The growing number of players in Massively Multiplayer Online games puts a heavy load on the network infrastructure and the general-purpose CPU of the game servers. A game server’s network stack processing needs equal treatment to the game-related processing ability. It is a fact that the networkcommunication tasks on the CPU reach the same order of magnitude as the game-related tasks, and the computing capability of the CPU can be a factor that limits the maximum number of players. Therefore, CPU offloading is becoming vital. FPGAs play an essential role in dedicated computation and network communication due to their superiority in flexibility and computation-oriented efficiency. Thus, an FPGA can be a good hardware platform to implement a network stack to replace the CPU in processing the network computations. However, most commercial and open-source network stack IPs support only one or few connections. This thesis project explores a network server on FPGA, implemented in RTL, that can handle multiple connections, specialized in the TCP protocol. The design in this project adds a cached memory hierarchy that provides a filter against port numbers of multiple connections from the same application and an Application Layer Controller, based on an open-source Ethernet, to increase the number of TCP connections further. A proof of concept was built, and its performance was tested. As a result, the TCP server on the FPGA was designed to handle a maximum of 40 configurable connections, but only 25 connections could be maintained during operation due to operational latency constraints. This FPGA server solution provides a latency of 1 ms in LAN. The babbling idiot and out-of-order packet transfer tests from clientswere also performed to guarantee robustness. During testing, poor performance in Packet Loss and Packet Error Handling was noted. In the future, this issue needs to be addressed. In addition, further investigations of methods for expanding the cache need to be done to allow handling more clients. / Det växande antalet spelare i Massively Multiplayer Online-spel belastar nätverksinfrastrukturen och spelservrarnas CPU:er. En spelservers förmåga att bearbeta nätverksstacken måste behandlas lika med den spelrelaterade bearbetningsförmågan. Det är ett faktum att nätverkskommunikationsuppgifterna på processorn når samma storleksordning som de spelrelaterade uppgifterna, och processorns beräkningsförmåga kan vara en faktor som begränsar det maximala antalet spelare. Därför blir avlastning av CPU-viktig. FPGA:er spelar en viktig roll i dedikerad beräkning och nätverkskommunikation på grund av dess överlägsenhet vad gäller flexibilitet och beräkningsorienterad effektivitet. Således kan en FPGA vara en bra hårdvaruplattform för att implementera en nätverksstack, för att ersätta CPU:n vid bearbetning av nätverksberäkningsarna. Men, de flesta kommersiella och öppna källkodsnätverksstack- IP:er stöder dock bara en eller ett fåtal anslutningar. Detta examensarbete utforskar en nätverksserver på FPGA, implementerad mha RTL, som kan hantera flera anslutningar, specialiserad på TCP-protokollet. Designen i detta projekt lägger till en cachad minneshierarki som ger ett filter mot portnummer för flera anslutningar från samma applikation och en Application Layer Controller, baserad på öppen källkod för Ethernet, för att öka antalet TCP-anslutningar ytterligare. Ett proof of concept byggdes och dess prestanda testades. Som ett resultat designades TCP-servern på FPGA:n att kunna hantera maximalt 40 konfigurerbara anslutningar, men endast 25 anslutningar kunde bibehållas under drift på grund av driftsfördröjningar. Denna FPGA-serverlösning ger en latens på 1 ms i LAN. Tester inkluderande den babblande idioten och out-of-order paketöverföring från klienter utfördes också för att garantera robusthet. Under testningen noterades dålig prestanda i paketförlust och paketfelshantering. I framtiden måste denna fråga åtgärdas. Dessutom behöver ytterligare undersökningar av metoder för att utöka cachen göras för att kunna hantera fler klienter.

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%).

Les technologies de l'Internet des objets (IdO) et de l'externalisation des services informatiques ont conduit à l'émergence de nouvelles menace sur la vie privée des utilisateurs. Cependant, l'implémentation des moyens de sécurité traditionnels sur les équipements de l'IdO constitue un premier défi lié aux limites de capacités. D'un autre côté, la délégation du traitement et du stockage des données, nous pose le problème de confiance envers les fournisseurs de service.Dans ce contexte, nous avons proposé une solution de chiffrement qui assure une protection de données centrée sur leurs propriétaires et adaptée à l'environnement contraignant des objets connectés. Ce modèle se base sur le chiffrement par attributs avec externalisation sécurisée et la technologie de la Blockchain. Ensuite, en réponse à la problématique de la confiance et de la sélection du service, nous avons exploré les possibilités offertes par les outils de l'intelligence artificielle. Pour ce faire, nous avons proposé un modèle de filtrage collaboratif basé sur les cartes de Kohonen avec une solution pour détecter les utilisateurs non fiable. / Internet of Things (IoT) and IT service outsourcing technologies have led to the emergence of new threats to users' privacy. However, the implementation of traditional security measures on IoT equipment is a first challenge due to capacity limitations. On the other hand, the offloading of data processing and storage poses the problem of trust in service providers.In this context, we have proposed an encryption solution that provides owner-centric data protection adapted to the constraining environment of IoT. This model is based on attribute-based encryption with secure offloading capability and Blockchain technology. Then, in response to the issue of trust and service selection, we explored the possibilities offered by artificial intelligence tools. To do this, we proposed a collaborative filtering model based on Kohonen maps and efficient solution to detect the untrusted users.

Um Systeme der fünften Generation zellularer Kommunikationsnetze (5G) zu ermöglichen, sind Energie effiziente Architekturen erforderlich, die eine zuverlässige Serviceplattform für die Bereitstellung von 5G-Diensten und darüber hinaus bieten können. Device Enhanced Edge Computing ist eine Ableitung des Multi-Access Edge Computing (MEC), das Rechen- und Speicherressourcen direkt auf den Endgeräten bereitstellt. Die Bedeutung dieses Konzepts wird durch die steigenden Anforderungen von rechenintensiven Anwendungen mit extrem niedriger Latenzzeit belegt, die den MEC-Server allein und den drahtlosen Kanal überfordern. Diese Dissertation stellt ein Berechnungs-Auslagerungsframework mit Berücksichtigung von Energie, Mobilität und Anreizen in einem gerätegestützten MEC-System mit mehreren Benutzern und mehreren Aufgaben vor, das die gegenseitige Abhängigkeit der Aufgaben sowie die Latenzanforderungen der Anwendungen berücksichtigt. / To enable fifth generation cellular communication network (5G) systems, energy efficient architectures are required that can provide a reliable service platform for the delivery of 5G services and beyond. Device Enhanced Edge Computing is a derivative of Multi-Access Edge Computing (MEC), which provides computing and storage resources directly on the end devices. The importance of this concept is evidenced by the increasing demands of ultra-low latency computationally intensive applications that overwhelm the MEC server alone and the wireless channel. This dissertation presents a computational offloading framework considering energy, mobility and incentives in a multi-user, multi-task device-based MEC system that takes into account task interdependence and application latency requirements.

Introduction: Instead of replacing existing analogue water meters with Internet of Things (IoT) connected substitutes, an alternative would be to attach an IoT connected module to the analogue water meter that optically reads the meter value using Optical Character Recognition (OCR). Such a module would need to be battery-powered given that access to the electrical grid is typically limited near water meters. Research has shown that offloading the OCR process can reduce the power dissipation from the battery, and that this dissipation can be reduced even further by reducing the amount of data that is transmitted.  Purpose: For the sake of minimising energy consumption in the proposed solution, the purpose of the study is to find out to what extent it is possible to reduce an input image’s file size by means of resolution, colour depth, and compression before the Google Cloud Vision OCR engine no longer returns feasible results.   Method and implementation: 250 images of analogue water meter values were processed by the Google Vision Cloud OCR through 38 000 different combinations of resolution, colour depth, and upscaling.  Results: The highest rate of successful OCR readings with a minimal file size were found among images within a range of resolutions between 133 x 22 to 163 x 27 pixels and colour depths between 1- and 2-bits/pixel.  Conclusion: The study shows that there is a potential for minimising data sizes, and thereby energy consumption, by offloading the OCR process by means of transmitting images of minimal file size.

Note-taking is ubiquitous. Whether we write down a grocery list, type our intentions for the day, or record a voice note for a friend, we often use notes to externalize our thoughts. We “delegate” this information to “in the world” extensions of our cognition, thus lightening our cognitive loads. The current thesis investigates the effects of note-taking as a form of cognitive offloading in an applied research setting. The note-taking literature is fragmented regarding practical note-taking recommendations for educators. The current thesis advances our knowledge and understanding of the crossover between cognitive offloading, note-taking, and individual differences. The laboratory research presented in this thesis uses novel materials that mimic the classroom environment, with future goals of translating this research into the actual classroom. Chapter 2 explores note-taking from a cognitive offloading perspective and demonstrates how differences in note-taking quantity affect recall. Chapter 3 showcases how differences in learning between note-taking modalities are seen sporadically and only when they intersect with the type of test. Chapter 4 investigates the importance of individual differences (e.g., working memory capacity) when exploring cognitive offloading and note-taking, and demonstrates how surface findings are not generalizable once we investigate underlying individual differences. While our research started as a way to understand how we offload information via note-taking and its effects on learning, we hope our findings and general discussion encourage the reader to explore the generalizability of applied cognitive research. Note-taking is a complex process, and our future work aims to investigate how learners differ and how we might disseminate research in education to be inclusive and diverse. / Thesis / Doctor of Philosophy (PhD) / Imagine a world without technology or external resources to express your thoughts: no “to-do” lists, calendar reminders, sketches, or notes. Since our memory capacities are limited, we often maintain a record of information “in the world”, as opposed to only using our limited memory stores. This form of mental delegation is known as “cognitive offloading”. One way we offload information for future access is through note-taking. In an educational setting, note-taking influences our ability to learn and review information. How we take notes, and their effects on learning, have been debated in the literature. This thesis explores the impact of cognitive offloading and note-taking on learning, and demonstrates the importance of exploring individual differences (e.g., memory capacity and note-taking preference) in applied educational research. Throughout this thesis, we prompt our audience to frame note-taking and cognitive research takeaways through an inclusive educational lens.

No description available.

State estimation filters are computationally intensive blocks used to calculate uncertain/unknown state values from noisy/not available sensor inputs in any autonomous systems. The inputs to the actuators depend on these filter’s output and thus the scheduling of filter has to be at very small time intervals. The aim of this thesis is to investigate the possibility of using hardware accelerators to perform this computation. To make a comparative study, 3 filters that predicts 4, 8 and 16 state information was developed and implemented in Arm real time and application purpose CPU, NVIDIA Quadro and Turing GPU, and Xilinx FPGA programmable logic. The execution, memory transfer time, and the total developement time to realise the logic in CPU, GPU and FPGA is discussed. The CUDA developement environment was used for the GPU implementation and Vivado HLS with SDSoc environment was used for the FPGA implementation. The thesis concludes that a hardware accelerator is needed if the filter estimates 16 or more state information even if the processor is entirely dedicated for the computation of filter logic. Otherwise, for a 4 and 8 state filter the processor shows similar performance as an accelerator. However, in a real time environment the processor is the brain of the system, so it has to give instructions to many other functions parallelly. In such an environment, the instruction and data caches of the processor will be disturbed and there will be a fluctuation in the execution time of the filter for every iteration. For this, the best and worst case processor timings are calculated and discussed. / Tillståndsberäkningsfilter är beräkningsintensiva block som används för att beräkna osäkra / okända tillståndsvärden från bullriga / ej tillgängliga sensoringångar i autonoma system. Ingångarna till manöverdonen beror på filterens utgång och därför måste schemaläggningen av filtret ske med mycket små tidsintervall. Syftet med denna avhandling är att undersöka möjligheten att använda hårdvaruacceleratorer för att utföra denna beräkning. För att göra en jämförande studie utvecklades och implementerades 3 filter som förutsäger information om 4, 8 och 16 tillstånd i realtid med applikationsändamålen CPU, NVIDIA Quadro och Turing GPU, och Xilinx FPGA programmerbar logik. Exekvering, minnesöverföringstid och den totala utvecklingstiden för att förverkliga logiken i båda hårdvarorna diskuteras. CUDAs utvecklingsmiljö användes för GPU-implementeringen och Vivado HLS med SDSoc-miljö användes för FPGA-implementering. Avhandlingen drar slutsatsen att en hårdvaru-accelerator behövs om filtret uppskattar information om mer än 16 tillstånd även om processorn är helt dedikerad för beräkning av filterlogik. För 4 och 8 tillståndsfilter, visar processorn liknande prestanda som en accelerator. Men i realtid är processorn hjärnan i systemet; så den måste ge instruktioner till många andra funktioner parallellt. I en sådan miljö kommer processorns instruktioner och datacacher att störas och det kommer att bli en fluktuation i exekveringstiden för filtret för varje iteration. För detta beräknas och diskuteras de bästa och värsta fallstiderna.

L'hétérogénéité et l'énergie sont deux considérations fondamentales pour les environnements informatiques ubiquitaires. Dans cette thèse, nous présentons notre approche pour gérer l'hétérogénéité et pour économiser l'énergie via des canevas de programmation intégrés. Pour gérer l'hétérogénéité, nous proposons une méthodologie et un support de programmation qui vise à faire communiquer les différentes entités de l’environnement ubiquitaire en utilisant le protocole SIP considéré alors comme un bus logique universel de communication. Nous avons intégré ce bus SIP dans le langage de description d’architecture DiaSpec développé par notre équipe Phoenix. Concernant la consommation d’énergie, nous proposons une méthodologie qui utilise les techniques d’offloading et de compression de données pour minimiser la consommation d'énergie des applications mobiles. Nous avons ainsi construit une stratégie d’aide à la conception au travers d’un outil qui permet de déterminer le meilleur mode d’exécution pour une tâche donnée que nous proposons d’intégrer dans le langage de description DiaSpec. / The topics of heterogeneity and energy are two fundamental considerations for pervasive computing environments. Inthis thesis, we describe our approach to manage heterogeneity and to handle energy concerns via a high-level programming framework.To manage heterogeneity, we describe a methodology and a programming support that use the SIP protocol as a universal communication bus in pervasive computing environments. Ourwork enables homogeneous communications between heterogeneous distributed entities. In doing so, we integrate the SIP communication bus into our programming framework. We rely on adeclarative language named DiaSpec to describe the architecture of pervasive applications. This description is passed to a generator for producing a Java programming framework dedicated to the application area. We leverage the generated framework with SIP adaptations to raise the abstraction level of SIP operations.We then present a classification of a wide variety of entities interms of features, capabilities and network connectors. Based on this classification, a methodology and a programming supportare described for connecting entities on the SIP communication bus. This work has been validated by applications using theSIP communication bus to coordinate widely varying entities,including serial-based sensors (RS232, 1-Wire), ZigBee devices,X10 devices, PDA, native SIP entities, and software components.Regarding the energy concerns, we describe a methodology that uses two strategies, namely computation offloading and data compression, to minimize energy cost of mobile applications.In doing so, we present an execution and transfer model for atask of a mobile application and define its five different stubs forthree program execution and data transfer modes. Based on this model and our two strategies, we construct a strategy scheme to determine the most efficient stub in terms of energy consumption.We then design the OffDeci tool, using this strategy scheme, toprovide energy feedback for the developer and to analyze thebalance between local and remote computing with consideration of data compression. Our experimental study demonstrates thefeasibility of the strategy scheme of our approach. Finally, weextend DiaSpec with declarations dedicated to manage energy concerns during the application design phase. We sketched the integration of this energy-handling declaration and OffDeci intoour high-level programming framework. This integration permitsto determine the best stub of a declared DiaSpec component interms of its energy cost.

Freqüentemente os profissionais da construção naval tomam decisões para selecionar os elementos que devem ser especificados na composição de determinado projeto. Além da experiência e do conhecimento necessários para optar por um caminho adequado, a escolha deve atender eficientemente ao problema proposto e às preferências do projetista. A seleção de componentes do projeto de estruturas flutuantes para exploração de campos de petróleo no mar envolve aspectos objetivos e subjetivos. O trabalho apresenta e aplica alguns métodos de auxílio à tomada de decisão possibilitando ao projetista aprimorar sua sensibilidade. O objetivo da pesquisa é apresentar de forma sumária as duas bases para a decisão sobre o projeto, que são os métodos de apoio e os requisitos que as instalações FPSO devem atender, fornecendo como resultado um procedimento que permitirá aos usuários a melhor escolha dos componentes e aumentar a sensibilidade dos projetistas na seleção entre as possíveis opções (fazer totalmente novo ou aproveitar o existente). O trabalho apresenta uma revisão dos princípios da análise da decisão, informações sobre métodos de apoio à decisão, os dados de entrada do problema proposto, o método de projetos navais, os requisitos básicos para construção de estruturas flutuantes de exploração de petróleo no mar e os requisitos básicos para uma instalação FPSO. Para a solução do problema se aplicaram os métodos de árvore de decisão, para a parte correspondente às decisões sob risco e o método de análise hierárquica AHP para as decisões tomadas sob certeza. / Frequently, naval construction engineers take decisions to select the elements which must specified for the composition of a determined project. Besides the necessary experience and the knowledge to choose a proper way, the option must fulfil efficiently the problem requirements and the preferences of the designer. The choice of components in the design off-shore structures for production in sea oilfields, involves objective and subjective aspects. This work introduces and uses a few methods of decision aids, helping the designer to improve his sensibility. The objective of this research is to present in a simple way two foundations to decide about the project, which are the aid decision methods and the requirements which the FPSO (Floating Production Storage and Offloading System) must fulfil, supplying a procedure that enables the designer to take the best choice of components and to increase his sensibility referring to the selection among the possible options, wich are to make a completely new FPSO or to use an existing one. This work offers a revision of the principles of the decision analysis theory, the data input of the problem, the classic naval design method, the basic requirements to build floating structures to off-shore oil extraction, and the basic requirements for a FPSO installation. To solve the problem the decision tree method, for decision under risk, and the AHP (Analytic Hierarchy Process), for decision under certainty, were applied.