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Unmanned aerial vehicle (UAV)-assisted communication is becoming astreamlined technology in providing improved coverage to the internet-of-things(IoT) based devices. Rapid deployment, portability, and flexibility are some ofthe fundamental characteristics of UAVs, which make them ideal for effectivelymanaging emergency-based IoT applications. This paper studies a UAV-assistedwireless IoT network relying on non-orthogonal multiple access (NOMA) tofacilitate uplink connectivity for devices spread over a disaster region. TheUAV setup is capable of relaying the information to the cellular base station(BS) using decode and forward relay protocol. By jointly utilizing the conceptsof unsupervised machine learning (ML) and solving the resulting non-convexproblem, we can maximize the total energy efficiency (EE) of IoT devices spreadover a disaster region. Our proposed approach uses a combination of k-medoidsand Silhouette analysis to perform resource allocation, whereas, poweroptimization is performed using iterative methods. In comparison to theexhaustive search method, our proposed scheme solves the EE maximizationproblem with much lower complexity and at the same time improves the overallenergy consumption of the IoT devices. Moreover, in comparison to a modifiedversion of greedy algorithm, our proposed approach improves the total EE of thesystem by 19% for a fixed 50k target number of bits.

Green UAV-enabled Internet-of-Things Network with AI-assisted NOMA for Disaster Management