  Enabling large-scale energy-efficient Internet-of-things (IoT) connectivity
is an essential step towards realization of networked society. While legacy
wide-area wireless systems are highly dependent on network-side coordination,
the level of consumed energy in signaling, as well as the expected increase in
the number of IoT devices, makes such centralized approaches infeasible in
future. Here, we address this problem by self-coordination for IoT networks
through learning from past communications. To this end, we first study
low-complexity distributed learning approaches applicable in IoT
communications. Then, we present a learning solution to adapt communication
parameters of devices to the environment for maximizing energy efficiency and
reliability in data transmissions. Furthermore, leveraging tools from
stochastic geometry, we evaluate the performance of proposed distributed
learning solution against the centralized coordination. Finally, we analyze the
interplay amongst energy efficiency, reliability of communications against
noise and interference over data channel, and reliability against adversarial
interference over data and feedback channels. The simulation results indicate
that compared to the state of the art approaches, both energy efficiency and
reliability in IoT communications could be significantly improved using the
proposed learning approach. These promising results, which are achieved using
lightweight learning, make our solution favorable in many low-cost low-power
IoT applications.
