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

TL;DR

The paper tackles energy-efficient uplink connectivity in a disaster zone by enabling a UAV to relay NOMA-based IoT traffic to a cellular BS. It introduces GREEN-AI, a framework that employs k-medoids clustering with Silhouette analysis for subcarrier allocation and a convexified, iterative power allocation to maximize energy efficiency under SIC and power constraints. Key contributions include a low-complexity ML-driven resource allocation scheme and a detailed complexity analysis showing substantial gains over exhaustive search and greedy baselines (e.g., ~36% improvement at low circuit power and ~19% at higher circuit power). The findings demonstrate that the proposed approach can significantly enhance energy efficiency in UAV-enabled disaster scenarios and can be extended to multi-UAV and multi-antenna deployments for broader applicability.

Abstract

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.

Figures (4)

• Figure 1: UAV-assisted IoT devices in a disaster situation.
• Figure 2: $EE$ with respect to $P_f$, when $K=70$, $P_{k}^{max}=0.2$ W and $Bt_{n}=50$ kbits.
• Figure 3: $EE$ with respect to $Z_{UAV}$ with $P_f=1.4002$, $P_{k}^{max}=0.2$ W and $Bt_{n}=50$ kbits.
• Figure 4: $EE$ with respect to $P_f$, when $K=70$, $P_{k}^{max}=0.2$ W $Bt_{n}=50$ kbits, and propagation effects include Rayleigh fading.