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Unmanned aerial vehicles (UAVs) have seen remarkable improvements in their flight capabilities and computational power. Their applications span various domains, such as agriculture, military, and communications. To improve communication quality and coverage in UAV-enabled systems, understanding effective placement techniques is crucial. While research has explored optimal UAV placement in two dimensions, there is a need for a comprehensive three-dimensional (3D) analysis, particularly for resource optimization. This article presents a systematic mapping study on 3D placement in UAV-enabled communication systems, incorporating a threat analysis for the reliability of the results. It provides valuable insights for future research on 3D placement optimization techniques, specifically examining optimization objectives, model system features, problem types, and solution strategies. Our research reveals, on one hand, an emphasis on optimizing data rate throughput, power transmission, and ground coverage, and a predominant use of large-scale fading in the air-to-ground channel model; on the other hand, heuristic algorithms prevail as the solution strategy. Moreover, we identify gaps in achieving critical objectives such as the probability of outage, deployment costs, quality of experience, and spectrum optimization. This highlights the need for further exploration in the formulation aspects of the system, which includes small-scale fading, access techniques, and interference management.

Usually, simulations are the first approach to evaluate wireless and mobile networks due to the difficulties involved in deploying real test scenarios. Working with simulations, testing, and validating the target network model often requires a large number of simulation runs. Consequently, there are a significant amount of outcomes to be analyzed to finally plot results. One of the most extensively used simulators for wireless and mobile networks is OMNeT++. This simulation environment provides useful tools to automate the execution of simulation campaigns, yet single-scenario simulations are also supported where the assignation of resources (i.e., CPUs) has to be declared manually. However, conducting a large number of simulations is still cumbersome and can be improved to make it easier, faster, and more comfortable to analyze. In this work, we propose a large-scale simulations framework called simulations manager for OMNeT++ (SMO). SMO allows OMNeT++ users to quickly and easily execute large-scale network simulations, hiding the tedious process of conducting big simulation campaigns. Our framework automates simulations executions, resources assignment, and post-simulation data analysis through the use of Python's wide established statistical analysis tools. Besides, our tool is flexible and easy to adapt to many different network scenarios. Our framework is accompanied by a command-line environment allowing a fast and easy manipulation that allows users to significantly reduce the total processing time to carry out large sets of simulations about 25% of the original time. Our code and its documentation are publicly available at GitHub and on our website.

We present PhySim-11p, a simulation model for the IEEE 802.11p physical layer in MATLAB. The implemented simulation obtains Packet Error Rate (PER) versus Signal-to-noise Ratio (SNR) curves for the different transmission rates allowed by the standard. The model simulates a multi-path channel with Doppler shift, which is composed of Additive White Gaussian Noise (AWGN) and frequency–time selective fading. As a use case, we present the curves of PER versus SNR obtained from our software against nine PER theoretical models. The objective is to find the theoretical model that more closely approximates the simulated results when considering transmission parameters such as the modulation scheme M and the coding rate r. Our software aims to facilitate the study of different physical layer phenoms such as amplifier effects or channel estimation and the possibility of its inclusion in network simulators. Besides, it can be easily set up to model any OFDM physical layer of IEEE 802.11 (e.g., 802.11a).

Abstract: Due to the unpredictable trajectory of its moving nodes, a VANET is a demanding communication environment. For such dynamic communication infrastructures, when certain requirements neet to be coped, the performance is a critical parameter. In this paper, we evaluate the VANET performance when an anonymous reporting service is deployed. In order to pay off the overhead generated by the anonymity service (based on the Crowds mechanism), we tune the layer-two address resolution protocol (ARP) so that it can be performed at the routing level. The tests are done with different VANET routing protocols and vehicle densities by means of simulations of a realistic scenario where the parameters of the anonymity service are also modified to determine their impact on the overall performance of the vehicular network. We found that, despite the more demanding environment generated by the deployment of the anonymity service, the VANET has a good performance in terms of packet losses, delay, and neighboring behavior, thanks to the implementation of a more efficient layer-two address resolution mechanism. Resumen: Por sí misma, una VANET es un entorno de comunicaciones muy demandante debido a sus nodos móviles cuya trayectoria es, en general, desconocida. Por ello, el rendimiento de las redes vehiculares es un parámetro crítico de estas infraestructuras dinámicas de comunicaciones, especialmente cuando se requiere desplegar sobre ellas servicios de red con distintos requisitos. En este artículo se evalúa el rendimiento de una VANET cuando un servicio anónimo de reporteo es desplegado. Con el fin de compensar la carga generada por el servicio de anonimidad, se afina el protocolo de resolución de direcciones en capa dos (ARP) de manera que éste se realice en capa de enrutamiento. Las pruebas se realizan con diferentes protocolos de enrutamiento y densidades de vehículos, por medio de simulaciones de un escenario realista en el que los parámetros del servicio de anonimidad son también modificados para determinar su impacto en la red vehicular. Encontramos que, a pesar del entorno más demandante que se obtiene por el despliegue del servicio de anonimidad, la VANET tiene un buen rendimiento en términos de pérdida de paquetes, retardo y establecimiento de vecinos, gracias a la implementación de un mecanismo de resolución de nombres más eficiente.