  In edge-cloud collaborative intelligence (CI), an unreliable transmission
channel exists in the information path of the AI model performing the
inference. It is important to be able to simulate the performance of the CI
system across an imperfect channel in order to understand system behavior and
develop appropriate error control strategies. In this paper we present a
simulation framework called DFTS2, which enables researchers to define the
components of the CI system in TensorFlow~2, select a packet-based channel
model with various parameters, and simulate system behavior under various
channel conditions and error/loss control strategies. Using DFTS2, we also
present the most comprehensive study to date of the packet loss concealment
methods for collaborative image classification models.
