  With the emerging Internet-of-Things services, massive machine-to-machine
(M2M) communication will be deployed on top of human-to-human (H2H)
communication in the near future. Due to the coexistence of M2M and H2H
communications, the performance of M2M (i.e., secondary) network depends
largely on the H2H (i.e., primary) network. In this paper, we propose ambient
backscatter communication for the M2M network which exploits the energy
(signal) sources of the H2H network, referring to traffic applications and
popularity. In order to maximize the harvesting and transmission opportunities
offered by varying traffic sources of the H2H network, we adopt a Bayesian
nonparametric (BNP) learning algorithm to classify traffic applications
(patterns) for secondary user (SU). We then analyze the performance of SU using
the stochastic geometrical approach, based on a criterion for optimal traffic
pattern selection. Results are presented to validate the performance of the
proposed BNP classification algorithm and the criterion, as well as the impact
of traffic sources and popularity.
