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.

翻译：无人机辅助通信正逐渐成为一项为基于物联网的设备提供增强覆盖的主流技术。快速部署、可移植性和灵活性是无人机的基本特性，这些特性使其成为有效管理应急物联网应用的理想选择。本文研究了一种依赖非正交多址技术的无人机辅助无线物联网网络，旨在为分布在灾害区域的设备提供上行连接。该无人机设置能够使用解码转发中继协议将信息中继至蜂窝基站。通过联合利用无监督机器学习的概念并求解由此产生的非凸问题，我们可以最大化分布在灾害区域的物联网设备的总能量效率。我们提出的方法结合使用k-medoids和轮廓分析来执行资源分配，而功率优化则通过迭代方法实现。与穷举搜索方法相比，我们提出的方案以低得多的复杂度解决了能量效率最大化问题，同时改善了物联网设备的整体能耗。此外，与改进版贪心算法相比，在固定目标比特数为50k的条件下，我们提出的方法将系统的总能量效率提高了19%。