Synergistic path planning of multi-UAVs for air pollution detection of ships in ports

The phenomena of the COVID-19 outbreak and the Arctic Iceberg melting over the past two years make us reconsider the impact our way of life has on the environment and the responsibility of business toward minimizing and potentially eliminating emissions. Increasing ship traffic in ports leads to the growing emission of air pollutants, which influences the air quality and public health in the surrounding areas. The International Maritime Organization (IMO) has adopted relevant regulations (e.g., Annex VI of IMO's pollution prevention treaty (MARPOL) and mandatory energy-efficiency measures) to address ship emissions. To ensure the effective implementation of such regulations and measures, air emission detection and monitoring has become crucial. In this paper, a dynamic multitarget path planning model is developed to realize multi-UAVs (Unmanned Aerial Vehicles) performing synergistic detection of ship emissions in ports. A path planning algorithm under a dynamic environment is developed to establish the model. This algorithm incorporates a Tabu table into particle swarm optimization (PSO) to improve its optimization ability, and it obtains the initial detection route of each UAV based on a “minimum ring” method. This paper describes a multi-UAVs synergistic algorithm to formulate the path reprogramming time in a dynamic environment by judging and cutting the “minimum ring”. This finding proves the improved efficiency of air pollution detection by UAVs. It provides useful insights for maritime and port authorities to detect ship emissions in practice and to ensure ship emission reduction for better air quality in the postpandemic era

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms

Mención Internacional en el título de doctorConvective weather poses a major threat that compromises the safe operation of flights while inducing delay and cost. The aircraft trajectory planning problem under thunderstorm evolution is addressed in this thesis, proposing two novel heuristic approaches that incorporate uncertainties in the evolution of convective cells. In this context, two additional challenges are faced. On the one hand, studies have demonstrated that given the computational power available nowadays, the best way to characterize weather uncertainties is through ensemble forecasting products, hence compatibility with them is crucial. On the other hand, for the algorithms to be used during a flight, they must be fast and deliver results in a few seconds. As a first methodology, three variants of the Scenario-Based Rapidly-Exploring Random Trees (SB-RRTs) are proposed. Each of them builds a tree to explore the free airspace during an iterative and random process. The so-called SB-RRT, the SB-RRT∗ and the Informed SB-RRT∗ find point-to-point safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. In a second instance, the Augmented Random Search (ARS) algorithm is used to sample trajectories from a directed graph and deform them iteratively in the search for an optimal path. The aim of such deformations is to adapt the initial graph to the unsafe set and its possible changes. In the end, the ARS determines the population of trajectories that, on average, minimizes a combination of flight time, time in storms, and fuel consumption Both methodologies are tested considering a dynamic model of an aircraft flying between two waypoints at a constant flight level. Test scenarios consist of realistic weather forecasts described by an ensemble of equiprobable members. Moreover, the influence of relevant parameters, such as the maximum number of iterations, safety margin (in SB-RRTs) or relative weights between objectives (in ARS) is analyzed. Since both algorithms and their convergence processes are random, sensitivity analyses are conducted to show that after enough iterations the results match. Finally, through parallelization on graphical processing units, the required computational times are reduced substantially to become compatible with near real-time operation. In either case, results show that the suggested approaches are able to avoid dangerous and uncertain stormy regions, minimize objectives such as time of flight, flown distance or fuel consumption and operate in less than 10 seconds.Los fenómenos convectivos representan una gran amenaza que compromete la seguridad de los vuelos, a la vez que incrementa los retrasos y costes. En esta tesis se aborda el problema de la planificación de vuelos bajo la influencia de tormentas, proponiendo dos nuevos métodos heurísticos que incorporan incertidumbre en la evolución de las células convectivas. En este contexto, se intentará dar respuesta a dos desafíos adicionales. Por un lado, hay estudios que demuestran que, con los recursos computacionales disponibles hoy en día, la mejor manera de caracterizar la incertidumbre meteorológica es mediante productos de tipo “ensemble”. Por tanto, la compatibilidad con ellos es crucial. Por otro lado, para poder emplear los algoritmos durante el vuelo, deben de ser rápidos y obtener resultados en pocos segundos. Como primera aproximación, se proponen tres variantes de los “Scenario-Based Rapidly-Exploring Random Trees” (SB-RRTs). Cada uno de ellos crea un árbol que explora el espacio seguro durante un proceso iterativo y aleatorio. Los denominados SB-RRT, SB-RRT∗ e Informed SB-RRT∗ calculan trayectorias entre dos puntos respetando un margen de seguridad impuesto por el usuario. Además, los dos últimos métodos convergen en soluciones de mínima distancia de vuelo. En segundo lugar, el algoritmo “Augmented Random Search” (ARS) se utiliza para muestrear trajectorias de un grafo dirigido y deformarlas iterativamente en busca del camino óptimo. El fin de tales deformaciones es adaptar el grafo inicial a las zonas peligrosas y a los cambios que puedan sufrir. Finalmente, el ARS calcula aquella población de trayectorias que, de media, minimiza una combinación del tiempo de vuelo, el tiempo en zonas tormentosas y el consumo de combustible. Ambas metodologías se testean considerando un modelo de avión volando punto a punto a altitud constante. Los casos de prueba se basan en datos meteorológicos realistas formados por un grupo de predicciones equiprobables. Además, se analiza la influencia de los parámetros más importantes como el máximo número de iteraciones, el margen de seguridad (en SB-RRTs) o los pesos relativos de cada objetivo (en ARS). Como ambos algoritmos y sus procesos de convergencia son aleatorios, se realizan análisis de sensibilidad para mostrar que, tras suficientes iteraciones, los resultados coinciden. Por último, mediante técnicas de paralelización en procesadores gráficos, se reducen enormemente los tiempos de cálculo, siendo compatibles con una operación en tiempo casi-real. En ambos casos los resultados muestran que los algoritmos son capaces de evitar zonas inciertas de tormenta, minimizar objetivos como el tiempo de vuelo, la distancia recorrida o el consumo de combustible, en menos de 10 segundos de ejecución.Programa de Doctorado en Ingeniería Aeroespacial por la Universidad Carlos III de MadridPresidente: Ernesto Staffetti Giammaria.- Secretario: Alfonso Valenzuela Romero.- Vocal: Valentin Polishchu

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention