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Teknik och fastighetsförvaltningen är en förvaltning inom Västerås stad som ansvarar för byggandet av Västerås stad. Förvaltningen är intresserad av att få en bredare kunskap om optimering av underhållssystem för luftkvalitet och hur det skulle leda till energibesparing. Uppkomsten till deras intresse för om optimering av underhållssystem för luftkvalitet och energibesparing, är av anledning att de söker nya innovativa möjligheter att optimera luftkvalitet inom deras befintliga och nya fastigheter inom Västerås stads kommun. Projektgruppen samt teknik och fastighetsförvaltningen valde att lägga fokus på Hamreskolan där de i dagsläget har ett gediget underhållssystem för luftkvaliteten men har en önskan till förbättring. Skälet är deras upplevelse av luftkvalitet som inte är optimal, upplevelsen är att man känner sig trött, att det är kallt och kvavt ibland även för varmt inne i lokalerna. Bra luftkvalite är väsentligt för det påverkar både personalen och eleverna prestationsförmåga prioriterades detta. Målet med detta examensarbete är att presentera förbättringsförslag för att optimera underhållssystemet i Hamreskolan. Underhållssystemet innefattar ventilationssystemet och styrsystemet där dess syfte är att underhålla luftkvaliteten. De metoder som användes för framtagandet av förbättrings förslagen är djup litteraturstudie, platsbesök i Hamreskolan, brainstorming med förvaltare från Teknik och fastighetsförvaltningen samt pugh matris för validering av förbättrings förslagen. I detta examensarbete presenteras och diskuteras de förbättringsförslag som kommer medföra positiva effekter för Hamreskolan vid implementation. Dessa förbättringsförslag behövs inte nödvändigtvist begränsas till endast implementation vid Hamreskolan, det går även att implementera vid flera fastigheter inom Västerås stad, Teknik och fastighetsförvaltning. Vid utvecklande av förbättringsförslagen har realitet för funktionalitet och dess effekt vid implementation i Hamreskolan varit i åtanken. / Technology and property management is an administration within the city of Västerås that is responsible for the construction of the city of Västerås. The administration is interested in gaining a broader knowledge of optimizing maintenance systems for air quality and how it would lead to energy savings. The emergence of their interest in optimizing maintenance systems for air quality and energy savings, is due to seeking new innovative opportunities to optimize air quality within their existing and new properties within the City of Västerås. The project group as well as technology and property management chose to focus on Hamreskolan, where they currently have a solid maintenance system for air quality but have a desire for improvement. The reason is their experience of air quality which is not optimal, the experience is that you feel tired, that it is cold and sometimes even too hot inside the premises. Good air quality is essential because it affects both the staff and the student's performance priorities. The aim of this thesis is to present improvement proposals to optimize the maintenance system in Hamreskolan. The maintenance system includes the ventilation system and the control system where its purpose is to maintain the air quality. The methods used for the preparation of improvement proposals are in-depth literature study, site visits to Hamreskolan, brainstorming with managers from Technology and Property Management and a pugh matrix for validation of improvement proposals. In this thesis, the improvement proposals that will have positive effects for Hamreskolan upon implementation are presented and discussed. These improvement proposals do not necessarily have to be limited to only implementation at Hamreskolan, it is also possible to implement at several properties within the City of Västerås, Technology and property management. In developing the improvement proposals, the reality for functionality and its effect when implemented in Hamreskolan has been in mind.

<p><strong>The Energy Monitoring Framework</strong>, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology.</p> <p>The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely.</p> <p>The main objectives of the project include the following:</p> <ul> <li>Set up a wireless network using sensors and smart implants with a base station/ controller.</li> <li>Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency.</li> <li>Set up a generalized interface to collect and process the sensor data values and store the data in a database.</li> <li>Design and develop a generic database compatible with various companies irrespective of the type and size.</li> <li> Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment.</li> </ul> <p>The General Structure of the project constitutes the following components:</p> <ul> <li>A wireless sensor network with a base station.</li> <li>An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators.</li> <li>A cloud that hosts a database and an API to collect and store information.</li> <li>A web application hosted in the cloud to provide an interactive platform for users to analyze the data.</li> </ul> <p>The project was demonstrated in:</p> <ul> <li>Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/).</li> <li>Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/).</li> <li>A company in Indiana.</li> </ul> <p>The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.</p> <p><br></p>

Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.<br>

Currently manufacturers in the European Surface Mount Technology (SMT) industry seeproduction changeover, machine downtime and process optimization as their biggestchallenges. They also see a need for collecting data and sharing information betweenmachines, people and systems involved in the manufacturing process. Internet of Things (IoT)technology provides an opportunity to make this happen. This research project gives answers tothe question of what the potentials and challenges of IoT implementation are in European SMTmanufacturing. First, key IoT concepts are introduced. Then, through interviews with expertsworking in SMT manufacturing, the current standpoint of the SMT industry is defined. The studypinpoints obstacles in SMT IoT implementation and proposes a solution. Firstly, local datacollection and sharing needs to be achieved through the use of standardized IoT protocols andAPIs. Secondly, because SMT manufacturers do not trust that sensitive data will remain securein the Cloud, a separation of proprietary data and statistical data is needed in order take a stepfurther and collect Big Data in a Cloud service. This will allow for new services to be offered byequipment manufacturers. / I dagsläget upplever tillverkare inom den europeiska ytmonteringsindustrin för elektronikproduktionsomställningar, nedtid för maskiner och processoptimering som sina störstautmaningar. De ser även ett behov av att samla data och dela information mellan maskiner,människor och system som som är delaktiga i tillverkningsprocessen.Sakernas internet, även kallat Internet of Things (IoT), erbjuder teknik som kan göra dettamöjligt. Det här forskningsprojektet besvarar frågan om vilken potential som finns samt vilkautmaningar en implementation av sakernas internet inom europeisk ytmonteringstillverkning avelektronik innebär. Till att börja med introduceras nyckelkoncept inom sakernas internet. Sedandefinieras utgångsläget i elektroniktillverkningsindustrin genom intervjuer med experter.Studien belyser de hinder som ligger i vägen för implementation och föreslår en lösning. Dettainnebär först och främst att datainsamling och delning av data måste uppnås genomanvändning av standardiserade protokoll för sakernas internet ochapplikationsprogrammeringsgränssnitt (APIer). På grund av att elektroniktillverkare inte litar påatt känslig data förblir säker i molnet måste proprietär data separeras från statistisk data. Dettaför att möjliggöra nästa steg som är insamling av så kallad Big Data i en molntjänst. Dettamöjliggör i sin tur för tillverkaren av produktionsmaskiner att erbjuda nya tjänster.

This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. The work innovates by integrating spatially-distributed LiDAR and RGB-D Camera sensors, with the optional inclusion of on-board IMU-based dead-reckoning, forming a robust and efficient coordination framework for autonomous systems. Two key developments are achieved. Firstly, a point cloud object detection technique, "Generalized L-Shape Fitting”, is advanced, enhancing bounding box fitting over point cloud data. Secondly, a new estimation framework, the Distributed Edge Node Switching Filter (DENS-F), is established. The DENS-F optimizes resource utilization and coordination, while minimizing reliance on on-board computation. Furthermore, it incorporates a short-term predictive feature, thanks to the Adaptive-Constant Acceleration motion model, which utilizes behaviour-based control inputs. The findings indicate that the DENS-F substantially improves accuracy and computational efficiency compared to the Kalman Consensus Filter (KCF), particularly when additional inertial data is provided by the vehicle. The type of sensor deployed and the consistency of the vehicle's path are also found to significantly influence the system's performance. The research opens new viewpoints for enhancing autonomous vehicle tracking, highlighting opportunities for future exploration in prediction models, sensor selection, and precision coordination. / Denna forskning föreslår en ny metod för att förbättra autonom mobil systemsnavigering i dynamiska och potentiellt skymda miljöer. Forskningen introducerar ett spårningsramverk som kombinerar data från stationära sensorenheter och ombordssensorer, vilket hanterar utmaningar med beräkningsefektivitet, tillförlitlighet och skalbarhet. Arbetet innoverar genom att integrera spatialt distribuerade LiDAR- och RGB-D-kamerasensorer, med det valfria tillägget av ombord IMU-baserad dödräkning, vilket skapar ett robust och efektivt samordningsramverk för autonoma system. Två nyckelutvecklingar uppnås. För det första avanceras en punktmolnsobjektdetekteringsteknik, “Generaliserad L-formig anpassning”, vilket förbättrar anpassning av inneslutande rutor över punktmolnsdata. För det andra upprättas ett nytt uppskattningssystem, det distribuerade kantnodväxlingsfltret (DENSF). DENS-F optimerar resursanvändning och samordning, samtidigt som det minimerar beroendet av ombordberäkning. Vidare införlivar det en kortsiktig prediktiv funktion, tack vare den adaptiva konstanta accelerationsrörelsemodellen, som använder beteendebaserade styrentréer. Resultaten visar att DENS-F väsentligt förbättrar noggrannhet och beräknings-efektivitet jämfört med Kalman Consensus Filter (KCF), särskilt när ytterligare tröghetsdata tillhandahålls av fordonet. Den typ av sensor som används och fordonets färdvägs konsekvens påverkar också systemets prestanda avsevärt. Forskningen öppnar nya synvinklar för att förbättra spårning av autonoma fordon, och lyfter fram möjligheter för framtida utforskning inom förutsägelsemodeller, sensorval och precisionskoordinering. / Questa ricerca propone un nuovo approccio per migliorare la navigazione dei sistemi mobili autonomi in ambienti dinamici e potenzialmente ostruiti. La ricerca introduce un sistema di tracciamento che combina dati da unità di rilevazione stazionarie e sensori di bordo, afrontando le sfde dell’effcienza computazionale, dell’affdabilità e della scalabilità. Il lavoro innova integrando sensori LiDAR e telecamere RGB-D distribuiti nello spazio, con l’inclusione opzionale di una navigazione inerziale basata su IMU di bordo, formando un robusto ed effciente quadro di coordinamento per i sistemi autonomi. Vengono raggiunti due sviluppi chiave. In primo luogo, viene perfezionata una tecnica di rilevazione di oggetti a nuvola di punti, “Generalized L-Shape Fitting”, migliorando l’adattamento del riquadro di delimitazione sui dati della nuvola di punti. In secondo luogo, viene istituito un nuovo framework di stima, il Distributed Edge Node Switching Filter (DENS-F). Il DENS-F ottimizza l’utilizzo delle risorse e il coordinamento, riducendo al minimo la dipendenza dal calcolo di bordo. Inoltre, incorpora una caratteristica di previsione a breve termine, grazie al modello di movimento Adaptive-Constant Acceleration, che utilizza input di controllo basati sul comportamento del veicolo. I risultati indicano che il DENS-F migliora notevolmente l’accuratezza e l’effcienza computazionale rispetto al Kalman Consensus Filter (KCF), in particolare quando il veicolo fornisce dati inerziali aggiuntivi. Si scopre anche che il tipo di sensore impiegato e la coerenza del percorso del veicolo infuenzano signifcativamente le prestazioni del sistema. La ricerca apre nuovi punti di vista per migliorare il tracciamento dei veicoli autonomi, evidenziando opportunità per future esplorazioni nei modelli di previsione, nella selezione dei sensori e nel coordinamento di precisione.

Indiana University-Purdue University Indianapolis (IUPUI) / The Energy Monitoring Framework, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology. The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely. The main objectives of the project include the following: • Set up a wireless network using sensors and smart implants with a base station/ controller. • Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency. • Set up a generalized interface to collect and process the sensor data values and store the data in a database. • Design and develop a generic database compatible with various companies irrespective of the type and size. • Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment. The General Structure of the project constitutes the following components: • A wireless sensor network with a base station. • An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators. • A cloud that hosts a database and an API to collect and store information. • A web application hosted in the cloud to provide an interactive platform for users to analyze the data. The project was demonstrated in: • Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/). • Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/). • A company in Indiana. The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.

Edge Computing is considered one of the key capabilities in next generation (5G) networks, which will enable inundation of latency, throughput, and data sensitive edge-native applications. Edge application developers require infrastructure at the edge to host the application workload and network connectivity procedures to connect the application users to the nearest edge where the application workload is hosted. Distributed nature of edge infrastructure and the requirement on network connectivity makes it attractive for communication service providers (CSPs) to become Edge Service providers (ESP); similarly, hyper-scale cloud providers (HCPs) are also planning to expand as ESP building on their cloud presence targeting edge application developers. CSPs across the globe follow a standard approach for building interoperable networks and infrastructure, while HCPs do not participate in telecom standardization bodies. Standards development organizations (SDOs) such as the European Telecommunication Standardization Institute (ETSI) and 3rd Generation Partnership Project (3GPP) are working to provide a standard architecture for edge computing solution for service providers. However, the current focus of SDOs is more on architecture and not much focus on application developer experience and the Application Programming Interfaces (APIs). On the architecture itself, there are different standards and approaches available which overlap with each other. APIs proposed by different SDOs are not easily consumable by edge application developers and require simplification. On the other hand, there are not many widely known standards in the hyper-scale cloud and public cloud industry to integrate with each other except the public application programming interfaces (APIs) offered by cloud providers. To scale and succeed, edge service providers need to focus on interoperability, not only from a cloud infrastructure perspective but from a network connectivity perspective as well. This work analyzes standards defined by different standardization bodies in the 5G edge computing area and the overlaps between the standards. The work then highlights the requirements from an edge application developer perspective, investigates the deficiencies of the standards, and proposes an end-to-end edge solution architecture and a method to simplify the APIs which fulfil the need for edge-native applications. The proposed solution considers CSPs providing multi-cloud infrastructure for edge computing by integrating with HCPs infrastructure. In addition, the work investigates existing standards to integrate cloud capabilities in network platforms and elaborates the way network and cloud computing capabilities can be integrated to provide complete edge service to edge application developers or enterprises. It proposes an alternative way to integrate edge application developers with cloud service providers dynamically by offering a catalog of services. / Edge Computing anses vara en av nyckelfunktionerna i nästa generations (5G) nätverk, vilket möjliggör minskad fördröjning, ökad genomströmning och datakänsliga och kantnära applikationer. Applikationsutvecklare för Edge Computing är beroende av kantinfrastruktur som är värd för applikationen, och nätverksanslutning för att ansluta applikationsanvändarna till närmaste kant där applikationens är placerad. Även om kantapplikationer kan vara värd för vilken infrastruktur som helst, planerar leverantörer av kommunikationstjänster (CSP:er) att erbjuda distribuerad kantinfrastruktur och anslutningar. På liknande sätt planerar även molnleverantörer med hög skalbarhet (HCP) att erbjudakantinfrastruktur. CSP:er följer standardmetoden för att bygga nätverk och infrastruktur medan HCP:er inte deltar i standardiseringsorgan. Standardutvecklingsorganisationer (SDO) som europeisk telekommunikations standardiseringsinstitut (ETSI) och 3rd Generation Partnership Project (3GPP) arbetar för att tillhandahålla en standardarkitektur för Edge Computing för tjänsteleverantörer. Men nuvarande fokus är mer på arkitektur och inte mycket fokus är riktat mot applikationsutvecklares erfarenhet och API:er. I själva arkitekturen finns det olika standarder och tillvägagångssätt som överlappar varandra. API:er föreslagna av olika SDO:er är inte lättillgängliga för utvecklar av kantapplikationer och måste förenklas. Å andra sidan finns det inte många allmänt kända standarder i hyperskalära moln och offentlig molnindustri som går att integrera med varandra förutom de offentliga gränssnitten för applikationsprogrammering (API:er) som erbjuds av molnleverantörer. För att kunna betjäna omfattande applikationsutvecklare måste CSP:er erbjuda multimolnfunktioner och därmed komplettera sin egen infrastruktur med kapaciteten för HCP:er. På liknande sätt kommer HCP:er att behöva integrera anslutningstjänster utöver infrastruktur för att erbjuda kantfunktioner. Den här arbetet beskriver olika standarder definierade av olika standardiseringsorgan i Edge Computing-området för 5G, och analyzerar överlappningar mellan standarderna. Arbetet belyser sedan kraven från ett utvecklingsperspektiv av kantapplikationer, undersöker bristerna i standarderna och föreslår en lösningsarkitektur som uppfyller behovet för kantbyggda applikationer. Den föreslagna lösningen beaktar CSP:er som tillhandahåller flermolnsinfrastruktur för Edge Computing genom att integreras med HCP:s infrastruktur. Arbetet undersöker vidare befintliga standarder för att integrera molnfunktioner i nätverksplattformar och utvecklar på vilket sätt nätverks- och molntjänster kan integreras för att erbjuda kompletta tjänster till utvecklare av kantapplikationer. Arbetet föreslår ett alternativt sätt att dynamiskt integrera utvecklare av kantapplikationer med leverantörer av molntjänster genom att erbjuda en katalog av tjänster.

[ES] Cerca de 3.000 millones de personas en todo el mundo no pueden aprovechar ni siquiera los servicios de conectividad más básicos, ya que la mayoría de ellas viven en zonas rurales o países en vías de desarrollo. Incluso los servicios de mensajería más simples serian de gran ayuda, por ejemplo, para los agricultores que desean conocer el precio de las mercancías que les interesa vender o comprar antes de decidir si se emprende un viaje posiblemente largo, caro y agotador. La tecnología LoRa permite realizar enlaces de larga distancia con un consumo reducido de energía a bajo coste, siendo su principal limitación el escaso ancho de banda que ofrece. Con LoRa, los lugares remotos, como las zonas rurales, pueden beneficiarse de servicios basados en la conectividad que, de otro modo, serían imposibles. Nuestra propuesta entra en la categoría de redes comunitarias, en las que los usuarios construyen su propia red cuando no hay infraestructura comercial disponible. Además de la simple aplicación de mensajería, LoRa puede utilizarse para distribuir información de sensores a las comunidades o para proporcionar alertas de desastres o datos meteorológicos. Presentamos un protocolo flexible basado en la tecnología LoRa que permite la transferencia de "contenido" denominado LoRaCTP, el cual proporciona los mecanismos necesarios para que LoRa sea fiable, introduciendo una configuración de conexión ligera. Hemos diseñado este protocolo también como soporte de comunicación para las soluciones IoT basadas en edge computing, dada su estabilidad, el bajo consumo de energía y la posibilidad de cubrir largas distancias. Integramos una arquitectura que permite la recolección de datos de fuentes externas genéricas. Especialmente las fuentes de audio, apuntando a dos servicios básicos: un sistema de mensajería de voz que permite a los usuarios que no saben leer o escribir, realizar notas de voz, y un servicio de compresión de audio para extraer las principales características de la entrada de audio y utilizarla para desarrollar análisis de audio inteligente basado en Machine Learning. Combinamos IoT y Edge computing en un enfoque de innovación frugal, para proponer una solución "ingeniosa", en términos de utilización mínima de recursos y sostenibilidad, para construir un soporte básico para un sistema IoT en áreas rurales. Incluimos un proxy MQTT para integrar dispositivos de bajo coste y bajo consumo en un sistema de mensajería basado en LoRa. A través de una interfaz REST, mostramos cómo se puede usar nuestra plataforma para distribuir información de sensores de las comunidades rurales. Además, MQTT permite que estos datos se proporcionen a "lagos de datos" externos para que puedan usarse para tareas tales como informes, visualización, análisis avanzado y aprendizaje automático. Describimos una arquitectura genérica de edge/fog, utilizando microservicios, un sistema basado en MQTT que puede recopilar datos de ingreso, manejar su persistencia y coordinar la integración de datos con la nube utilizando un servicio específico llamado agregador. Las estaciones edge tienen un canal dedicado con el agregador que se basa en LoRa para permitir transmisiones de largo alcance con bajo consumo de energía. / [CA] Prop de 3.000 milions de persones a tot el món no poden aprofitar ni tan sols els serveis de connectivitat més bàsics, ja que la majoria d'elles viuen en zones rurals o països en vies de desenvolupament. Fins i tot els serveis de missatgeria més simples serien de gran ajuda, per exemple, per als agricultors que desitgen conèixer el preu de les mercaderies que els interessa vendre o comprar abans de decidir si s'emprèn un viatge possiblement llarg, car i esgotador. La tecnologia LoRa permet realitzar enllaços de llarga distància amb un consum reduït d'energia a baix cost, sent la seua principal limitació l'escassa amplada de banda que ofereix. Amb LoRa, els llocs remots, com les zones rurals, poden beneficiar-se de serveis basats en la connectivitat que, d'una altra manera, serien impossibles. La nostra proposta entra en la categoria de xarxes comunitàries, en les quals els usuaris construeixen la seua pròpia xarxa quan no hi ha infraestructura comercial disponible. A més de la simple aplicació de missatgeria, LoRa pot utilitzarse per a distribuir informació de sensors a les comunitats o per a proporcionar alertes de desastres o dades meteorològiques. Presentem un protocol flexible basat en la tecnologia LoRa que permet la transferència de "contingut" denominat LoRaCTP, el qual proporciona els mecanismes necessaris perquè LoRa siga fiable, introduint una configuració de connexió lleugera. Hem dissenyat aquest protocol també com a suport de comunicació per a les solucions IoT basades en edge computing, donada la seua estabilitat, el baix consum d'energia i la possibilitat de cobrir llargues distàncies. Integrem una arquitectura que permet la recol·lecció de dades de fonts externes genèriques. Especialment, les fonts d'àudio, apuntant a dos serveis bàsics: un sistema de missatgeria de veu que permet als usuaris que no saben llegir o escriure realitzar notes de veu, i un servei de compressió d'àudio per a extraure les principals característiques de l'entrada d'àudio i utilitzar-la per a desenvolupar anàlisi d'àudio intel·ligent basat en Machine Learning. Combinem IoT i Edge computing en un enfocament d'innovació frugal, per a proposar una solució "enginyosa", en termes d'utilització mínima de recursos i sostenibilitat, per a construir un suport bàsic per a un sistema IoT en àrees rurals. Incloem un proxy MQTT per a integrar dispositius de baix cost i baix consum en un sistema de missatgeria basat en LoRa. A través d'una interfície REST, vam mostrar com es pot usar la nostra plataforma per a distribuir informació de sensors de les comunitats rurals. A més, MQTT permet que aquestes dades es proporcionen a "llacs de dades" externes perquè puguen usar-se per a tasques com ara informes, visualització, anàlisi avançada i aprenentatge automàtic. Descrivim una arquitectura genèrica de edge/fog, utilitzant microserveis, un sistema basat en MQTT que pot recopilar dades d'ingrés, manejar la seua persistència i coordinar la integració de dades amb el núvol utilitzant un servei específic anomenat agregador. Les estacions edge tenen un canal dedicat amb el agregador que es basa en LoRa per a permetre transmissions de llarg abast amb baix consum d'energia. / [EN] Nearly 3 billion people around the world are unable to take advantage of even the most basic connectivity services, as most of them live in rural areas or developing countries. Even the simplest messaging services would be of great help, for example, to farmers who want to know the price of goods they are interested in selling or buying before deciding whether to embark on a possibly long, expensive and exhausting journey. LoRa technology enables long-distance links with reduced power consumption at low cost, its main limitation being the low bandwidth it offers. With LoRa, remote locations, such as rural areas, can benefit from connectivity-based services that would otherwise be impossible. Our proposal falls into the category of community networks, where users build their own network when commercial infrastructure is not available. In addition to a simple messaging application, LoRa can be used to distribute sensor information to communities or to provide disaster alerts or weather data. We present a flexible protocol based on LoRa technology that enables the transfer of "content" called LoRaCTP, which provides the necessary mechanisms for LoRa to be reliable, introducing a lightweight connection setup. We have designed this protocol also as a communication support for IoT solutions based on edge computing, given its stability, low power consumption and the possibility of covering long distances. Likewise, we integrated an architecture that allows data collection from generic external sources. Especially audio sources, targeting two basic services: a voice messaging system that allows users who cannot read or write to make voice notes, and an audio compression service to extract the main features of the audio input and use it to develop intelligent audio analytics based on Machine Learning. We combine IoT and Edge computing in a frugal innovation approach, to propose an "ingenious" solution, in terms of minimum resource utilization and sustainability, to build a basic support for an IoT system in rural areas. We include an MQTT proxy to integrate low-cost and low-power devices into a LoRa-based messaging system. Through a REST interface, we show how our platform can be used to distribute sensor information from rural communities. In addition, MQTT allows this data to be provided to external "data lakes" so that it can be used for tasks such as reporting, visualization, advanced analytics, and machine learning. We describe a generic edge/fog architecture, using microservices, an MQTT-based system that can collect ingress data, manage its persistence, and coordinate data integration with the cloud using a specific service called an aggregator. The edge stations have a dedicated channel with the aggregator that is based on LoRa to enable long-range transmissions with low power consumption. / Agradezco al Gobierno de los Estados Unidos Mexicanos a través del Consejo Nacional de Ciencia y Tecnología (CONACYT) y al Gobierno de Chiapas mediante el Consejo de Ciencia y Tecnología del Estado de Chiapas (COCYTECH) por brindarme la oportunidad de mejoramiento profesional y académico por medio del programa de becas “CONACYT - Gobierno del Estado de Chiapas” en la convocatoria del 2017. / Nakamura Pinto, MK. (2022). Diseño de un sistema de mensajería instantánea para redes comunitarias [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/188948

<p dir="ltr">Federated Learning (FL), a novel subclass of Artificial Intelligence, decentralizes the learning process by enabling participants to benefit from a comprehensive model trained on a broader dataset without direct sharing of private data. This approach integrates multiple local models into a global model, mitigating the need for large individual datasets. However, the decentralized nature of FL increases its vulnerability to adversarial attacks. These include backdoor attacks, which subtly alter classification in some categories, and byzantine attacks, aimed at degrading the overall model accuracy. Detecting and defending against such attacks is challenging, as adversaries can participate in the system, masquerading as benign contributors. This thesis provides an extensive analysis of the various security attacks, highlighting the distinct elements of each and the inherent vulnerabilities of FL that facilitate these attacks. The focus is primarily on backdoor attacks, which are stealthier and more difficult to detect compared to byzantine attacks. We explore defense strategies effective in identifying malicious participants or mitigating attack impacts on the global model. The primary aim of this research is to evaluate the effectiveness and limitations of existing server-level defenses and to develop innovative defense mechanisms under diverse threat models. This includes scenarios where the server collaborates with clients to thwart attacks, cases where the server remains passive but benign, and situations where no server is present, requiring clients to independently minimize and isolate attacks while enhancing main task performance. Throughout, we ensure that the interventions do not compromise the performance of both global and local models. The research predominantly utilizes 2D and 3D datasets to underscore the practical implications and effectiveness of proposed methodologies.</p>