  The combination of Internet of Things (IoT) and Edge Computing (EC) can
assist in the delivery of novel applications that will facilitate end users
activities. Data collected by numerous devices present in the IoT
infrastructure can be hosted into a set of EC nodes becoming the subject of
processing tasks for the provision of analytics. Analytics are derived as the
result of various queries defined by end users or applications. Such queries
can be executed in the available EC nodes to limit the latency in the provision
of responses. In this paper, we propose a meta-ensemble learning scheme that
supports the decision making for the allocation of queries to the appropriate
EC nodes. Our learning model decides over queries' and nodes' characteristics.
We provide the description of a matching process between queries and nodes
after concluding the contextual information for each envisioned characteristic
adopted in our meta-ensemble scheme. We rely on widely known ensemble models,
combine them and offer an additional processing layer to increase the
performance. The aim is to result a subset of EC nodes that will host each
incoming query. Apart from the description of the proposed model, we report on
its evaluation and the corresponding results. Through a large set of
experiments and a numerical analysis, we aim at revealing the pros and cons of
the proposed scheme.
