A hybrid reasoning mechanism for effective sensor selection for tasks

  • Authors:

  • Geeth De Mel;Murat Sensoy;Wamberto Vasconcelos;Timothy J. Norman

  • Affiliations:

  • Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK and US Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA and IBM T.J. Watson Research Centre, Y ...;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK and Department of Computer Science, Özyegin University, Istanbul, Turkey;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2013

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Abstract

In this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.