Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions

The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, low motion-to-photon latency, and real-time tactile and control experience). However, the current communication systems fall short to satisfy these requirements. Mobile edge computing (MEC) has been indispensable to enable low latency and powerful computing. Moreover, the sixth generation (6G) networks promise to provide end users with high-capacity communications to MEC servers. In this paper, we bring together the primary components into a 6G mobile-edge framework to empower the Metaverse. This includes the usage of heterogeneous radios, intelligent reflecting surfaces (IRS), non-orthogonal multiple access (NOMA), and digital twins (DTs). We also discuss novel communication paradigms (i.e., semantic communication, holographic-type communication, and haptic communication) to further satisfy the demand for human-type communications and fulfil user preferences and immersive experiences in the Metaverse

Advances in flexible manipulation through the application of AI-based techniques

282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Guidance, navigation and control of multirotors

Aplicat embargament des de la data de defensa fins el dia 31 de desembre de 2021This thesis presents contributions to the Guidance, Navigation and Control (GNC) systems for multirotor vehicles by applying and developing diverse control techniques and machine learning theory with innovative results. The aim of the thesis is to obtain a GNC system able to make the vehicle follow predefined paths while avoiding obstacles in the vehicle's route. The system must be adaptable to different paths, situations and missions, reducing the tuning effort and parametrisation of the proposed approaches. The multirotor platform, formed by the Asctec Hummingbird quadrotor vehicle, is studied and described in detail. A complete mathematical model is obtained and a freely available and open simulation platform is built. Furthermore, an autopilot controller is designed and implemented in the real platform. The control part is focused on the path following problem. That is, following a predefined path in space without any time constraint. Diverse control-oriented and geometrical algorithms are studied, implemented and compared. Then, the geometrical algorithms are improved by obtaining adaptive approaches that do not need any parameter tuning. The adaptive geometrical approaches are developed by means of Neural Networks. To end up, a deep reinforcement learning approach is developed to solve the path following problem. This approach implements the Deep Deterministic Policy Gradient algorithm. The resulting approach is trained in a realistic multirotor simulator and tested in real experiments with success. The proposed approach is able to accurately follow a path while adapting the vehicle's velocity depending on the path's shape. In the navigation part, an obstacle detection system based on the use of a LIDAR sensor is implemented. A model of the sensor is derived and included in the simulator. Moreover, an approach for treating the sensor data to eliminate the possible ground detections is developed. The guidance part is focused on the reactive path planning problem. That is, a path planning algorithm that is able to re-plan the trajectory online if an unexpected event, such as detecting an obstacle in the vehicle's route, occurs. A deep reinforcement learning approach for the reactive obstacle avoidance problem is developed. This approach implements the Deep Deterministic Policy Gradient algorithm. The developed deep reinforcement learning agent is trained and tested in the realistic simulation platform. This agent is combined with the path following agent and the rest of the elements developed in the thesis obtaining a GNC system that is able to follow different types of paths while avoiding obstacle in the vehicle's route.Aquesta tesi doctoral presenta diverses contribucions relaciones amb els sistemes de Guiat, Navegació i Control (GNC) per a vehicles multirrotor, aplicant i desenvolupant diverses tècniques de control i de machine learning amb resultats innovadors. L'objectiu principal de la tesi és obtenir un sistema de GNC capaç de dirigir el vehicle perquè segueixi una trajectòria predefinida mentre evita els obstacles que puguin aparèixer en el recorregut del vehicle. El sistema ha de ser adaptable a diferents trajectòries, situacions i missions, reduint l'esforç realitzat en l'ajust i la parametrització dels mètodes proposats. La plataforma experimental, formada pel cuadricòpter Asctec Hummingbird, s'estudia i es descriu en detall. S'obté un model matemàtic complet de la plataforma i es desenvolupa una eina de simulació, la qual és de codi lliure. A més, es dissenya un controlador autopilot i s'implementa en la plataforma real. La part de control està enfocada al problema de path following. En aquest problema, el vehicle ha de seguir una trajectòria predefinida en l'espai sense cap tipus de restricció temporal. S'estudien, s'implementen i es comparen diversos algoritmes de control i geomètrics de path following. Després, es milloren els algoritmes geomètrics usant xarxes neuronals per convertirlos en algoritmes adaptatius. Per finalitzar, es desenvolupa un mètode de path following basat en tècniques d'aprenentatge per reforç profund (deep Reinforcement learning). Aquest mètode implementa l'algoritme Deep Deterministic Policy Gradient. L'agent intel. ligent resultant és entrenat en un simulador realista de multirotors i validat en la plataforma experimental real amb èxit. Els resultats mostren que l'agent és capaç de seguir de forma precisa la trajectòria de referència adaptant la velocitat del vehicle segons la curvatura del recorregut. A la part de navegació, s'implementa un sistema de detecció d'obstacles basat en l'ús d'un sensor LIDAR. Es deriva un model del sensor i aquest s'inclou en el simulador. A més, es desenvolupa un mètode per tractar les mesures del sensor per eliminar les possibles deteccions del terra. Pel que fa a la part de guiatge, aquesta està focalitzada en el problema de reactive path planning. És a dir, un algoritme de planificació de trajectòria que és capaç de re-planejar el recorregut del vehicle a l'instant si algun esdeveniment inesperat ocorre, com ho és la detecció d'un obstacle en el recorregut del vehicle. Es desenvolupa un mètode basat en aprenentatge per reforç profund per l'evasió d'obstacles. Aquest mètode implementa l'algoritme Deep Deterministic Policy Gradient. L'agent d'aprenentatge per reforç s'entrena i valida en un simulador de multirotors realista. Aquest agent es combina amb l'agent de path following i la resta d'elements desenvolupats en la tesi per obtenir un sistema GNC capaç de seguir diferents tipus de trajectòries, evadint els obstacles que estiguin en el recorregut del vehicle.Esta tesis doctoral presenta varias contribuciones relacionas con los sistemas de Guiado, Navegación y Control (GNC) para vehículos multirotor, aplicando y desarrollando diversas técnicas de control y de machine learning con resultados innovadores. El objetivo principal de la tesis es obtener un sistema de GNC capaz de dirigir el vehículo para que siga una trayectoria predefinida mientras evita los obstáculos que puedan aparecer en el recorrido del vehículo. El sistema debe ser adaptable a diferentes trayectorias, situaciones y misiones, reduciendo el esfuerzo realizado en el ajuste y la parametrización de los métodos propuestos. La plataforma experimental, formada por el cuadricoptero Asctec Hummingbird, se estudia y describe en detalle. Se obtiene un modelo matemático completo de la plataforma y se desarrolla una herramienta de simulación, la cual es de código libre. Además, se diseña un controlador autopilot, el cual es implementado en la plataforma real. La parte de control está enfocada en el problema de path following. En este problema, el vehículo debe seguir una trayectoria predefinida en el espacio tridimensional sin ninguna restricción temporal Se estudian, implementan y comparan varios algoritmos de control y geométricos de path following. Luego, se mejoran los algoritmos geométricos usando redes neuronales para convertirlos en algoritmos adaptativos. Para finalizar, se desarrolla un método de path following basado en técnicas de aprendizaje por refuerzo profundo (deep reinforcement learning). Este método implementa el algoritmo Deep Deterministic Policy Gradient. El agente inteligente resultante es entrenado en un simulador realista de multirotores y validado en la plataforma experimental real con éxito. Los resultados muestran que el agente es capaz de seguir de forma precisa la trayectoria de referencia adaptando la velocidad del vehículo según la curvatura del recorrido. En la parte de navegación se implementa un sistema de detección de obstáculos basado en el uso de un sensor LIDAR. Se deriva un modelo del sensor y este se incluye en el simulador. Además, se desarrolla un método para tratar las medidas del sensor para eliminar las posibles detecciones del suelo. En cuanto a la parte de guiado, está focalizada en el problema de reactive path planning. Es decir, un algoritmo de planificación de trayectoria que es capaz de re-planear el recorrido del vehículo al instante si ocurre algún evento inesperado, como lo es la detección de un obstáculo en el recorrido del vehículo. Se desarrolla un método basado en aprendizaje por refuerzo profundo para la evasión de obstáculos. Este implementa el algoritmo Deep Deterministic Policy Gradient. El agente de aprendizaje por refuerzo se entrena y valida en un simulador de multirotors realista. Este agente se combina con el agente de path following y el resto de elementos desarrollados en la tesis para obtener un sistema GNC capaz de seguir diferentes tipos de trayectorias evadiendo los obstáculos que estén en el recorrido del vehículo.Postprint (published version

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Edge Intelligence Simulator:a platform for simulating intelligent edge orchestration solutions

Abstract. To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions is a challenging and arduous task, where the evaluation and comparison of the proposed solution is a focal point. Simulation is commonly used to evaluate and compare proposed solutions. However, there does not currently exist openly available simulators that would have a specific focus on supporting the research on intelligent edge orchestration methods. This thesis presents a simulation platform called Edge Intelligence Simulator (EISim), the purpose of which is to facilitate the research on intelligent edge orchestration solutions. In its current form, the platform supports simulating deep reinforcement learning based solutions and different orchestration control topologies in scenarios related to task offloading and resource pricing on edge. The platform also includes additional tools for creating simulation environments, running simulations for agent training and evaluation, and plotting results. This thesis gives a comprehensive overview of the state of the art in edge and fog simulation, orchestration, offloading, and resource pricing, which provides a basis for the design of EISim. The methods and tools that form the foundation of the current EISim implementation are also presented, along with a detailed description of the EISim architecture, default implementations, use, and additional tools. Finally, EISim with its default implementations is validated and evaluated through a large-scale simulation study with 24 simulation scenarios. The results of the simulation study verify the end-to-end performance of EISim and show its capability to produce sensible results. The results also illustrate how EISim can help the researcher in controlling and monitoring the training of intelligent agents, as well as in evaluating solutions against different control topologies.Reunaälysimulaattori : alusta älykkäiden reunalaskennan orkestrointiratkaisujen simulointiin. Tiivistelmä. Älykkäiden ratkaisujen täytyy tulla olennaiseksi osaksi reunaympäristön resurssien hallinnointia, jotta tulevaisuuden vuorovaikutteisten ja älykkäiden sovellusten suoritusta voidaan tukea tasolla, joka täyttää sovellusten tiukat suoritusvaatimukset. Älykkäiden orkestrointiratkaisujen kehitys on vaativa ja työläs prosessi, jonka keskiöön kuuluu olennaisesti menetelmien testaaminen ja vertailu muita menetelmiä vasten. Simulointia käytetään tyypillisesti menetelmien arviointiin ja vertailuun, mutta tällä hetkellä ei ole avoimesti saatavilla simulaattoreita, jotka eritoten keskittyisivät tukemaan älykkäiden reunaorkestrointiratkaisujen kehitystä. Tässä opinnäytetyössä esitellään simulaatioalusta nimeltään Edge Intelligence Simulator (EISim; Reunaälysimulaattori), jonka tarkoitus on helpottaa älykkäiden reunaorkestrointiratkaisujen tutkimusta. Nykymuodossaan se tukee vahvistusoppimispohjaisten ratkaisujen sekä erityyppisten orkestroinnin kontrollitopologioiden simulointia skenaarioissa, jotka liittyvät laskennan siirtoon ja resurssien hinnoitteluun reunaympäristössä. Alustan mukana tulee myös lisätyökaluja, joita voi käyttää simulaatioympäristöjen luomiseen, simulaatioiden ajamiseen agenttien koulutusta ja arviointia varten, sekä simulaatiotulosten visualisoimiseen. Tämä opinnäytetyö sisältää kattavan katsauksen reunaympäristön simuloinnin, reunaorkestroinnin, laskennan siirron ja resurssien hinnoittelun nykytilaan kirjallisuudessa, mikä tarjoaa kunnollisen lähtökohdan EISimin toteutukselle. Opinnäytetyö esittelee menetelmät ja työkalut, joihin EISimin tämänhetkinen toteutus perustuu, sekä antaa yksityiskohtaisen kuvauksen EISimin arkkitehtuurista, oletustoteutuksista, käytöstä ja lisätyökaluista. EISimin validointia ja arviointia varten esitellään laaja simulaatiotutkimus, jossa EISimin oletustoteutuksia simuloidaan 24 simulaatioskenaariossa. Simulaatiotutkimuksen tulokset todentavat EISimin kokonaisvaltaisen toimintakyvyn, sekä osoittavat EISimin kyvyn tuottaa järkeviä tuloksia. Tulokset myös havainnollistavat, miten EISim voi auttaa tutkijoita älykkäiden agenttien koulutuksessa ja ratkaisujen arvioinnissa eri kontrollitopologioita vasten