  Some of the main challenges towards utilizing conventional cryptographic
techniques in Internet of Things (IoT) include the need for generating secret
keys for such a large-scale network, distributing the generated keys to all the
devices, key storage as well as the vulnerability to security attacks when an
adversary gets physical access to the devices. In this paper, a novel secret
key generation method is proposed for IoTs that utilize the intrinsic
randomness embedded in the devices' memories introduced in the manufacturing
process. A fuzzy extractor structure using serially concatenated BCH-Polar
codes is proposed to generate reproducible keys from a ReRAM-based
\emph{ternary-state} Physical Unclonable Functions (PUFs) for device
authentication and secret key generation. The ReRAM based PUFs are the most
practical choice for authentication and key generation in IoT, as they operate
at or below the systems' noise level and therefore are less vulnerable to side
channel attacks compared to the alternative memory technologies. However, the
current ReRAM-based PUFs present a high false negative authentication rate
since the behavior of these devices can vary in different physical conditions
that results in a low probability of regenerating the same response in
different attempts. In this paper, we propose a secret key generation scheme
for ternary state PUFs that enables reliable reconstruction of the desired
secret keys utilizing a serially concatenated BCH-Polar fuzzy extractor. The
experimental results show that the proposed model can offer a significantly
lower probability of mismatch between the original key and the regenerated
ones, while a less number of \textit{Helper data} bits were used to extract the
\textit{Key} when compared to previously proposed fuzzy extractor techniques.
