Authors: Nawel Sekkal; Sidi Mohamed Benslimane; Michael Mrissa; Cheol Young Park; Boudjemaa Boudaa
Addresses: LabRI-SBA Lab, Ecole Superieure en Informatique, Sidi Bel Abbes, Algeria ' LabRI-SBA Lab, Ecole Superieure en Informatique, Sidi Bel Abbes, Algeria ' InnoRenew CoE, Livade 6, 6310 Izola, Slovenia; Faculty of Mathematics, Natural Sciences and Information Technology, University of Primorska Glagoljaška ulica 8, 6000 Koper, Slovenia ' The C4I and Cyber Center, George Mason University, MS 4B5, Fairfax, VA 22030-4444, USA ' Département d'Informatique, Université Ibn Khaldoun, Tiaret, Algeria
Abstract: The web of things (WoT) uses web technologies to connect embedded objects to each other and to deliver services to stakeholders. The context of these interactions (situation) is a key source of information which can be sometimes uncertain. In this paper, we focus on the development of intelligent web services. The main requirements for intelligent service are to deal with context diversity, semantic context representation and the capacity to reason with uncertain information. From this perspective, we propose a framework for intelligent services to deal with various contexts, to reactively respond to real-time situations and proactively predict future situations. For the semantic representation of context, we use PR-OWL, a probabilistic ontology based on multi-entity Bayesian networks. PR-OWL is flexible enough to represent complex and uncertain contexts. We validate our framework with an intelligent plant watering use case to show its reasoning capabilities.
Keywords: smart web service; the web of things; context reasoning; proactive; reactive; multi-entity Bayesian networks; MEBNs; PR-OWL.
International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.1, pp.1 - 27
Received: 08 Aug 2018
Accepted: 01 Mar 2019
Published online: 03 Mar 2020 *