  Deep Learning (DL) has been utilized pervasively in the Internet of Things
(IoT). One typical application of DL in IoT is device identification from
wireless signals, namely Non-cryptographic Device Identification (NDI).
However, learning components in NDI systems have to evolve to adapt to
operational variations, such a paradigm is termed as Incremental Learning (IL).
Various IL algorithms have been proposed and many of them require dedicated
space to store the increasing amount of historical data, and therefore, they
are not suitable for IoT or mobile applications. However, conventional IL
schemes can not provide satisfying performance when historical data are not
available. In this paper, we address the IL problem in NDI from a new
perspective, firstly, we provide a new metric to measure the degree of
topological maturity of DNN models from the degree of conflict of
class-specific fingerprints. We discover that an important cause for
performance degradation in IL enabled NDI is owing to the conflict of devices'
fingerprints. Second, we also show that the conventional IL schemes can lead to
low topological maturity of DNN models in NDI systems. Thirdly, we propose a
new Channel Separation Enabled Incremental Learning (CSIL) scheme without using
historical data, in which our strategy can automatically separate devices'
fingerprints in different learning stages and avoid potential conflict.
Finally, We evaluated the effectiveness of the proposed framework using real
data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of
IoT in aviation. The proposed framework has the potential to be applied to
accurate identification of IoT devices in a variety of IoT applications and
services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87)
and \url{https://github.com/pcwhy/CSIL}}
