  Recently, deep neural networks have been outperforming conventional machine
learning algorithms in many computer vision-related tasks. However, it is not
computationally acceptable to implement these models on mobile and IoT devices
and the majority of devices are harnessing the cloud computing methodology in
which outstanding deep learning models are responsible for analyzing the data
on the server. This can bring the communication cost for the devices and make
the whole system useless in those times where the communication is not
available. In this paper, a new framework for deploying on IoT devices has been
proposed which can take advantage of both the cloud and the on-device models by
extracting the meta-information from each sample's classification result and
evaluating the classification's performance for the necessity of sending the
sample to the server. Experimental results show that only 40 percent of the
test data should be sent to the server using this technique and the overall
accuracy of the framework is 92 percent which improves the accuracy of both
client and server models.
