A Bayesian Game Framework for a Semi-Supervised Allocation of the Spreading Factors in LoRa Networks

LoRa networks have been gaining ground as a solution for Internet of Things because of their potential ability to handle massive number of devices. One of the most challenging problems of such networks is the need to set the Spreading Factors (SF) used by the terminals as close to a uniform distribution as possible, to guarantee reliable transmission of packets. This can be tackled through stochastic allocations based on centralized strategies, and more recently some contributions proposed fully distributed approaches based on game theory. However, these studies still consider games of complete information, where users have full knowledge on each other payoffs. In reality, it would be more appropriate to extend these approaches to Bayesian games, as we propose to do here. More precisely, we extend the game theoretic formulation to a semi-supervised allocation, where the distributed character of the allocation is retained as the nodes still act independently in choosing their SF, based on what they think it is their best preferred choice. We also utilize the central gateway as a coordinator regulating these proposals and the interaction of the nodes with the coordinator is framed as a Bayesian entry game, where nodes exploit a prior to decide whether to join the proposed allocation or not. Under this framework, nodes reach a satisfactory compromise between the assignment they receive from the network and their desired rate.