Ferreira Flávio Henry Cunha da Silva, Neto Miércio Cardoso de Alcântara, Barros Fabrício José Brito, Araújo Jasmine Priscyla Leite de
Institute of Technology, Federal University of Pará (UFPA), Belém 66075-110, Brazil.
Sensors (Basel). 2023 Jul 7;23(13):6231. doi: 10.3390/s23136231.
This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.
本文旨在提供一种元启发式方法，用于优化无人机阵列，以实现无线通信系统覆盖区域最大化。该系统采用无人机基站，应用场景为亚马逊地区城市中存在的郊区、从轻度到重度树木繁茂的环境。为此，分析并应用了低功耗无线区域网络（LPWAN）。LPWAN是为低数据速率工作而设计的系统，但能保持甚至增强高功率网络所提供的广泛区域覆盖。所选用的LPWAN类型是LoRa，它在915MHz的免授权频谱上运行，要求用户连接到网关以便将信息中继到中央服务器；在这种情况下，阵列中的每架无人机都安装了一个LoRa模块，用作非固定网关。为了对阵列中无人机的最佳定位进行分类和优化，选择了三种伴随的生物启发式计算（BIC）方法：布谷鸟搜索（CS）、花授粉算法（FPA）和遗传算法（GA）。然后通过MATLAB对高范围物联网-LoRa网络的定位优化结果进行模拟和展示。基于在大学校园进行的测量，开发了一个经过经验调整的传播模型，以获得针对LoRa扩频因子（SF）为8、9、10和11的森林环境中的传播模型。最后，对无人机理论传播模型的无人机定位模拟结果与通过测量得到的模型进行了比较。
