The use of unmanned aerial vehicles (UAVs) is well established in the military sector with great advantages in modern warfare. The concept of using UAV swarms has been discussed over two decades, but is now seeing its first real system used by the Israel defence forces. There is no exact definition what a swarm is, but it is proposed that it should satisfy the following three requirements. A swarm should have limited human control, the number of agents in a swarm should be at least three and the agents in the swarm should cooperate to perform common tasks. The complexity of controlling multiple autonomous UAVs gives way to the problem of how to take advantage of the operators cognitive and tactical abilities to control a swarm to effectively conduct military reconnaissance missions. The method of using behaviour trees as a control structure was derived from previous work in swarm systems. A behaviour tree is a structured way of organising and prioritising actions of autonomous systems. Behaviour trees are similar to finite state machines (FSMs) with the advantages of being modular, reactive, and with better readability. Three different behaviour trees with increasing complexity was created and simulated in the game engine Unity. A fourth more real life behaviour tree was created and used as a basis for discussing the strength and weaknesses of using behaviour trees against previous work. Using behaviour tree as a unifying structure for creating a swarm that integrates the tactical abilities of an operator with the strength of an autonomous swarm seems promising. The proposed method of using behaviour trees i suggested to be used as a platform for discussing the swarm desired functions and to create a common vision for both operators and engineers how a swarm should function. / Användning av drönare är väletablerad inom det militära och ger stora fördelar i dagens moderna krigsföring. Idén om att använda en svärm av drönare har diskuterats under de senaste två decennierna, men först nu sett sin första riktiga system som använts av Israels försvarsmakt. Det finns ingen exakt definition av vad en svärm är, men det har föreslagits att en svärm ska uppfylla de följande tre kraven. En svärm ska ha begränsad mänsklig interaktion, antalet agenter ska vara minst tre och svärmen ska samarbeta för att lösa gemensamma uppgifter. Svårigheterna med att styra en autonom svärm ger upphov till hur man ska utnyttja en operatörs kognitiva och taktiska förmåga för att styra en autonom drönarsvärm för att effektivt utföra militära spaningsuppdrag. Utifrån tidigare arbete inom styrning av svärmar verkade beteende träd som en lovande metod. Beteendeträd är ett strukturerat sätt att organisera och prioritera beteenden för ett autonomt system. Beteendeträd har många likheter med ändliga tillståndsmaskiner, men fördelarna att vara modulära, responsiva och mer lättläsliga. Tre olika beteendeträd med ökande komplexitet skapades och deras funktionalitet simulerades i Unity. Ett fjärde mer verklighetstroget beteendeträd skapades och användes som underlag för att diskutera beteendeträds styrkor och svagheter i jämförelse med tidigare arbeten. Användningen av beteendeträd för att förena den mänskliga operatören med det autonoma systemet verkar lovande. Den föreslagna metoden att använda beteendeträd för att styra en svärm är tänkt att användas som ett gemensamt underlag för att möjliggöra att operatörer och ingenjörer kan ha en gemensam bild hur en svärm ska fungera.

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

The maneuverability and freedom provided by unmanned aerial vehicles (UAVs) make these an interesting choice for transporting objects in settings such as search and rescue operations, construction, and smart factories. A commonly proposed method of transport is by using cables attached between each UAV and the payload. However, the geometrical constraints posed by these attachments typically result in a system with highly complex dynamics. Although not an issue for conventional PID control schemes, these complex dynamics make the direct application of model predictive controllers (MPCs) infeasible for real-time usage. For this reason, much of the previous work has focused on treating the payload as a disturbance, thereby losing the ability to predict its effect on the UAVs. Contrary to this, this thesis presents an MPC that both captures the dynamics of the payload, and is capable of real-time usage. This is made possible by a parametrized linearization of the original system, and results in greatly improved performance compared to the disturbance model approach. The controller is derived for a system with two UAVs that transport a bar-like payload and verified both in simulations and physical experiments. The resulting control system is able track a multitude of setpoints, including rotations of both payload and UAVs, as well as lateral translations. Furthermore, it is able to attenuate external disturbances well, and dampens and prevents oscillations more efficiently when compared to the disturbance based approach. The resulting MPC solving time is on the order of milliseconds. Additionally, an initial attempt to decentralize the system is made, and the resulting controller experimentally tested on the UAV–bar system, resulting in a lower MPC solving time (2:5 times faster on average), but worsened performance in terms of position tracking of the bar. / Den manövrerbarhet och frihet som möjliggörs av användandet utav obemannade luftfarkoster (drönare) gör dessa till tämligen intressanta kandidater för lasttransport inom områden såsom sök- och räddningsuppdrag, byggnadskonstruktion och s.k. smarta fabriker. En vanligen förespråkad transportmetod består utav att förse systemet med kablar som fästs mellan last och drönare. De geometriska restriktioner som denna lastkoppling innebär resulterar emellertid ofta i system med väldigt komplicerad dynamik och interaktionskrafter. Även om detta inte innebär något problem för konventionella PID reglersystem så omöjliggör detta det direkta applicerandet utav modellprediktiv reglering (MPC) för realtidsbruk. Av denna anledning har tidigare verk fokuserat på att behandla lasten och dess inverkan på drönarna som en störning, men med detta därmed förlorat möjligheten att förutspå dess effekt på drönarna. I kontrast till detta, kommer det i detta verk att presenteras en MPC som både fångar lastens dynamik och är snabb nog för realtidsanvändning. Detta görs möjligt utav en parametriserad linjärisering utav originalsystemet och ger märkbart bättre resultat än den störningbaserade modellen. Reglersystemet appliceras på ett system bestående utav två drönare och en stång-liknande last och resultatet verifieras både i form av numeriska simuleringar och fysiska experiment. Det resulterande systemet klarar av både rotationer utav last och drönare samt translationer i alla riktningar. Dessutom är systemet kapabelt att hantera externa störningar och både dämpar och förhindrar oscillationer bättre i jämförelse med reglersystem baserat på störningsmodeller. Lösningstiden för MPC-regulatorn är i storleksordningen millisekunder. Utöver detta görs ett initialt försök i att decentralisera tidigare nämnda MPC och det resulterande reglersystemet utvärderas experimentellt på samma drönarsystem som tidigare. Detta resulterar i en lägre lösningstid (2.5 ggr snabbare i genomsnitt), men även i försämrad prestanda med avseende på reglering av stångens position.

It is a well-recognized fact that unmanned aerial vehicles are an essential element in today’s network-centric integrated battlefield environment. Compared to solo UAV missions, multiple unmanned aerial vehicles deployed in co-operative mode, offer many advantages that has motivated UAV researchers all over the world to evolve concept of operations that aims in achieving a paradigm shift from traditional ”dull” missions to perform ”dirty” and ”dangerous” missions. In future success of a mission will depend on interaction among UAV groups with no interaction with any ground entity. To reach this capability level, it is necessary for researchers, to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. The thesis is in four parts: (i) Development of an organized framework to realize the goal of achieving fully autonomous systems. (ii) Design of UAV grouping algorithm and coordination tactics for ground attack missions. (iii) Cooperative network management in GPS denied environments. (iv) UAV group tactical path and goal re-plan in GPS denied wide area urban environments. This research thesis represents many first steps taken in the study of autonomous UAV systems and in particular group autonomy. An organized framework for autonomous mission control level by defining various sublevels, classifying the existing solutions and highlighting the various research opportunities available at each level is discussed. Significant contribution to group autonomy research, by providing first of its kind solution for UAV grouping based on Dubins’ path, establishing GPS protected wireless network capable of operating in GPS denied environment and demonstration of group tactical path and goal re-plan in a layered persistent ISR mission is presented. Algorithms discussed in this thesis are generic in nature and can be applied to higher autonomous mission control levels, involving strategic decisions among UAVs, satellites and ground forces in a network centric environment.

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.

Approved for public release; distribution in unlimited. / This study analyzes the effective use of multiple unmanned aerial vehicles (UAVs) for the Navy's Surface Search and Control mission. In the future, the Navy hopes to leverage the capabilities of a family of UAVs to provide increased situational awareness in the maritime environment. This family of UAVs includes a Broad Area Maritime Surveillance (BAMS) UAV and Vertical Take-Off UAVs (VTUAVs). The concepts of operations for how these UAVs work together have yet to be determined. Questions exist about the best number of UAVs, types of UAVs, and tactics that will provide increased capabilities. Through modeling and agent-based simulation, this study explores the validity of future UAV requirements and provides insights into the effectiveness of different UAV combinations. For the scenarios modeled, the best UAV combination is BAMS plus two or three VTUAVs. However, analysis shows that small numbers of VTUAVs can perform as well without BAMS as they do with BAMS. For combinations with multiple UAVs, BAMS proves to be a valuable asset that not only reduces the number of missed classifications, but greatly improves the amount of coverage on all contacts in the maritime environment. BAMS tactics have less effect than the mere presence of BAMS itself. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / This thesis is part of an ongoing research conducted at the Naval Postgraduate School to achieve the autonomous shipboard landing of Unmanned Aerial Vehicles (UAV). Two main problems are addressed in this thesis. The first is to establish communication between the UAV's ground station and the Autonomous Landing Flight Control Computer effectively. The second addresses the design and implementation of an autonomous landing controller using classical control techniques. Device drivers for the sensors and the communications protocol were developed in ANSI C. The overall system was implemented in a PC104 computer running a real-time operating system developed by The Mathworks, Inc. Computer and hardware in the loop (HIL) simulation, as well as ground test results show the feasibility of the algorithm proposed here. Flight tests are scheduled to be performed in the near future. / Lieutenant Junior Grade, Mexican Navy

Approved for public release; distribution is unlimited / An increasing number of unmanned vehicles (UV) are being incorporated into maritime operations as organic elements of Expeditionary and Carrier Strike Groups for development of the recognized maritime picture. This thesis develops an analytically-based planning aid for allocating UVs to missions. Inputs include the inventory of UVs, sensors, their performance parameters, and operational scenarios. Operations are broken into mission critical functions: detection, identification, and collection. The model output assigns aggregated packages of UVs and sensors to one of the three functions within named areas of interest. A spreadsheet model uses conservative time-speed-distance calculations, and simplified mathematical models from search theory and queuing theory, to calculate measures of performance for possible assignments of UVs to missions. The spreadsheet model generates a matrix as input to a linear integer program assignment model which finds the best assignment of UVs to missions based on the user inputs and simplified models. The results provide the mission planner with quantitatively-based recommendations for unmanned vehicle mission tasking in challenging scenarios. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / In urbanized terrain, radiowave propagation is subjected to fading on large-scales and smallscales that would impede on the quality and reliability of data link transmission. This would have implications in many military applications. One example is the performance of unmanned aerial vehicle (UAV) data and communications links in complex urban environments. The purpose of this research is to study the effectiveness of diversity techniques on the performance of urban UAV data and communications links. The techniques investigated were spatial, polarization, and angle diversities. The ray tracing software, Urbana Wireless Toolset, was used in the modeling and simulation process. The various combinations of diversity techniques were simulated using a realistic urban city model. For the few transmit-receive geometries examined, it was found that angle diversity with a directive antenna provided the greatest increase in signal strength relative to the no diversity case. / Civilian, Singapore DOD

Os veículos aéreos não tripulados (VANTs) vêm apresentando destaque crescente no setor aeronáutico mundial, tanto no desenvolvimento dos mesmos quanto nas diferentes aplicações desses veículos, devido ao seu grande potencial de utilização. Em muitas aplicações, a utilização de múltiplos VANTs apresenta várias vantagens sobre um VANT sozinho. No entanto, o agrupamento pode experimentar problemas inerentes à comunicação sem fio, podendo originar novos problemas como inviabilizar a coordenação e a execução cooperativa de uma missão, por exemplo. Em um cenário não colaborativo, VANTs com algum nível de autonomia e que partilhem do mesmo espaço aéreo também deverão ser capazes de trocar informações entre si, principalmente informações sobre posicionamento e rota de voo, e também estarão suscetíveis aos distúrbios da comunicação sem fio. Para balancear as vantagens e as desvantagens, esses robôs precisarão se comunicar cientes das restrições e utilizando a rede de modo otimizado, por meio da aplicação de algoritmos que equilibrem adequadamente técnicas de difusão de informações e técnicas de mitigação de retransmissões. Neste projeto de doutorado foi feita a investigação sobre o método de disseminação de dados, por meio de broadcasting, em uma rede móvel ad hoc entre VANTs, denominada Flying Ad hoc Networks (FANETs). FANETs são um novo paradigma que pode superar as restrições de missões de um único VANT. As FANETs são compostas por vários VANTs que cooperam para realizar alguma missão crítica (por exemplo, uma missão de busca e resgate). Para manter a coordenação, todos os VANTs devem continuamente enviar ou retransmitir mensagens através do canal sem fio para garantir que todos os membros da rede saibam o estado da rede. Geralmente, a troca de dados necessários para manter a sincronização da missão exige o uso de broadcast para que todos os membros da rede possam recebê-los. No entanto, quando essa troca de mensagens é feita arbitrariamente, isso pode causar o problema da tempestade de broadcast (BSP), levando o meio sem fio a um estado inoperante. Apesar de alguns esforços relatados na literatura para o provimento de técnicas gerais de mitigação do problema BSP, o desafio de agregar novas informações ou conhecimento a receptores que estejam voando, ao invés de apenas espalhar os dados na rede, tem recebido menos atenção. Nesta tese, além de demonstrar que o problema BSP intensifica a contenção de rede à medida que o número de VANTs aumenta, também foi criado um método que se prova mais eficiente que os existentes até então. Tal técnica, denominada de Algoritmo Baseado em Vizinhança Dinâmica para o Problema da Tempestade de Broadcast (DNA-BSP), foi desenvolvida e validada com base em experimentos de mundo real e em simulações computacionais. Ele pode mitigar o problema BSP, que é um desafio real nas FANETs, reduzindo a redundância de mensagens em mais de 98% e tornando a entrega de mensagens 99,5% mais rápida do que no cenário de flooding, superando as técnicas gerais de mitigação do BSP quando aplicado em FANETs. Os resultados detalhados neste texto também poderão orientar trabalhos futuros ao fornecer informações úteis para o planejamento e otimização de redes ad hoc móveis para VANTs. / Unmanned aerial vehicles (UAVs) have been showing increasing notoriety in the global aviation scene, both on their development and on the different applications for these vehicles, due to their high potential of use. In many applications, using multiple UAVs has several advantages over a single UAV. However, a cluster of UAVs may experience issues inherent to wireless communication, which may lead to new complications such as making mission coordination and cooperative execution impractible. In a non-collaborative scenario, UAVs with some level of autonomy which share the same airspace should also be able to exchange information among themselves, especially positioning and flight path information, and may also be susceptible to wireless communication disturbances. These robots have to use the network fairly and should communicate under restrictions, appropriately adjusting techniques that disseminate information and that mitigate broadcasts, in order to balance the advantages and disadvantages of being a group. This Ph.D. research investigates how broadcasting is used to disseminate data throughout ad hoc mobile networks between UAVs called Flying Ad hoc Networks (FANETs). FANETs are a new paradigm that can overcome the mission constraints of single UAVs. FANETs are composed of several UAVs that cooperate to accomplish a critical mission (e.g., hazardous area monitoring). Aiming to maintain UAVs coordination, all aircraft must continuously retransmit or relay messages through the wireless channel to assure that every member knows the FANET status. However, when this message exchange is done blindly, it may cause the broadcast storm problem (BSP), leading the wireless medium to a dysfunctional state. Despite some efforts reported in the literature for providing general techniques to mitigate the broadcast storm problem, the challenge of aggregating new information or knowledge to receivers, instead of just spreading the information in the network, has received less attention. In this research, it has been proved that the broadcast storm problem causes network contention as the number of UAVs increases, and the innovative Dynamic Neighborhood-based Algorithm for the Broadcast Storm Problem (DNA-BSP) has been provided as a countermeasure, which was developed and validated based on computer simulations and outdoor experiments. It can mitigate the broadcast storm problem, which is a real challenge in FANETs, reducing message redundancy in more than 98%, and making message delivery 99,5% faster than in flooding scenario, outperforming classical broadcast storm mitigation techniques when applied in FANETs. Our detailed results can also guide future researches and provide useful insights for engineers planning and optimizing mission-critical mobile ad hoc network with support of UAVs.