  Current advances in Pervasive Computing (PC) involve the adoption of the huge
infrastructures of the Internet of Things (IoT) and the Edge Computing (EC).
Both, IoT and EC, can support innovative applications around end users to
facilitate their activities. Such applications are built upon the collected
data and the appropriate processing demanded in the form of requests. To limit
the latency, instead of relying on Cloud for data storage and processing, the
research community provides a number of models for data management at the EC.
Requests, usually defined in the form of tasks or queries, demand the
processing of specific data. A model for pre-processing the data preparing them
and detecting their statistics before requests arrive is necessary. In this
paper, we propose a promising and easy to implement scheme for selecting the
appropriate host of the incoming data based on a probabilistic approach. Our
aim is to store similar data in the same distributed datasets to have,
beforehand, knowledge on their statistics while keeping their solidity at high
levels. As solidity, we consider the limited statistical deviation of data,
thus, we can support the storage of highly correlated data in the same dataset.
Additionally, we propose an aggregation mechanism for outliers detection
applied just after the arrival of data. Outliers are transferred to Cloud for
further processing. When data are accepted to be locally stored, we propose a
model for selecting the appropriate datasets where they will be replicated for
building a fault tolerant system. We analytically describe our model and
evaluate it through extensive simulations presenting its pros and cons.
