Navegando por Assunto "Bioinspired computing"

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Intelligent positioning of drones via metaheuristic optimization algorithms for maximizing signal coverage area in forested environments
(Universidade Federal do Pará, 2022-01-31) FERREIRA, Flávio Henry Cunha da Silva; ALCÂNTARA NETO, Miércio Cardoso de; http://lattes.cnpq.br/0549389076806391; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
This dissertation 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. For this purpose, two types of networks utilizing UAVs have been analyzed: a standard Wi-Fi network operating at 2.4 GHz, and a low-power wireless area network (LPWAN), both considering medium to high-density forest environments. LPWAN are systems designed to work with low data rates but still keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen herein 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 every UAV in the array, three concomitant bioinspired optimization methods have been chosen: the cuckoo search (CS), the flower pollination algorithm (FPA) and the bat echolocation algorithm (BA). All of these methods have a search space distribution based on Lévy / Mantegna flights (CS, FPA) and normal distribution (BA), and present distinct performance results for both drone array network cases. Positioning optimization results are then simulated and presented via MATLAB, first for the Wi-Fi network and later for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on the UFPA campus was developed to obtain a propagation model in forested environments. Finally, drone positioning utilizing the propagation model corrected with measurements is compared with the positioning using the classical theoretical model, showing that the corrected model is more efficient in representing the forest environment than the classical model usually used in recent publications.