  Unmanned aerial vehicle (UAV)-assisted communication is becoming a
streamlined technology in providing improved coverage to the internet-of-things
(IoT) based devices. Rapid deployment, portability, and flexibility are some of
the fundamental characteristics of UAVs, which make them ideal for effectively
managing emergency-based IoT applications. This paper studies a UAV-assisted
wireless IoT network relying on non-orthogonal multiple access (NOMA) to
facilitate uplink connectivity for devices spread over a disaster region. The
UAV setup is capable of relaying the information to the cellular base station
(BS) using decode and forward relay protocol. By jointly utilizing the concepts
of unsupervised machine learning (ML) and solving the resulting non-convex
problem, we can maximize the total energy efficiency (EE) of IoT devices spread
over a disaster region. Our proposed approach uses a combination of k-medoids
and Silhouette analysis to perform resource allocation, whereas, power
optimization is performed using iterative methods. In comparison to the
exhaustive search method, our proposed scheme solves the EE maximization
problem with much lower complexity and at the same time improves the overall
energy consumption of the IoT devices. Moreover, in comparison to a modified
version of greedy algorithm, our proposed approach improves the total EE of the
system by 19% for a fixed 50k target number of bits.
