  Pervasive computing applications deal with the incorporation of intelligent
components around end users to facilitate their activities. Such applications
can be provided upon the vast infrastructures of Internet of Things (IoT) and
Edge Computing (EC). IoT devices collect ambient data transferring them towards
the EC and Cloud for further processing. EC nodes could become the hosts of
distributed datasets where various processing activities take place. The future
of EC involves numerous nodes interacting with the IoT devices and themselves
in a cooperative manner to realize the desired processing. A critical issue for
concluding this cooperative approach is the exchange of data synopses to have
EC nodes informed about the data present in their peers. Such knowledge will be
useful for decision making related to the execution of processing activities.
In this paper, we propose n uncertainty driven model for the exchange of data
synopses. We argue that EC nodes should delay the exchange of synopses
especially when no significant differences with historical values are present.
Our mechanism adopts a Fuzzy Logic (FL) system to decide when there is a
significant difference with the previous reported synopses to decide the
exchange of the new one. Our scheme is capable of alleviating the network from
numerous messages retrieved even for low fluctuations in synopses. We
analytically describe our model and evaluate it through a large set of
experiments. Our experimental evaluation targets to detect the efficiency of
the approach based on the elimination of unnecessary messages while keeping
immediately informed peer nodes for significant statistical changes in the
distributed datasets.
