  Efficient resource allocation (RA) strategies within massive and dense
Internet of Things (IoT) networks is one of the major challenges in the
deployment of IoT-network based smart ecosystems involving heterogeneous
power-constrained IoT devices operating in varied radio and environmental
conditions. In this paper, we focus on the transmit power minimization problem
for IoT devices while maintaining a threshold channel throughput. The
established optimization literature is not robust against the fast-fading
channel and the interaction among different transmit signals in each instance.
Besides, realistically, each IoT node possesses incomplete channel state
information (CSI) on its neighbors, such as the channel gain being private
information for the node itself. In this work, we resort to Bayesian game
theoretic strategies for solving the transmit power optimization problem
exploiting incomplete CSIs within massive IoT networks. We provide a steady
discussion on the rationale for selecting the game theory, particularly the
Bayesian scheme, with a graphical visualization of our formulated problem. We
take advantage of the property of the existence and uniqueness of the Bayesian
Nash equilibrium (BNE), which exhibits reduced computational complexity while
optimizing transmit power and maintaining target throughput within networks
comprised of heterogeneous devices.
