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scribe semantically rich policies; and (c) efficiently
refine those policies to device- and service- level
policies by means of efficient reasoning procedures
and conflict detection mechanisms. In order to ad-
dress the performance issues we have seen with other
ontology-based frameworks, we have restricted our
policy representation language to OWL-QL. This is
because, OWL-QL supports efficient reasoning pro-
cedures whilst providing sufficient amount of expres-
siveness for IoT application. We then presented an
implantation of this framework and showed its appli-
cability through a smart home application. Addition-
ally, we demonstrated how our system detects policy
conflicts and uses an AI planner to find alternative
means to achieve the goals so that policy violations
are avoided. Lastly, we discussed the shortcomings
of our work and described the lines of future research
to address those shortcomings.
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