  Recent research has shown the potential of using available mobile fog devices
(such as smartphones, drones, domestic and industrial robots) as relays to
minimize communication outages between sensors and destination devices, where
localized Internet-of-Things services (e.g., manufacturing process control,
health and security monitoring) are delivered. However, these mobile relays
deplete energy when they move and transmit to distant destinations. As such,
power-control mechanisms and intelligent mobility of the relay devices are
critical in improving communication performance and energy utilization. In this
paper, we propose a Q-learning-based decentralized approach where each mobile
fog relay agent (MFRA) is controlled by an autonomous agent which uses
reinforcement learning to simultaneously improve communication performance and
energy utilization. Each autonomous agent learns based on the feedback from the
destination and its own energy levels whether to remain active and forward the
message, or become passive for that transmission phase. We evaluate the
approach by comparing with the centralized approach, and observe that with
lesser number of MFRAs, our approach is able to ensure reliable delivery of
data and reduce overall energy cost by 56.76\% -- 88.03\%.
