Unmanned aerial vehicles (UAVs) attract increasing attention for various wireless network applications by using UAVs' reliable line-of-sight (LoS) paths in air-ground connections and their flexible placement and movement. As such, the wireless network architecture is becoming three-dimensional (3D), incorporating terrestrial and aerial network nodes, which is more dynamic than the traditional terrestrial communications network. Despite the UAVs' advantages of high LoS path probability and flexible mobility, the challenges of UAV communications need to be considered in the design of integrated air-ground networks, such as spectrum sharing, air-ground interference management, energy-efficient and cost-effective UAV-assisted communications. On the other hand, in wireless networks, users request not only reliable communication services but also execute computation-intensive and latency-sensitive tasks. As one of the enabling technologies in wireless networks, edge computing is proposed to offload users' computation tasks to edge servers to reduce users' latency and energy consumption. However, this requires efficient utilization of both communication resources and computation resources. Furthermore, integrating UAVs into edge computing networks brings many benefits, such as enhancing offloading ability and extending offloading coverage region. This dissertation makes a series of fundamental contributions to UAVs and edge computing in wireless networks that include: 1) Reliable UAV communications, 2) Efficient edge computing schemes, and 3) Integration of UAV and edge computing. In the first contribution, we investigate UAV spectrum access and UAV swarm-enabled aerial reconfigurable intelligent surface (SARIS) for achieving reliable UAV communications. On the one hand, we study a 3D spectrum sharing between device-to-device (D2D) and UAVs communications. Specifically, UAVs perform spatial spectrum sensing to opportunistically access the licensed channels occupied by the D2D communications of ground users. The results show that UAVs' optimal spatial spectrum sensing radius can be obtained given specific network parameters. On the other hand, we study the beamforming and placement design for SARIS networks in downlink transmissions. We consider that the direct links between the ground base station (BS) and mobile users are blocked due to obstacles in the urban environment. SARIS assists the BS in reflecting the signals to randomly distributed mobile users. The results show that the proposed SARIS network significantly improves the weighted sum-rate for ground users, and the placement design plays an essential role in the overall system performance. In the second contribution, we develop a joint communication and computation resource allocation scheme for vehicular edge computing (VEC) systems. The full channel state information (CSI) in VEC systems is not always available at roadside units (RSUs). The channel varies fast due to vehicles' mobility, and it is pretty challenging to estimate CSI and feed back the RSUs for processing the VEC algorithms. To address the above problem, we introduce a large-scale CSI-based partial computation offloading scheme for VEC systems. Using deep learning and optimization tools, we minimize the users' energy consumption while guaranteeing their offloading latency and outage constraints. The results demonstrate that the introduced resource allocation scheme can significantly reduce the total energy consumption of users compared with other computation offloading schemes. In the third contribution, we present novel frameworks for integrating UAVs to edge computing networks to achieve improved computing performance. We study mobile edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and user equipment (UE), where UAVs and GCAPs cooperatively provide computation resources for UEs. The resource allocation algorithm is developed based on the block coordinate descent method by optimizing the subproblems of users' association, power control, bandwidth allocation, computation capacity allocation, and UAV placement. The results show the advantages of the introduced iterative algorithm regarding the reduced total energy consumption of UEs. Finally, we highlight directions for future works to advance the research presented in this dissertation and discuss its broader impact across the wireless networks industry and standard-making. / Doctor of Philosophy / The fifth-generation (5G) cellular network aims to achieve a high data rate by having greater bandwidth, deploying denser networks, and multiplying the antenna links' capacity. However, the current wireless cellular networks are fixed on the ground and thus pose many disadvantages. Moreover, the improved system performance comes at the cost of increased capital expenditures and operating expenses in wireless networks due to the enormous energy consumption at base stations (BS) and user equipment (UE). More spectrum and energy-efficient yet cost-effective technologies need to be developed in next-generation wireless networks, i.e., beyond-5G or sixth-generation (6G) networks. Recently, unmanned aerial vehicle (UAV) has attracted significant attention in wireless communications. Due to UAVs' agility and mobility, UAVs can be quickly deployed to support reliable communications, resorting to its line-of-sight-dominated connections in the air-ground channels. However, the sufficient available spectrum for extensive UAV communications is scarce, and the co-channel interference in air-air and air-ground connections need to be considered in the design of UAV networks. In addition to users' communication requests, users also need to execute intensive computation tasks with specific latency requirements. As such, edge computing has been proposed to integrate wireless communications and computing by offloading users' computation tasks to edge servers in proximity, reducing users' computation energy consumption and latency. Besides, integrating UAVs into edge computing networks makes efficient offloading schemes by leveraging the advantages of UAV communications. This dissertation makes several contributions that enhance UAV communications and edge computing systems performance, respectively, and present novel frameworks for UAV-assisted three-dimensional (3D) edge computing systems. This dissertation addresses the fundamental challenges in UAV communications, including spectrum sharing, interference management, UAV 3D placement, and beamforming, allowing broadband, wide-scale, cost-effective, and reliable wireless connectivity. Furthermore, this dissertation focuses on the energy-efficient vehicular edge computing systems and mobile edge computing systems, where the UAVs are applied to achieve 3D edge computing systems. To this end, various mathematical frameworks and efficient joint communication and computation resource allocation algorithms are proposed to design, analyze, optimize, and deploy UAV and edge computing systems. The results show that the proposed air-ground integrated networks can deliver spectrum-and-energy-efficient yet cost-effective wireless services, thus providing ubiquitous wireless connectivity and green computation offloading in the future beyond-5G or 6G wireless networks.

Smart buildings are one of the most important emerging applications of Internet of Things (IoT). The astronomical growth in IoT devices, data generated from these devices and ubiquitous connectivity have given rise to a new computing paradigm, referred to as "Edge computing", which argues for data analysis to be performed at the "edge" of the IoT infrastructure, near the data source. The development of efficient Edge computing systems must be based on advanced understanding of performance benefits that Edge computing can offer. The goal of this work is to develop this understanding by examining the end-to-end latency and throughput performance characteristics of Smart building streaming IoT applications when deployed at the resource-constrained infrastructure Edge and to compare it against the performance that can be achieved by utilizing Cloud's data-center resources. This work also presents a real-time streaming application to detect and localize the footstep impacts generated by a building's occupant while walking. We characterize this application's performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck. / Master of Science / Among the various emerging applications of Internet of Things (IoT) are Smart buildings, that allow us to monitor and manipulate various operating parameters of a building by instrumenting it with sensor and actuator devices (Things). These devices operate continuously and generate unbounded streams of data that needs to be processed at low latency. This data, until recently, has been processed by the IoT applications deployed in the Cloud at the cost of high network latency of accessing Cloud’s resources. However, the increasing availability of IoT devices, ubiquitous connectivity, and exponential growth in the volume of IoT data has given rise to a new computing paradigm, referred to as “Edge computing”. Edge computing argues that IoT data should be analyzed near its source (at the network’s Edge) in order to eliminate high latency of accessing Cloud for data processing. In order to develop efficient Edge computing systems, an in-depth understanding of the trade-offs involved in Edge and Cloud computing paradigms is required. In this work, we seek to understand these trade-offs and the potential benefits of Edge computing. We examine end to-end latency and throughput performance characteristics of Smart building streaming IoT applications by deploying them at the resource-constrained Edge and compare it against the performance that can be achieved by Cloud deployment. We also present a real-time streaming application to detect and localize the footstep impacts generated by a building’s occupant while walking. We characterize this application’s performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck.

Edge computing facilitates applications of cyber-physical systems that require low latencies by moving compute and storage resources closer to the end application. Whilst the edge network benefits such systems in terms of responsiveness, it increases the systems’ complexity due to edge devices’ often heterogeneous and resource-constrained nature. In this work, we evaluate whether WebAssembly can be used as a lightweight and portable abstraction layer for such applications. Through the implementation of an edge network robot control scenario, we benchmark and compare the performance of WebAssembly against its native equivalent. We measure WebAssembly’s overhead and assess the impact of different placement options in the network. We further compare the overall application responsiveness against the latency requirements of an industrial application to evaluate its performance. We find that WebAssembly satisfies the portability and performance requirements of the selected industrial use case. Our empirical results show that WebAssembly doubles the execution latency in a localized setting, but does not excessively impact the overall responsiveness of a cyber-physical system. / Edge computing underlättar tillämpningar av cyberfysiska system som kräver låga latenser genom att flytta beräknings- och lagringsresurser närmare slutapplikationen. Även om edge-nätverket gynnar sådana system när det gäller reaktionsförmåga, ökar det systemens komplexitet på grund av edge-enheternas ofta heterogena och resursbegränsade natur. I detta arbete utvärderar vi om WebAssembly kan användas som ett lättviktigt och portabelt abstraktionslager för sådana applikationer. Genom att implementera ett robotkontrollscenario för edge-nätverk benchmarkar och jämför vi prestandan hos WebAssembly med dess inbyggda motsvarighet. Vi mäter WebAssemblys overhead och utvärderar effekten av olika placeringsalternativ i nätverket. Vi jämför även den övergripande applikationsresponsen mot latenskraven i en industriell applikation för att utvärdera dess prestanda. Vi konstaterar att WebAssembly uppfyller portabilitets- och prestandakraven för det utvalda industriella användningsfallet. Våra empiriska resultat visar att WebAssembly fördubblar exekveringslatensen i en lokaliserad miljö, men att det inte påverkar den övergripande responsiviteten i ett cyberfysiskt system i alltför hög grad.

Edge computing has emerged as a paradigm to support the growing demand for real-time processing of data generated at the edge of the network. As the devices at the edge are constrained, one of the challenges in the area is how to schedule workloads. The scheduling problem is difficult to tackle due to the multitude of sources from which variables originate, diverse algorithms and execution methods, and tasks involving information dissemination and action execution. This project aims to explore the problem and implement a system that simplifies the construction of a scheduler for the edge computing to reduce the cognitive load on developers that work on the area and focus their attention on their expertise area. To construct the solution, a literature review is conducted, a set of functional and non functional requirements are proposed, an implementation using a Kubernetes operator and Python application is performed, and an evaluation and validation of the solution against the requirements and an use case and test case are performed. The results demonstrate that the system generates customized instances capable of receiving any number of inputs, outsources the execution of the logic and interacts with different outputs. This allows developers to rapidly deploy instances for their own needs, focusing on their domain of expertise. / Edge computing har framträtt som ett paradigm för att stödja den växande efterfrågan på realtidsbehandling av data som genereras vid nätverkets kant. Eftersom enheterna vid kanten är begränsade utgör en av utmaningarna inom området hur arbetsbelastningar ska schemaläggas. Schemaläggningsproblemet är svårt att hantera på grund av den mångfald av källor varifrån variabler härstammar, varierande algoritmer och utförandemetoder samt uppgifter som involverar informationsförmedling och utförande av åtgärder. Detta projekt syftar till att utforska problemet och implementera ett system som förenklar konstruktionen av en schemaläggare för kantberäkning för att minska den kognitiva belastningen på utvecklare som arbetar inom området och fokusera deras uppmärksamhet på deras expertområde. För att konstruera lösningen genomförs en litteraturgenomgång, en uppsättning funktionella och ickefunktionella krav föreslås, en implementation med hjälp av en Kubernetesoperatör och en Python-applikation utförs, och en utvärdering och validering av lösningen gentemot kraven, inklusive både användnings- och testfall, genomförs. Resultaten visar att systemet genererar anpassade instanser som kan ta emot vilket antal inmatningar som helst, outsourcar utförandet av logiken och interagerar med olika utgångar. Detta gör det möjligt för utvecklare att snabbt distribuera instanser för sina egna behov och fokusera på sitt expertområde.

Replication primarily provides data availability by having multiple copies over different systems and is exploited to make distributed systems scalable in num- bers and geographical areas. Placing a replica closer to the source of request can also significantly reduce the time required to service the request, improv- ing applications’ performance. However, modifications done at a single copy need to be propagated to all the standing copies to maintain the data’s consis- tency. Over the years, numerous strategies have been proposed for handling the tradeoff between consistency and availability, of which the majority pro- vides either strong consistency or eventual consistency. These models do not provide sufficient compatibility for developing modern applications for geo- distributed (edge) environments.Conflict-Free Replicated Data Types (CRDT) provides a new model of consistency referred to as strong eventual consistency. In principle, CRDTs guarantee conflict-free merge even when the updates arrive out of order using simple mathematical properties. Lasp is a coordination free distributed pro- gramming model for building modern distributed applications using CRDTs. Lasp uses a gossip protocol for disseminating state changes to all replicas in the system. The current implementation of gossip in Lasp is agnostic to the application’s behavior in propagating the updates efficiently to critical repli- cas in the system. In the thesis, we introduce an application-specific feature to optimize the dissemination of updates in Lasp. The proposed algorithm propagates the updates by catering to the different consistency requirements of the replicas in the system. The experimental results on a topology of 100 replicas found that the update latency at critical replicas with high consistency requirements is reduced by 40–50%, and the total bandwidth consumption in the system is reduced by 4–8% without significant repercussion on other repli- cas in the system. / Datareplikering erbjuder primärt tillgänglighet genom att tillhandahålla mul- tipla kopior fördelat över olika system, och utnyttjas för att göra distribuerade system skalbara i antal och över geografiska områden. Att placera en replika nära källan till en förfrågan kan dessutom signifikant reducera tiden det krävs att besvara förfrågan vilket förbättrar applikationens prestanda. Modifikatio- ner gjorda på en av kopiorna måste dock propageras till alla stående kopior för att upprätthålla datans konsistens . Över tid har många strategier föreslagits för att hantera avvägningen mellan konsistens och tillgänglighet, där majorite- ten erbjuder antingen stark eller eventuell konsistens. Dessa modeller erbjuder inte tillräcklig kompatibilitet för utveckling av moderna applikationer för geo- distribuerade (edge) miljöer.Konfliktfria replikerade datatyper (CRDT) erbjuder en ny modell av konsi- stens som kallas stark eventuell konsistens. I princip garanterar CRDTer kon- fliktfria sammanslagningar trots att uppdateringar sker i oordning, genom att använda dess matematiska egenskaper. Lasp är en koordineringsfri distribue- rad programmeringsmodell för att bygga moderna distribuerade applikationer med hjälp av CRDTer. Lasp använder skvallerprotokoll för att sprida tillstånds- förändringar till alla replikor i systemet. Den nuvarande implementationen av skvaller i Lasp är agnostiskt för applikationens beteende relaterat till effektiv propagering av uppdateringar till kritiska replikor i systemet. I det här exa- mensarbetet introducerar vi applikationsspecifik funktionalitet för att optime- ra spridandet av uppdateringar i Lasp. Den föreslagna algoritmen sprider upp- dateringarna genom att tillgodose de olika konsistenskraven för replikorna i systemet. Experimentella resultat i en topologi av 100 replikor visade att upp- dateringslatensen vid kritiska replikor med höga konsistenskrav minskas med 40–50% och att den totala bandbreddskonsumtionen i systemet minskas med 4–8% utan signifikanta negativa följder för andra replikor i systemet.

Edge computing can provide decentralized computation and storage resources with low latency and high bandwidth. It is a promising infrastructure to host services with stringent latency requirements, for example autonomous driving, cloud gaming, and telesurgery to the customers. Because of the structural complexity associated with the edge computing applications, research topics like service placement gain great importance. To provide a realistic and efficient general environment for evaluating service placement solutions that can be used to analyze latency requirements of services at scale, a new testing tool for mobile edge cloud is designed and implemented in this thesis. The proposed tool is implemented as a cloud native application, and allows deploying applications in an edge computing infrastructure that consists of Kubernetes and Istio, it can be easily scaled up to several hundreds of microservices, and deployment into the edge clusters is automated. With the help of the designed tool, two different microservice placement algorithms are evaluated in an emulated edge computing environment based on Federated Kubernetes. The results have shown how the performance of algorithms varies when the parameters of the environment, and the applications instantiated and deployed by the tool are changed. For example, increasing the request rate 200% can increase the delay by 100% for different algorithms. Moreover, complicating the mobile network can improve the latency performance up to 20% depending on the microservice placement algorithm. / Edge computing kan ge decentraliserad beräkning och lagringsresurser med låg latens och hög bandbredd. Det är en lovande infrastruktur för att vara värd för tjänster med strängt prestandakrav, till exempel autonom körning, molnspel och telekirurgi till kunderna. På grund av den strukturella komplexiteten som är associerad med edge computing applikationerna, får forskningsämnen som tjänsteplacering stor betydelse. För att tillhandahålla en realistisk och effektiv allmän miljö för utvärdering av lösningar för tjänsteplacering, designas och implementeras ett nytt testverktyg för mobilt kantmoln i denna avhandling. Det föreslagna verktyget implementeras på molnmässigt sätt som gör det möjligt att distribuera applikationer i en edge computing-infrastruktur som består av Kubernetes och Istio. Med hjälp av det konstruerade verktyget utvärderas två olika placeringsalgoritmer för mikrotjänster i en realistisk edge computing miljö. Resultaten visar att en ökning av förfrågningsgraden 200 % kan öka förseningen med 100 % för olika algoritmer. Dessutom kan komplicering av mobilnätet förbättra latensprestanda upp till 20% beroende på algoritmen för mikroserviceplaceringen.

The architecture of the network is undergoing a series of structural changes from the core network to the user to pave the way for 5G. New infrastructure elements are being massively deployed, thus making 5G more heterogeneous. This emerging paradigm, along with new services and handheld devices, creates a massive, highly mobile, heterogeneous environment with hard constraints in throughput, latency, resilience, and power consumption. This dissertation presents Agile MEC (AMEC), a shift in the concept of MEC to support user's mobility with the rapid relocation of services; and Network-coded Cooperation (NCC), a new system for massive content distribution in cellular networks. In summary, AMEC provides a mobility framework that reliably reduces the latency and power consumption in the system, and NCC improves network throughput, network resilience, and power consumption by offloading cellular traffic to underlay networks. / Die Architektur des Netzes durchläuft eine Reihe von strukturellen Veränderungen vom Kernnetz bis zum Benutzer, um den Weg für 5G zu ebnen. Neue Infrastruktur Elemente werden massiv eingesetzt, wodurch 5G heterogener wird. Dieses aufkommende Paradigma bildet zusammen mit neuen Diensten und Handheld-Geräten eine massive, hochmobile, heterogene Umgebung mit harten Einschränkungen in Bezug auf Durchsatz, Latenz, Belastbarkeit und Stromverbrauch. In dieser Dissertation werden Agile MEC (AMEC), eine Verschiebung des MEC-Konzepts zur Unterstützung der Mobilität der Benutzer durch die schnelle Verlagerung von Diensten, und Network-coded Cooperation (NCC), ein neues System zur massiven Verteilung von Inhalten in zellularen Netzwerken, vorgestellt. Zusammenfassend lässt sich sagen, dass AMEC einen Mobilitätsrahmen bietet, der die Latenzzeit und den Stromverbrauch im System zuverlässig reduziert, und NCC verbessert den Netzwerkdurchsatz, die Netzwerkstabilität und den Stromverbrauch, indem es den zellularen Datenverkehr auf unterlagerte Netzwerke verlagert.

With an increasing number of people currently living in urban areas, many cities around the globe are faced with issues such as increased pollution and traffic congestion. In an effort to tackle such challenges, governments and city councils are formulating new and innovative strategies. The integration of ICT with these strategies creates the concept of smart cities. The Internet of Things (IoT) is a key driver for smart city initiatives, making it necessary to have an IT infrastructure that can take advantage of the many benefits that IoT can provide. The Cloudlet is a new infrastructure model that offers cloud-computing capabilities at the edge of the mobile network. This environment is characterized by low latency and high bandwidth, constituting a novel ecosystem where network operators can open their network edge to third parties, allowing them to flexibly and rapidly deploy innovative applications and services towards mobile subscribers. In this thesis, we present a cloudlet architecture that leverages edge computing to provide a platform for IoT devices on top of which many smart city applications can be deployed. We first provide an overview of existing challenges and requirements in IoT systems development. Next, we analyse existing cloudlet solutions. Finally, we present our cloudlet architecture for IoT, including design and a prototype solution. For our cloudlet prototype, we focused on a micro-scale emission model to calculate the CO2 emissions per individual trip of a vehicle, and implemented the functionality that allows us to read CO2 data from CO2 sensors. The location data is obtained from an Android smartphone and is processed in the cloudlet. Finally, we conclude with a performance evaluation. / Med en befolkning som ökar i urbana områden, står många av världens städer inför utmaningar som ökande avgaser och trafikstockning. I ett försök att tackla sådana utmaningar, formulerar regeringar och stadsfullmäktige nya och innovativa strategier. Integrationen av ICT med dessa strategier bildar konceptet smart cities. Internet of Things (IoT) är en drivande faktor för smart city initiativ, vilket gör det nödvändigt för en IT infrastruktur som kan ta till vara på de många fördelar som IoT bidrar med. Cloudlet är en ny infrastrukturell modell som erbjuder datormolnskompetens i mobilnätverkets edge. Denna miljö karakteriseras av låg latens och hög bandbredd, utgörande ett nytt ekosystem där nätverksoperatörer kan hålla deras nätverks-edge öppet för utomstående, vilket tillåter att flexibelt och snabbt utveckla innovativa applikationer och tjänster för mobila subskribenter. I denna avhandling presenterar vi en cloudlet-arkitektur som framhäver edge computing, för att förse en plattform för IoT utrustning där många smart city applikationer kan utvecklas. Vi förser er först med en överblick av existerande utmaningar och krav i IoT systemutveckling. Sedan analyserar vi existerande cloudlet lösningar. Slutligen presenteras vår cloudlet arkitektur för IoT, inklusive design och en prototyplösning. För vår cloudlet-prototyp har vi fokuserat på en modell av mikroskala för att räkna ut CO2 emissioner per enskild resa med fordon, och implementerat en funktion som tillåter oss att läsa CO2 data från CO2 sensorer. Platsdata är inhämtad med hjälp av en Android smartphone och behandlas i cloudlet. Det hela sammanfattas med en prestandaevaluering.

In Human-Robot Collaboration scenarios, safety must be ensured by a risk management process that requires the execution of computationally expensive perception models (e.g., based on computer vision) in real-time. However, robots usually have constrained hardware resources that hinder timely responses, resulting in unsafe operations. Although Multi-access Edge Computing allows robots to offload complex tasks to servers on the network edge to meet real-time requirements, this might not always be possible due to dynamic changes in the network that can cause congestion or failures. This work proposes a safety-based task offloading strategy to address this problem. The goal is to intelligently use edge resources to reduce delays in the risk management process and consequently enhance safety. More specifically, depending on safety and network metrics, a Reinforcement Learning (RL) solution is implemented to decide whether a less accurate model should run locally on the robot or a more complex one should run remotely on the network edge. A third possibility is to reuse the previous output through verification of temporal coherence. Experiments are performed in a simulated warehouse scenario where humans and robots have close interactions. Results show that the proposed RL solution outperforms the baselines in several aspects. First, the edge is used only when the network performance is good, reducing the number of failures (up to 47%). Second, the latency is also adapted to the safety requirements (risk X latency reduced up to 48%), avoiding unnecessary network congestion in safe situations and letting other robots in hazardous situations use the edge. Overall, the latency of the risk management process is largely reduced (up to 68%), and this positively affects safety (time in safe zone increased up to 3:1%). / I scenarier med människa-robotkollaboration måste säkerheten säkerställas via en riskhanteringsprocess. Denna process kräver exekvering av beräkningstunga uppfattningsmodeller (t.ex. datorseende) i realtid. Robotar har vanligtvis begränsade hårdvaruresurser vilket förhindrar att respons uppnås i tid, vilket resulterar i osäkra operationer. Även om Multi-access Edge Computing tillåter robotar att avlasta komplexa uppgifter till servrar på edge, för att möta realtidskraven, så är detta inte alltid möjligt på grund av dynamiska förändringar i nätverket som kan skapa överbelastning eller fel. Detta arbete föreslår en säkerhetsbaserad uppgiftsavlastningsstrategi för att hantera detta problem. Målet är att intelligent använda edge-resurser för att minska förseningar i riskhanteringsprocessen och följaktligen öka säkerheten. Mer specifikt, beroende på säkerhet och nätverksmätvärden, implementeras en Reinforcement Learning (RL) lösning för att avgöra om en modell med mindre noggrannhet ska köras lokalt eller om en mer komplex ska köras avlägset på edge. En tredje möjlighet är att återanvända sista utmatningen genom verifiering av tidsmässig koherens. Experimenten utförs i ett simulerat varuhusscenario där människor och robotar har nära interaktioner. Resultaten visar att den föreslagna RL-lösningen överträffar baslinjerna i flera aspekter. För det första används edge bara när nätverkets prestanda är bra, vilket reducerar antal fel (upp till 47%). För det andra anpassas latensen också till säkerhetskraven (risk X latens reducering upp till 48%), undviker onödig överbelastning i nätverket i säkra situationer och låter andra robotar i farliga situationer använda edge. I det stora hela reduceras latensen av riskhanterings processen kraftigt (upp till 68%) och påverkar på ett positivt sätt säkerheten (tiden i säkerhetszonen ökas upp till 4%).

La technologie et les conducteurs socio-économiques créent les conditions d'une transformation profonde, appelée "Softwarization", du Telco et des TIC. Réseaux définis par logiciel et réseau Fonctions de virtualisation sont deux des principales technologies permettant ouvrant la voie à cette transformation. Softwarization permettra de virtualiser toutes les fonctions de réseau et de services d'une infrastructure de Telco et de les exécuter sur une plates-formes logicielles, entièrement découplés de l'infrastructure physique sous (presque basé sur du matériel standard). Tous les services seront fournis en utilisant un «continuum» des ressources virtuelles (traitement, de stockage et de communication) avec un investissement en capital initial pratiquement très limité et avec des coûts d'exploitation modestes. 5G sera la première exploitation de Softwarization. 5G sera une infrastructure distribuée massivement dense, intégrant le traitement, le stockage et (fixes et radio) des capacités de mise en réseau. En résumé, l'objectif général de cette thèse a étudié les défis techniques et les opportunités d'affaires apportées par le "Softwarization" et 5G. En particulier, la thèse propose que le 5G devra avoir une sorte de système d'exploitation (5GOS) capable de fonctionner les RAN et de base et les infrastructures fixes convergés. Les contributions de cette thèse ont été: 1) définir une vision pour les futures infrastructures 5G, des scénarios, des cas d'utilisation et les exigences principales: 2) définissant l'architecture fonctionnelle d'un système d'exploitation pour 5G; 3) la conception de l'architecture logicielle d'un 5GOS pour le "bord Cloud"; 4) comprendre les impacts technico-économiques de la vision et 5GOS, et les stratégies les plus efficaces pour l'exploiter / Technology and socio-economic drivers are creating the conditions for a profound transformation, called “Softwarization”, of the Telco and ICT. Software-Defined Networks and Network Functions Virtualization are two of the key enabling technologies paving the way towards this transformation. Softwarization will allow to virtualize all network and services functions of a Telco infrastructure and executing them onto a software platforms, fully decoupled from the underneath physical infrastructure (almost based on standard hardware). Any services will be provided by using a “continuum” of virtual resources (processing, storage and communications) with practically very limited upfront capital investment and with modest operating costs. 5G will be the first exploitation of Softwarization. 5G will be a massively dense distributed infrastructure, integrating processing, storage and (fixed and radio) networking capabilities. In summary, the overall goal of this thesis has been investigating technical challenges and business opportunities brought by the “Softwarization” and 5G. In particular, the thesis proposes that the 5G will have to have a sort of Operating System (5GOS) capable of operating the converged fixed and RAN and core infrastructures. Main contributions of this thesis have been: 1) defining a vision for future 5G infrastructures, scenarios, use-cases and main requirements; 2) defining the functional architecture of an Operating System for 5G; 3) designing the software architecture of a 5G OS for the “Edge Cloud”; 4) understanding the techno-economic impacts of the vision and 5GOS, and the most effective strategies to exploit it