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training of personnel; iv) the presence of an x86 node
allows to aggregate or pre-process data gathered by
sensors to save transmission resources, or to recover
performance degradations due to excessive transmis-
sion delays (e.g., by properly tuning TCP parameters).
7 CONCLUSIONS
In this paper we presented a task allocation middle-
ware for developing adaptive WSN architectures in a
multi-agentenvironment. To proveits effectiveness, it
has been used to develop a forest fire preventionappli-
cation. Besides, the prototypal implementation also
demonstrated that using devices also offering human-
to-machine interaction enables individuals to easily
access measurements and functionalities of the WSN.
Future work aims at enriching the middleware
with semantic capabilities, for instance by using a set
of tuples defined via the Resource Description Frame-
work (RDF). This allows to develop ontology-based
techniques, enabling to model the application domain,
and to use automatic reasoning for the discovery and
decomposition of complex tasks. Additionally, the
SOA paradigm allows to easily integrate our middle-
ware with a Geographical Information System (GIS),
or to offer the built-in workflow engine as a “service”
to schedule resources and personnel when in presence
of alarms. Lastly, as a part of our ongoing research,
we will evaluate how to merge our solution with a De-
cision Support System (DSS).
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