  Being capable of sensing and behavioral adaptation in line with their
changing environments, cognitive cyber-physical systems (CCPSs) are the new
form of applications in future wireless networks. With the advancement of the
machine learning algorithms, the transmission scheme providing the best
performance can be utilized to sustain a reliable network of CCPS agents
equipped with self-decision mechanisms, where the interactions between each
agent are modeled in terms of service quality, security, and cost dimensions.
In this work, first, we provide network utility as a reliability metric, which
is a weighted sum of the individual utility values of the CCPS agents. The
individual utilities are calculated by mixing the quality of service (QoS),
security, and cost dimensions with the proportions determined by the
individualized user requirements. By changing the proportions, the CCPS network
can be tuned for different applications of next-generation wireless networks.
Then, we propose a secure transmission policy selection (STPS) mechanism that
maximizes the network utility by using the Markov-decision process (MDP). In
STPS, the CCPS network jointly selects the best performing physical layer
security policy and the parameters of the selected secure transmission policy
to adapt to the changing environmental effects. The proposed STPS is realized
by reinforcement learning (RL), considering its real-time decision mechanism
where agents can decide automatically the best utility providing policy in an
altering environment.
