The Sensor Analysis and Intelligence Laboratory (SAIL) at Mississippi State University's (MSU's) Center for Advanced Vehicular Systems (CAVS) incorporated sensors with unmanned aerial vehicles (UAVs). Mounting plates were created to secure the sensors to the UAVs for data collection. This study’s purpose was to detail the process that went in to creating two different versions of the mount plates. One version of the mounting system was cut from G10 fiberglass sheets, and the other version was made from 3D printing with polylactic acid (PLA). Characteristics such as cost, time, and simplicity of the manufacturing methods are compared in this study. Plate performance characteristics such as compatibility, weight, and success/failure are also discussed. Detailing the advantages and limitations of either approach will aid future researchers’ decision-making process for their own studies. They can use this study as a foundational framework for deciding which mount would best fit with their system requirements.

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Unmanned Aerial Vehicles (UAVs) are quite successful in maintaining steady flight operations, but propeller failure that exists causes them to experience a possible crash. The objective of this thesis project is to propose a UAV failure detection model as part of the developing framework of an autonomous emergency landing system for UAVs. Health monitoring is integrated where the quadcopter is flown for three cases of propeller faults. Vibration signals are measured during each flight, where a hardware system is designed with Arduino Uno and an Inertial Measurement Unit (IMU) sensor that contains a 3-axis accelerometer and a 3-axis gyroscope, and vibration graphs are made. Once the data is extracted, different parameters (aX, aY, aZ, gX, gY, and gZ) are selected with dimension n ∈ {1,2,3,4,5,6}, and 750 data points are chosen for the K-Means Clustering algorithm. Quadcopter Failure Detection Cluster (QFDC) plots and confusion matrices are created, and three different health states are classified as clusters – normal state, faulty state, and failure state. The parameter set gZ-aZ has the best performance metrics with an accuracy of 92.1%, which is chosen for the decision-making step that involves a Light Emitting Diode (LED) subsystem. Boundary conditions are set from the gZ-aZ QFDC plot where three LEDs turn on based on the specified health state to validate the model. The accuracies of the LED system range between 89% and 95%. Successful failure detection for UAVs would make UAVs safer and more reliable to fly with less imposed restrictions.

This project presents a motion planning algorithmcapable of generating a quadrotor UAV trajectory between aninitial state and a goal state in an obstacle-cluttered environment.This trajectory is dynamically feasible, collision free and optimalby minimizing a cost function in jerk. The algorithm consistsof incrementally expanding a set of concatenated trajectoriesdefined as motion primitives, stored in a tree data structure, untila feasible high-level trajectory is found. TheA∗sorting algorithmis utilized to sort the tree by least cost, hence ensuring the optimaltrajectory is found. In case of narrow spaces requiring someangled UAV attitude, aggressive maneuvering can be attemptedto achieve a feasible trajectory. Two scenarios are introducedfor which feasible trajectories are calculated. These scenariosare also virtually simulated by coupling a UAV dynamic modelwith a feedback controller, for which the feasible trajectories areachieved despite introducing artificial disturbances in the controlinputs. Limitations of the implemented methods are mentioned,together with suggestions to areas of improvement, at the end ofthe report. / Detta projekt presenterar en rörelseplaneringsalgoritm som genererar en bana mellanett initialtillstånd och ett slutmålstillstånd för en fyrmotorigUAV i ett rum med hinder. Denna bana är dynamiskgenomförbar, kollisionsfri samt optimal genom att minimeraen kostnadsfunktion i ryck. Algoritmen består av att stegvis utöka en uppsättning av hoplänkade banor definierade sommotion primitives, som lagras i ett träd (datastruktur), tills engenomförbar bana hittas. SorteringsalgoritmenA∗användsför att sortera trädet efter minst kostnad, vilket säkerställer att den optimala banan hittas. I fallet med små utrymmen,som kräver en vinklad orientering hos UAV:n, kan aggressivamanövrar utföras för att försöka hitta en genomförbar bana.Två scenarion presenteras där genomförbara banor beräknasfram. Dessa scenarion simuleras också virtuellt genom attkoppla UAV:ns dynamiska modell med ettåterkopplingssystem,där banorna genomförs trots att artificiella störningar införsi kontrollsignalerna. Begränsningar i metoden och förslag på förbättringar diskuteras i slutet på rapporten. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Aerobiological sampling using unmanned aerial vehicles (UAVs) is an exciting research field blending various scientific and engineering disciplines. The biological data collected using UAVs helps to better understand the atmospheric transport of microorganisms. Autopilot-equipped UAVs can accurately sample along pre-defined flight plans and precisely regulated altitudes. They can provide even greater utility when they are networked together in coordinated sampling missions: such measurements can yield further information about the aerial transport process. In this work flight vehicle path planning, control and coordination strategies are considered for unmanned autonomous aerial vehicles. A time-optimal path planning algorithm, that is simple enough to be solved in real time, is derived based on geometric concepts. The method yields closed-form solution for an important subset of candidate extremal paths; the rest of the paths are found using a simple numerical root-finding algorithm. A multi-UAV coordination framework is applied to a specific control-volume sampling problem that supports aerobiological data-collection efforts conducted in the lower atmosphere. The work is part of a larger effort that focuses on the validation of atmospheric dispersion models developed to predict the spread of plant diseases in the lower atmosphere. The developed concepts and methods are demonstrated by field experiments focusing on the spread of the plant pathogen <i>Phytophthora infestans</i>. / Ph. D.

Next-generation cellular systems will rely on millimeter wave (mmWave) bands to meet the increasing demand for wireless connectivity from end user equipment. Given large available bandwidth and small-sized antenna elements, mmWave frequencies can support high communication rates and facilitate the use of multiple-input-multiple-output (MIMO) techniques to increase the wireless capacity. However, the small wavelength of mmWave yields severe path loss and high channel uncertainty. Meanwhile, using a large number of antenna elements requires a high energy consumption and heavy communication overhead for MIMO transmissions and channel measurement. To facilitate efficient mmWave communications, in this dissertation, the challenges of energy efficiency and communication overhead are addressed. First, the use of unmanned aerial vehicle (UAV), intelligent signal reflector, and device-to-device (D2D) communications are investigated to improve the reliability and energy efficiency of mmWave communications in face of blockage. Next, to reduce the communication overhead, new channel modeling and user localization approaches are developed to facilitate MIMO channel estimation by providing prior knowledge of mmWave links. Using advance mathematical tools from machine learning (ML), game theory, and communication theory, this dissertation develops a suite of novel frameworks using which mmWave communication networks can be reliably deployed and operated in wireless cellular systems, UAV networks, and wearable device networks. For UAV-based wireless communications, a learning framework is developed to predict the cellular data traffic during congestion events, and a new framework for the on-demand deployment of UAVs is proposed to offload the excessive traffic from the ground base stations (BSs) to the UAVs. The results show that the proposed approach enables a dynamical and optimal deployment of UAVs that alleviates the cellular traffic congestion. Subsequently, a novel energy-efficient framework is developed to reflect mmWave signals from a BS towards mobile users using a UAV-carried intelligent reflector (IR). To optimize the location and reflection coefficient of the UAV-carried IR, a deep reinforcement learning (RL) approach is proposed to maximize the downlink transmission capacity. The results show that the RL-based approach significantly improves the downlink line-of-sight probability and increases the achievable data rate. Moreover, the channel estimation challenge for MIMO communications is addressed using a distributional RL approach, while optimizing an IR-aided downlink multi-user communication. The results show that the proposed method captures the statistic feature of MIMO channels, and significantly increases the downlink sum-rate. Moreover, in order to capture the characteristics of air-to-ground channels, a data-driven approach is developed, based on a distributed framework of generative adversarial networks, so that each UAV collects and shares mmWave channel state information (CSI) for cooperative channel modeling. The results show that the proposed algorithm enables an accurate channel modeling for mmWave MIMO communications over a large temporal-spatial domain. Furthermore, the CSI pattern is analyzed via semi-supervised ML tools to localize the wireless devices in the mmWave networks. Finally, to support D2D communications, a novel framework for mmWave multi-hop transmissions is investigated to improve the performance of the high-rate low-latency transmissions between wearable devices. In a nutshell, this dissertation provides analytical foundations on the ML-based performance optimization of mmWave communication systems, and the anticipated results provide rigorous guidelines for effective deployment of mmWave frequency bands into next-generation wireless systems (e.g., 6G). / Doctor of Philosophy / Different kinds of new smart devices are invented and deployed every year. Emerging smart city applications, including autonomous vehicles, virtual reality, drones, and Internet-of-things, will require the wireless communication system to support more data transmissions and connectivity. However, existing wireless network (e.g., 5G and Wi-Fi) operates at congested microwave frequency bands and cannot satisfy needs of these applications due to limited resources. Therefore, a different, very high frequency band at the millimeter wave (mmWave) spectrum becomes an inevitable choice to manage the exponential growth in wireless traffic for next-generation communication systems. With abundant bandwidth resources, mmWave frequencies can provide the high transmission rate and support the wireless connectivity for the massive number of devices in a smart city. Despite the advantages of communications at the mmWave bands, it is necessary to address the challenges related to high-frequency transmissions, such as low energy efficiency and unpredictable link states. To this end, this dissertation develops a set of novel network frameworks to facilitate the service deployment, performance analysis, and network optimization for mmWave communications. In particular, the proposed frameworks and efficient algorithms are tailored to the characteristics of mmWave propagation and satisfy the communication requirements of emerging smart city applications. Using advanced mathematical tools from machine learning, game theory, and wireless communications, this dissertation provides a comprehensive understanding of the communication performance over mmWave frequencies in the cellular systems, wireless local area networks, and drone networks. The anticipated results will promote the deployment of mmWave frequencies in next-generation communication systems.

Emerging integrated satellite-drone-terrestrial communication (ISDTC) technologies are expected to contribute to our life by bringing forth high speed wireless connectivity to every corner of the world. On the one hand, the Internet of Things (IoT) provides connectivity to various physical objects by enabling them to share information and to coordinate decisions. On the other hand, the non-terrestrial components of an ISDTC system, i.e. unmanned aerial vehicles (UAVs), and satellites, can boost the capacity of wireless networks by providing services to hotspots, disaster affected, and rural areas. Despite the several benefits and practical applications of ISDTC technologies, one must address many technical challenges such as, resource management, trajectory design, device cooperation, data routing, and security. The key goal of this dissertation is to develop analytical foundations for the optimization of ISDTC operations, and the deployment of non-terrestrial networks (NTNs). First, the problem of resource management within ISDTC systems is investigated for service-effective cooperation among the terrestrial networks and NTNs. The performance of a multi-layer ISDTC system is analyzed within a competitive market setting.Using a novel decentralized algorithm, spectrum resources are allocated to each one of the communication links, considering the fairness among devices. The proposed algorithm is proved to reach a Walrasian equilibrium, at which the sum-rate of the network is maximized. The results also show that the proposed algorithm can reach the equilibrium with a practical convergence speed. Then, the effective deployment of NTNs under environmental dynamics is investigated using machine learning solutions with meta training capabilities. First, the use of satellites for on-demand coverage to unforeseeable radio access needs is investigated using game theory. The optimal data routing strategies are learned by the satellite system, using a novel reinforcement learning approach with distribution-robust meta training capability. The results show that, the proposed meta training mechanism significantly reduces the learning cost on the satellites, and is guaranteed to reach the maximal service coverage in the system. Next, the problem of control of UAV-carried radio access points under energy constraints is studied. In particular, novel frameworks are proposed to design trajectories for UAVs that seek to deliver data service to distributed, dynamic, and unforeseeable wireless access requests. The results show that the proposed approaches are guaranteed to converge to an optimal trajectory, and can get a faster convergence speed and lower computation cost using decomposition, cross validation and meta learning. Finally, this dissertation looks at the security of an IoT system. In particular, the impact of human intervention on the system security is analyzed under specific resource constraints. Psychological game theory frameworks are proposed to analyze the human psychology and its impact on the security of the system. The results show that the proposed solution can help the defender optimize its connectivity within the IoT system by estimating the attacker's behavior. In summary, the outcomes of this dissertation provide key guidelines for the effective deployment of ISDTC systems. / Doctor of Philosophy / In the past decade, the goal of providing wireless connectivity to all individuals and communities, including the most disadvantaged ones, has become a national priority both in the US and globally. Yet, remarkably, until today, there is still a great portion of the Earth's population who falls out of today's wireless broadband coverage. While people who live in under-developed or rural areas are still in "wireless darkness", communities in megacities often experience below-par wireless service due to their overloaded communication systems. To provide high-speed, reliable wireless connectivity to those on the less-served side of the digital divide, an integrated space-air-ground communication system can be designed. Indeed, airborne and space-based non terrestrial networks (NTNs) can enhance the capacity and coverage of existing wireless cellular networks (e.g., 5G and beyond) by providing supplemental, affordable, flexible, and reliable service to users in rural, disaster affected, and over-crowded areas. In order to fill the coverage holes and bridge the digital divide, seamless integration among NTNs and terrestrial networks is needed. In particular, when deploying an integrated communication system, one must consider the problems of spectrum management, device cooperation, trajectory design, and data routing within the system. Meanwhile, with the increased exposure to malicious attacks on high altitude platforms and vulnerable IoT devices, the security within the integrated system must be analyzed and optimized for reliable data service. To overcome all the technological challenges that hinder the realization of global digital inclusion, this dissertation uses techniques from the fields of game theory, meta learning, and optimization theory to deploy, control, coordinate, and manage terrestrial networks and NTNs. The anticipated results show that a properly integrated satellite-drone-terrestrial communication (ISDTC) system can deliver cost-effective, high speed, seamless wireless service to our world.