  The heterogeneity of the Internet-of-things (IoT) network can be exploited as
a dynamic computational resource environment for many devices lacking
computational capabilities. A smart mechanism for allocating edge and mobile
computers to match the need of devices requesting external computational
resources is developed. In this paper, we employ the concept of Social IoT and
machine learning to downgrade the complexity of allocating appropriate edge
computers. We propose a framework that detects different communities of devices
in SIoT enclosing trustworthy peers having strong social relations. Afterwards,
we train a machine learning algorithm, considering multiple computational and
non-computational features of the requester as well as the edge computers, to
predict the total time needed to process the required task by the potential
candidates belonging to the same community of the requester. By applying it to
a real-world data set, we observe that the proposed framework provides
encouraging results for mobile computer allocation.
