state of the art and (ii) show the effective benefits of the different optimizations we have implemented.

Keywords

  • Semantic web
  • Reasoning
  • Web of things
  • Embedded systems
  • Optimization

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Notes

  1. 1.

    Microcontrollers are small processing units designed to run embedded applications, in contrast to more powerful microprocessors that can execute general purpose applications.

  2. 2.

    https://www.w3.org/TR/rdf-mt/#rdfs_entailment.

  3. 3.

    https://coswot.gitlab.io/.

  4. 4.

    https://www.w3.org/TR/wot-usecases/#Agricultural-irrigation.

  5. 5.

    https://www.w3.org/TR/wot-usecases/#edge-computing.

  6. 6.

    https://www.w3.org/TR/owl2-profiles/#OWL_2_RL.

  7. 7.

    https://en.wikipedia.org/wiki/Logic_programming.

  8. 8.

    https://www.w3.org/TR/rdf11-mt/#entailment-rules-informative.

  9. 9.

    As we herein consider the KB as being an ontology expressed in OWL 2 RL under RDF-based semantics, facts are RDF triples.

  10. 10.

    http://www.clipsrules.net/.

  11. 11.

    https://graphdb.ontotext.com/documentation/free/reasoning.html#retraction-of-assertions.

  12. 12.

    https://github.com/drobilla/sord.

  13. 13.

    https://rdf.js.org/.

  14. 14.

    https://github.com/drobilla/serd.

  15. 15.

    https://github.com/troydhanson/uthash.

  16. 16.

    Rulesets are described at https://jena.apache.org/documentation/inference/#RDFSconfiguration.

  17. 17.

    The maximum amount of RAM used by a program throughout its execution.

  18. 18.

    Indeed, these devices have, in addition to a limited memory size for handling the application data (DRAM), the same kind of limitations for storing the program itself (IRAM). The reasoner should also be compiled specifically for the targeted platform, and use platform-specific available libraries.

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Acknowledgment

This work is supported by grant ANR-19-CE23-0012 from the Agence Nationale de la Recherche, France, for the CoSWoT project.

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Correspondence to Alexandre Bento .

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Bento, A., Médini, L., Singh, K., Laforest, F. (2022). Do Arduinos Dream of Efficient Reasoners?. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_17

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