  In this paper, we examine the internet of things system which is dedicated
for smart cities, smart factory, and connected cars, etc. To support such
systems in wide area with low power consumption, energy harvesting technology
without wired charging infrastructure is one of the important issues for
longevity of networks. In consideration of the fact that the position and
amount of energy charged for each device might be unbalanced according to the
distribution of nodes and energy sources, the problem of maximizing the minimum
throughput among all nodes becomes a NP-hard challenging issue. To overcome
this complexity, we propose a machine learning based relaying topology
algorithm with a novel backward-pass rate assessment method to present proper
learning direction and an iterative balancing time slot allocation algorithm
which can utilize the node with sufficient energy as the relay. To validate the
proposed scheme, we conducted simulations on the system model we established,
thus confirm that the proposed scheme is stable and superior to conventional
schemes.
