Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

With the development of 5G technology and edge computing, more and more network application services have been migrated to the cloud network in order to improve the performance, availability and ensure Quality of Service. Edge computing has essentially changed the service deployment model and reduce the latency further for better customer experience, which is realized by deploying network service replicas in geographically distributed edge sites. However, how to discover edge application servers and select a proper instance to serve the edge users becomes an important research topic. This master thesis project addresses the problem by leveraging DNS based service selection mechanism, designing and implementing stable match based service selection algorithms with the aim of minimizing latency between edge users and services and balance the load among edge sites, and integrating the solutions by RESTful APIs. To evaluate the performance of the service selection algorithms, a set of experiments are carried on different simulated topologies with different traffic pattern. The experimental results show that the stable match algorithm and its variants can significantly reduce the average latency by up to 50% compared to traditional approaches, while the enhanced stable match based algorithms are able to have the same load balancing effect with the widely used Round Robin algorithm. / Med utvecklingen av 5G-teknik och edge computing har fler nätverkstjänster migrerats till molnätet för att förbättra prestanda, tillgänglighet och säkerställa servicekvalitet. Edge computing har i huvudsak förändrat tjänster distribution modellen och minskat latensen ytterligare för bättre kundupplevelse, vilket realiseras genom att distribuera nätverkstjänstreplikat på geografiskt distribuerade kantsajter. Hur man upptäcker kantappservrar och väljer en rätt instans för att betjäna kantanvändarna blir dock ett viktigt forskningsämne. Detta projekt löser problemet genom att utnyttja DNS-baserad mekanism för tjänstval, designa och implementera stabila matchbaserade algoritmer för tjänsteval i syfte att minimera latens mellan kantanvändare och tjänster och balansera belastningen mellan kantsajter och integrera lösningarna med RESTful API:er. För att utvärdera prestandan för algoritmerna för val av tjänster utförs en uppsättning experiment på olika simulerade topologier med olika trafikmönster. De experimentella resultaten visar att den stabila matchningsalgoritmen och dess varianter avsevärt kan minska den genomsnittliga latensen med upp till 50% jämfört med traditionella metoder, medan de förbättrade stabila matchbaserade algoritmerna kan ha samma belastningsbalanseringseffekt med den mycket använda Round Robin algoritm.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increases energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices causes a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

Industrial automation is critical in today's industry. The majority of new scientific and technological advancements are either enabling technologies or industrial automation application areas. In the past, the two main forms of control systems were distributed control systems (DCS) and programmable logic controllers (PLCs). PLCs have been referred as the "brain" of production systems because they provide the capacity to meet interoperability, reconfigurability, and portability criteria. Today's industrial automation systems rely heavily on control software to ensure that the automation process runs smoothly and efficiently. Furthermore, requirements like flexibility, adaptability, and robustness add to the control software's complexity. As a result, new approaches to building control software are required. The International Electrotechnical Commission attempted to meet these new and impending demands with the new IEC 61499 family of standards for distributed automation systems. The IEC 61499 standard specifies a high-level system design language for distributed data and control. With the advancement of these technologies like edge/fog computing and IIoT, how the control software in future smart factory managed is discussed here. This study aims to do a systematic literature review on PLC, software containers, edge/fog computing and IIoT for future industrial use. The objective is to identify the correspondence between the functional block (IEC 61499) and the container technology such as Docker. The impact of edge computing and the internet of things in industrial automation is also analysed. Since the aim is to do a comparative study, a qualitative explorative study is done, with the purpose to gather rich insight about the field. The analysis of the study mainly focused on four major areas such as deployment, run time, performance and security of these technologies. The result shows that containerisation or container based solutions is the basis for future automation as it outperforms virtual machines in terms of deployment, run time, performance and security.

Edge computing emerged as an attractive method of distributing computational resources in a network. When compared with cloud computing, edge computing presents a number of key benefits which include improved response times, scalability, privacy, and redundancy. This makes edge computing desirable for use in mobile robotics, in which low response times and redundancy are key issues. This thesis work will cover the design and implementation of a general-purpose edge orchestrator, that can support a wide range of domains due to being built around the concept of modularity. An edge orchestrator is a program that manages an edge network by analyzing the edge network and the requirements of devices within that network, then optimizing how the computational resources are distributed within the devices in the network. Modules have been designed and implemented on top of the orchestrator that allow for optimizations specific to mobile robotics. A proof of concept module was designed to optimize for latency which was compared with an external algorithm that seeks to optimize for latency as well. Both were implemented on the orchestrator and an evaluation was performed to compare both approaches. It was found that the module designed in this thesis is better suited for optimizing for latency. LXD was chosen to be used for software packaging which is a container-based software packaging solution. A software packaging solution is used to package software which would be deployed by the orchestrator. The choice of LXD is analyzed through an evaluation procedure that compares it with Docker, which is another container-based software packaging solution. It was found that LXD produces containers of smaller size but required more time to generate those containers, when compared with Docker. It was also found that LXD container images exhibited better performance than the Docker ones for software which is not I/O heavy. It was decided through this evaluation that LXD was a better choice for the orchestrator. / Edge computing är en attraktiv metod för distribution av beräkningsresurser i ett nätverk. Jämfört med molnberäkningar har edge computing ett antal viktiga fördelar som inkluderar förbättrade svarstider, skalbarhet, integritet och redundans. Detta gör edge computing önskvärt för användning i mobil robotik, där låga svarstider och redundans är viktiga frågor. Detta examensarbete täcker min design och implementering av en generell edge-orkestrerare, som kan stödja ett brett spektrum av domäner eftersom den är byggd på ett modulärt sätt. En edge-orkestrerare är ett program som hanterar ett edge-nätverk genom att analysera edge-nätverket och kraven på enheter inom det nätverket, för att sedan optimera hur beräkningsresurserna fördelas över enheterna i nätverket. Jag har utformat och implementerat moduler ovanpå orkestratorn som möjliggör optimeringar specifika för mobil robotik. Jag designade också en koncepttest-modul för att optimera för latens, vilken jag jämförde med en extern algoritm som även den försöker optimera för latens. Jag implementerade båda på orkestratorn och utförde en utvärdering för att jämföra båda metoderna. Resultaten visar att modulen utformad i detta examensarbete är bättre lämpad för att optimera för latens. För mjukvarupaketering valde jag att använda LXD, vilket är en containerbaserad mjukvarupaketeringslösning. Dess syfte är att paketera programvara som ska distribueras av orkestratorn. Jag analyserade valet av LXD genom ett utvärderingsförfarande som jämför det med Docker, som är en annan containerbaserad mjukvarupaketeringslösning. Jag fann att LXD producerar mindre containrar, men krävde mer tid för att generera dessa containrar jämfört med Docker. Jag fann också att LXD-containerbilder visade bättre prestanda än Docker-bilderna för programvara som inte är I/O-intensiv. Jag fann genom denna utvärdering att LXD var ett bättre val för orkestratorn.

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation. / <p>Note: The papers are not included in the fulltext online</p>

This thesis tackles the topic of serving IoT applications in the computing continuum. It proposes an approach to place applications in the tiers of the continuum, considering latency and energy as predefined metrics. It presents a system model to represent the computing continuum environment, and then, defines an optimization function that is tailored to meet the specific requirements of the IoT applications. The optimization function addresses the relationship between latency and energy consumption in the framework of IoT service provision, and it is implemented in two different directions: (1) the first direction uses a modified Genetic algorithm, and (2) the second direction utilizes the Machine learning concept. To evaluate the performance of the proposed approach, we incorporate different testbed setups and network configurations. All the setups and configurations are designed to represent the diverse demands of IoT applications. Then, different algorithms (such as Non-dominated Sorting Genetic Algorithm (NSGA), Brute Force, and Machine Learning) are implemented to provide different application placement scenarios. The results highlight the efficiency of the proposed approach in comparison with the Brute Force optimal solution while meeting the application requirements. This thesis proposes an optimized solution for serving IoT applications in the computing continuum environment. It considers two essential metrics (latency and energy consumption) in the applications placement processes while meeting the diverse functional and non-functional requirements of these applications. The study provides insights and ideas for future research to refine strategies that will minimize latency and energy consumption. It also urges researchers to consider more metrics while developing and implementing IoT applications. The requirements related to computing resources and performance levels make the development and implementation of these applications complex and challenging. This study serves as a foundational stepping stone towards addressing those challenges.

<p dir="ltr">The onset of smart manufacturing signifies a crucial shift in the industrial landscape, underscoring the pressing need for systems capable of adapting to and managing the complex dynamics of modern production environments. In this context, the importance of smart monitoring becomes increasingly apparent, serving as a vital tool for ensuring operational efficiency and reliability. Inspired by the critical role of auditory perception in human decision-making, this study investigated the application of machine sound recognition for practical use in manufacturing environments. Addressing the challenge of utilizing machine sounds in the loud noises of factories, the study employed an Internal Sound Sensor (ISS).</p><p dir="ltr">The study examined how sound propagates through structures and further explored acoustic characteristics of the ISS, aiming to apply these findings in machine monitoring. To leverage the ISS effectively and achieve a higher level of monitoring, a smart sound monitoring framework was proposed to integrate sound monitoring with machine data and human-machine interface. Designed for applicability and cost effectiveness, this system employs real-time edge computing, making it adaptable for use in various industrial settings.</p><p dir="ltr">The proposed framework and ISS deployed across a diverse range of production environments, showcasing a leap forward in the integration of smart technologies in manufacturing. Their application extends beyond continuous manufacturing to include discrete manufacturing systems, demonstrating adaptability. By analyzing sound signals from various production equipment, this study delves into developing machine sound recognition models that predict operational states and productivity, aiming to enhance manufacturing efficiency and oversight on real factory floors. This comprehensive and practical approach underlines the framework's potential to revolutionize operational management and manufacturing productivity. The study progressed to integrating manufacturing context with sound data, advancing towards high-level monitoring for diagnostic predictions and digital twin. This approach confirmed sound recognition's role in manufacturing diagnostics, laying a foundation for future smart monitoring improvements.</p>

[ES] En los últimos años, Internet de las cosas (``Internet of Things'' o ``IoT'') ha evolucionado de ser simplemente un concepto académico, construido alrededor de protocolos de comunicación y dispositivos, a ser un ecosistema con aplicaciones industriales y de negocio con implicaciones tecnológicas y sociales sin precedentes. Gracias a las nuevas redes de acceso inalámbricas emergentes, sensores mejorados y sistemas embebidos con procesadores cada vez más eficientes y baratos, una gran cantidad de objetos (tanto de nuestra vida cotidiana como de sistemas y procesos industriales) están interconectados entre sí, trasladando la información del mundo físico a las aplicaciones y servicios de Internet. A través de las pasarelas IoT los dispositivos que interactúan con el mundo físico son capaces de conectarse a las redes de comunicación e intercambiar información. Son varios los retos que deben afrontar las pasarelas en su papel dentro del Internet de las Cosas, entre ellas, la escalabilidad, seguridad, la gestión de dispositivos y, recientemente, la interoperabilidad. La falta de interoperabilidad entre los dispositivos provoca importantes problemas tecnológicos y empresariales, tales como la imposibilidad de conectar dispositivos IoT no interoperables a plataformas IoT heterogéneas, la imposibilidad de desarrollar aplicaciones IoT que exploten múltiples plataformas en dominios homogéneos y/o cruzados, la lentitud en la introducción de la tecnología IoT a gran escala, el desánimo en la adopción de la tecnología IoT, el aumento de los costes, la escasa reutilización de las soluciones técnicas y la insatisfacción de los usuarios. El propósito de esta tesis doctoral es la búsqueda de una solución óptima para la interoperabilidad entre dispositivos de Internet de las Cosas mediante la definición de una pasarela IoT genérica, modular y extensible; sin dejar de lado aspectos esenciales como la seguridad, escalabilidad y la calidad de servicio. Se completa esta tesis doctoral con una implementación software de la pasarela IoT siguiendo la definición propuesta, así como el despliegue y la evaluación de los resultados obtenidos en numerosos casos de uso pertenecientes a pilotos del proyecto de investigación Europeo ``INTER-IoT'' financiado a través del programa marco Horizonte 2020. / [CA] En els últims anys, Internet de les coses (``Internet of Things'' o ``IoT'') ha evolucionat de ser simplement un concepte acadèmic, construït al voltant de protocols de comunicació i dispositius, a ser un ecosistema amb aplicacions industrials i de negoci amb implicacions tecnològiques i socials sense precedents. Gràcies a les noves xarxes d'accés ``wireless'' emergents, sensors millorats i sistemes embeguts amb processadors cada vegada més eficients i barats, una gran quantitat d'objectes (tant de la nostra vida quotidiana com de sistemes i processos industrials) estan interconnectats entre si, traslladant la informació del món físic a les aplicacions i serveis d'Internet. A través de les passarel·les IoT els dispositius que interactuen amb el món físic són capaços de connectar-se a les xarxes de comunicació i intercanviar informació. Són diversos els reptes que han d'afrontar les passarel·les en el seu paper dins de la Internet de les Coses, entre elles, l'escalabilitat, seguretat, la gestió de dispositius i, recentment, la interoperabilitat. La falta d'interoperabilitat entre els dispositius provoca importants problemes tecnològics i empresarials, com ara la impossibilitat de connectar dispositius IoT no interoperables a plataformes IoT heterogènies, la impossibilitat de desenvolupar aplicacions IoT que exploten múltiples plataformes en dominis homogenis i/o croats, la lentitud en la introducció de la tecnologia IoT a gran escala, el descoratjament en l'adopció de la tecnologia IoT, l'augment dels costos, l'escassa reutilització de les solucions tècniques i la insatisfacció dels usuaris. El propòsit d'aquesta tesi doctoral és la cerca d'una solució òptima per a la interoperabilitat entre dispositius d'Internet de les Coses mitjançant la definició d'una passarel·la IoT genèrica, modular i extensible; sense deixar de costat aspectes essencials com la seguretat, escalabilitat i la qualitat de servei. Es completa aquesta tesi doctoral amb una implementació programari de la passarel·la IoT seguint la definició proposada, així com el desplegament i l'avaluació dels resultats obtinguts en nombrosos casos d'ús pertanyents a pilots del projecte d'investigació Europeu ``INTER-IoT'' finançat a través del programa marc Horitzó 2020. / [EN] In recent years, the Internet of Things (``IoT") has evolved from being simply an academic concept, built around communication protocols and devices, to an ecosystem with industrial and business applications with unprecedented technological and social implications. Thanks to new emerging wireless access networks, improved sensors and embedded systems with increasingly efficient and inexpensive processors, a large number of objects (both in our daily lives and in industrial systems and processes) are interconnected with each other, moving information from the physical world to Internet applications and services. Through IoT gateways, devices that interact with the physical world are able to connect to communication networks and exchange information. There are several challenges that gateways must face in their role within the Internet of Things, including scalability, security, device management and, recently, interoperability. The lack of interoperability between devices causes major technological and business problems, such as the impossibility of connecting non-interoperable IoT devices to heterogeneous IoT platforms, the impossibility of developing IoT applications that exploit multiple platforms in homogeneous and/or cross-domains, the slow introduction of IoT technology on a large scale, discouragement in the adoption of IoT technology, increased costs, low utilization of technical solutions and user dissatisfaction. The purpose of this doctoral thesis is the search for an optimal solution for interoperability between Internet of Things devices by defining a generic, modular and extensible IoT gateway; without neglecting essential aspects such as security, scalability and quality of service. This doctoral Thesis is completed with a software implementation of the IoT gateway following the proposed definition, as well as the deployment and evaluation of the results obtained in numerous use cases belonging to the pilots of the European research project ``INTER-IoT'' funded through the Horizon 2020 framework program. / Esta tesis doctoral se completa con una implementación software de la pasarela IoT siguiendo la definición propuesta, así como el despliegue y la evaluación de los resultados obtenidos en numerosos casos de uso pertenecientes a pilotos del proyecto de investigación Europeo “INTER-IoT” financiado a través del programa marco Horizonte 2020. / Olivares Gorriti, E. (2022). Especificación y desarrollo de una pasarela física y virtual para interoperabilidad de dispositivos heterogéneos en el ámbito de Internet de las Cosas [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181492

This dissertation presents novel approaches to optimizing convolutional neural network (CNN) architectures for connected autonomous vehicle (CAV) workload on edge, tailored to surmount the challenges inherent in cooperative perception under the stringent resource constraints of edge devices (an endpoint on the network, the interface between the data center and the real world). Employing a modular methodology, this research utilizes the insights from granular examination of CAV perception workloads on edge platforms, identifying and analyzing critical bottlenecks. Through memory contention-aware neural architecture search (NAS), coupled with multi-objective optimization (MOO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this work dynamically optimizes CNN architectures, focusing on reducing memory cost, layer configuration and parameter optimization to reach set hardware constraints whilst maintaining a target precision performance. The results of this exploration are significant, achieving a 63% reduction in memory usage while maintaining a precision rate above 80% for CAV relevant object classes. This dissertation makes novel contributions to the field of edge computing in CAVs, offering a scalable and automated pipeline framework for dynamically obtaining an optimized model for given constraints, thus enabling CAV workloads on edge. In future research, this dissertation also opens multiple different venues for areas of integration. The modular aspect of the pipeline allows for security, privacy, scalability, and energy constraints to be added natively. Through detailed layer by layer analysis and refinement, this dissertation can ensure that CAVs can fully utilize any suitable edge device for the workload requested to realize autonomous driving for everyone.