Avaliação de quatro algoritmos de trilateração baseados em RSSI considerando a variação do número de nós fixos

Abstract

Unmanned Aerial Vehicles (UAVs) are machines of great importance these days, as they are no longer a resource that was previously limited to military use but are beginning to exist in the everyday life of society. This change is due to the advancement of technology and the prevalence of his IoT (Internet of Things), where devices are becoming more and more connected. The use of drones for tracking has a variety of applications that exist in fields such as military, agriculture, and government. RSSI is one of the most popular distance estimation techniques, notable for its wide use in traditional wireless technologies and its presence in highly available and easy acquisition devices. The current work proposes a simulation using the RSSI, Received Signal Strength Indicator, method using four different trilateration algorithms to determine the number of anchor nodes from which signals are generated to determine the desired position. Check precision and accuracy in different scenarios. Heatmaps were generated for each trilateration method using MATLAB software to obtain the behavior of the parameters selected for qualitative analysis of the results. Using the data generated by the simulations, mean errors and standard deviations medium values were calculated to allow a general assessment of the algorithm's performance. Comparing the extracted results allowed the identification the strengths and weaknesses of each method applied to RSSI, opening the possibility of using any one of the four algorithms in certain scenarios, although the presented It does not obviate the need to empirically verify the performances presented. From the three chosen parameters, it was possible to estimate IoT applications in which each triangulation method would have a better performance. Assuming, for example, situations in which there would be a need for greater accuracy for an application in drone location or a scenario where the position processing was crucial.