68 research outputs found

    Serverless Strategies and Tools in the Cloud Computing Continuum

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    Tesis por compendio[ES] En los últimos años, la popularidad de la computación en nube ha permitido a los usuarios acceder a recursos de cómputo, red y almacenamiento sin precedentes bajo un modelo de pago por uso. Esta popularidad ha propiciado la aparición de nuevos servicios para resolver determinados problemas informáticos a gran escala y simplificar el desarrollo y el despliegue de aplicaciones. Entre los servicios más destacados en los últimos años se encuentran las plataformas FaaS (Función como Servicio), cuyo principal atractivo es la facilidad de despliegue de pequeños fragmentos de código en determinados lenguajes de programación para realizar tareas específicas en respuesta a eventos. Estas funciones son ejecutadas en los servidores del proveedor Cloud sin que los usuarios se preocupen de su mantenimiento ni de la gestión de su elasticidad, manteniendo siempre un modelo de pago por uso de grano fino. Las plataformas FaaS pertenecen al paradigma informático conocido como Serverless, cuyo propósito es abstraer la gestión de servidores por parte de los usuarios, permitiéndoles centrar sus esfuerzos únicamente en el desarrollo de aplicaciones. El problema del modelo FaaS es que está enfocado principalmente en microservicios y tiende a tener limitaciones en el tiempo de ejecución y en las capacidades de computación (por ejemplo, carece de soporte para hardware de aceleración como GPUs). Sin embargo, se ha demostrado que la capacidad de autoaprovisionamiento y el alto grado de paralelismo de estos servicios pueden ser muy adecuados para una mayor variedad de aplicaciones. Además, su inherente ejecución dirigida por eventos hace que las funciones sean perfectamente adecuadas para ser definidas como pasos en flujos de trabajo de procesamiento de archivos (por ejemplo, flujos de trabajo de computación científica). Por otra parte, el auge de los dispositivos inteligentes e integrados (IoT), las innovaciones en las redes de comunicación y la necesidad de reducir la latencia en casos de uso complejos han dado lugar al concepto de Edge computing, o computación en el borde. El Edge computing consiste en el procesamiento en dispositivos cercanos a las fuentes de datos para mejorar los tiempos de respuesta. La combinación de este paradigma con la computación en nube, formando arquitecturas con dispositivos a distintos niveles en función de su proximidad a la fuente y su capacidad de cómputo, se ha acuñado como continuo de la computación en la nube (o continuo computacional). Esta tesis doctoral pretende, por lo tanto, aplicar diferentes estrategias Serverless para permitir el despliegue de aplicaciones generalistas, empaquetadas en contenedores de software, a través de los diferentes niveles del continuo computacional. Para ello, se han desarrollado múltiples herramientas con el fin de: i) adaptar servicios FaaS de proveedores Cloud públicos; ii) integrar diferentes componentes software para definir una plataforma Serverless en infraestructuras privadas y en el borde; iii) aprovechar dispositivos de aceleración en plataformas Serverless; y iv) facilitar el despliegue de aplicaciones y flujos de trabajo a través de interfaces de usuario. Además, se han creado y adaptado varios casos de uso para evaluar los desarrollos conseguidos.[CA] En els últims anys, la popularitat de la computació al núvol ha permès als usuaris accedir a recursos de còmput, xarxa i emmagatzematge sense precedents sota un model de pagament per ús. Aquesta popularitat ha propiciat l'aparició de nous serveis per resoldre determinats problemes informàtics a gran escala i simplificar el desenvolupament i desplegament d'aplicacions. Entre els serveis més destacats en els darrers anys hi ha les plataformes FaaS (Funcions com a Servei), el principal atractiu de les quals és la facilitat de desplegament de petits fragments de codi en determinats llenguatges de programació per realitzar tasques específiques en resposta a esdeveniments. Aquestes funcions són executades als servidors del proveïdor Cloud sense que els usuaris es preocupen del seu manteniment ni de la gestió de la seva elasticitat, mantenint sempre un model de pagament per ús de gra fi. Les plataformes FaaS pertanyen al paradigma informàtic conegut com a Serverless, el propòsit del qual és abstraure la gestió de servidors per part dels usuaris, permetent centrar els seus esforços únicament en el desenvolupament d'aplicacions. El problema del model FaaS és que està enfocat principalment a microserveis i tendeix a tenir limitacions en el temps d'execució i en les capacitats de computació (per exemple, no té suport per a maquinari d'acceleració com GPU). Tot i això, s'ha demostrat que la capacitat d'autoaprovisionament i l'alt grau de paral·lelisme d'aquests serveis poden ser molt adequats per a més aplicacions. A més, la seva inherent execució dirigida per esdeveniments fa que les funcions siguen perfectament adequades per ser definides com a passos en fluxos de treball de processament d'arxius (per exemple, fluxos de treball de computació científica). D'altra banda, l'auge dels dispositius intel·ligents i integrats (IoT), les innovacions a les xarxes de comunicació i la necessitat de reduir la latència en casos d'ús complexos han donat lloc al concepte d'Edge computing, o computació a la vora. L'Edge computing consisteix en el processament en dispositius propers a les fonts de dades per millorar els temps de resposta. La combinació d'aquest paradigma amb la computació en núvol, formant arquitectures amb dispositius a diferents nivells en funció de la proximitat a la font i la capacitat de còmput, s'ha encunyat com a continu de la computació al núvol (o continu computacional). Aquesta tesi doctoral pretén, doncs, aplicar diferents estratègies Serverless per permetre el desplegament d'aplicacions generalistes, empaquetades en contenidors de programari, a través dels diferents nivells del continu computacional. Per això, s'han desenvolupat múltiples eines per tal de: i) adaptar serveis FaaS de proveïdors Cloud públics; ii) integrar diferents components de programari per definir una plataforma Serverless en infraestructures privades i a la vora; iii) aprofitar dispositius d'acceleració a plataformes Serverless; i iv) facilitar el desplegament d'aplicacions i fluxos de treball mitjançant interfícies d'usuari. A més, s'han creat i s'han adaptat diversos casos d'ús per avaluar els desenvolupaments aconseguits.[EN] In recent years, the popularity of Cloud computing has allowed users to access unprecedented compute, network, and storage resources under a pay-per-use model. This popularity led to new services to solve specific large-scale computing challenges and simplify the development and deployment of applications. Among the most prominent services in recent years are FaaS (Function as a Service) platforms, whose primary appeal is the ease of deploying small pieces of code in certain programming languages to perform specific tasks on an event-driven basis. These functions are executed on the Cloud provider's servers without users worrying about their maintenance or elasticity management, always keeping a fine-grained pay-per-use model. FaaS platforms belong to the computing paradigm known as Serverless, which aims to abstract the management of servers from the users, allowing them to focus their efforts solely on the development of applications. The problem with FaaS is that it focuses on microservices and tends to have limitations regarding the execution time and the computing capabilities (e.g. lack of support for acceleration hardware such as GPUs). However, it has been demonstrated that the self-provisioning capability and high degree of parallelism of these services can be well suited to broader applications. In addition, their inherent event-driven triggering makes functions perfectly suitable to be defined as steps in file processing workflows (e.g. scientific computing workflows). Furthermore, the rise of smart and embedded devices (IoT), innovations in communication networks and the need to reduce latency in challenging use cases have led to the concept of Edge computing. Edge computing consists of conducting the processing on devices close to the data sources to improve response times. The coupling of this paradigm together with Cloud computing, involving architectures with devices at different levels depending on their proximity to the source and their compute capability, has been coined as Cloud Computing Continuum (or Computing Continuum). Therefore, this PhD thesis aims to apply different Serverless strategies to enable the deployment of generalist applications, packaged in software containers, across the different tiers of the Cloud Computing Continuum. To this end, multiple tools have been developed in order to: i) adapt FaaS services from public Cloud providers; ii) integrate different software components to define a Serverless platform on on-premises and Edge infrastructures; iii) leverage acceleration devices on Serverless platforms; and iv) facilitate the deployment of applications and workflows through user interfaces. Additionally, several use cases have been created and adapted to assess the developments achieved.Risco Gallardo, S. (2023). Serverless Strategies and Tools in the Cloud Computing Continuum [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202013Compendi

    IoT and Sensor Networks in Industry and Society

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    The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

    A Survey on Mobile Edge Computing for Video Streaming : Opportunities and Challenges

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    5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.publishedVersionPeer reviewe

    Actas del XXIV Workshop de Investigadores en Ciencias de la Computación: WICC 2022

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    Compilación de las ponencias presentadas en el XXIV Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Mendoza en abril de 2022.Red de Universidades con Carreras en Informátic

    System Abstractions for Scalable Application Development at the Edge

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    Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

    Time-Shifted Prefetching and Edge-Caching of Video Content: Insights, Algorithms, and Solutions

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    Video traffic accounts for 82% of global Internet traffic and is growing at an unprecedented rate. As a result of this rapid growth and popularity of video content, the network is heavily burdened. To cope with this, service providers have to spend several millions of dollars for infrastructure upgrades; these upgrades are typically triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5 times as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collected YouTube and Netflix usage from over 1500 users spanning at least a one-year period consisting of approximately 8.5 million videos collectively watched. We use the datasets to analyze and present key insights about user-level usage behavior, and show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. Thereafter, equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching solutions, using machine-learning and deep-learning techniques (specifically supervised classifiers and LSTM networks), which anticipates the video content the user will consume based on their prior watching behavior, and prefetches it during off-peak periods. We find that our developed solutions can reduce nearly 35% of peak-time YouTube traffic and 70% of peak-time Netflix series traffic. We developed and evaluated a proof-of-concept system for prefetching video traffic. We also show how to integrate the two systems for prefetching YouTube and Netflix content. Furthermore, based on our findings from our developed algorithms, we develop a framework for prefetching video content regardless of the type of video and platform upon which it is hosted.Ph.D

    Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

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    This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

    A Systematic Literature Review on Distributed Machine Learning in Edge Computing

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    Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies

    Mobiilse värkvõrgu protsessihaldus

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    Värkvõrk, ehk Asjade Internet (Internet of Things, lüh IoT) edendab lahendusi nagu nn tark linn, kus meid igapäevaselt ümbritsevad objektid on ühendatud infosüsteemidega ja ka üksteisega. Selliseks näiteks võib olla teekatete seisukorra monitoorimissüsteem. Võrku ühendatud sõidukitelt (nt bussidelt) kogutakse videomaterjali, mida seejärel töödeldakse, et tuvastada löökauke või lume kogunemist. Tavaliselt hõlmab selline lahendus keeruka tsentraalse süsteemi ehitamist. Otsuste langetamiseks (nt milliseid sõidukeid parasjagu protsessi kaasata) vajab keskne süsteem pidevat ühendust kõigi IoT seadmetega. Seadmete hulga kasvades võib keskne lahendus aga muutuda pudelikaelaks. Selliste protsesside disaini, haldust, automatiseerimist ja seiret hõlbustavad märkimisväärselt äriprotsesside halduse (Business Process Management, lüh BPM) valdkonna standardid ja tööriistad. Paraku ei ole BPM tehnoloogiad koheselt kasutatavad uute paradigmadega nagu Udu- ja Servaarvutus, mis tuleviku värkvõrgu jaoks vajalikud on. Nende puhul liigub suur osa otsustustest ja arvutustest üksikutest andmekeskustest servavõrgu seadmetele, mis asuvad lõppkasutajatele ja IoT seadmetele lähemal. Videotöötlust võiks teostada mini-andmekeskustes, mis on paigaldatud üle linna, näiteks bussipeatustesse. Arvestades IoT seadmete üha suurenevat hulka, vähendab selline koormuse jaotamine vähendab riski, et tsentraalne andmekeskust ülekoormamist. Doktoritöö uurib, kuidas mobiilsusega seonduvaid IoT protsesse taoliselt ümber korraldada, kohanedes pidevalt muutlikule, liikuvate seadmetega täidetud servavõrgule. Nimelt on ühendused katkendlikud, mistõttu otsuste langetus ja planeerimine peavad arvestama muuhulgas mobiilseadmete liikumistrajektoore. Töö raames valminud prototüüpe testiti Android seadmetel ja simulatsioonides. Lisaks valmis tööriistakomplekt STEP-ONE, mis võimaldab teadlastel hõlpsalt simuleerida ja analüüsida taolisi probleeme erinevais realistlikes stsenaariumites nagu seda on tark linn.The Internet of Things (IoT) promotes solutions such as a smart city, where everyday objects connect with info systems and each other. One example is a road condition monitoring system, where connected vehicles, such as buses, capture video, which is then processed to detect potholes and snow build-up. Building such a solution typically involves establishing a complex centralised system. The centralised approach may become a bottleneck as the number of IoT devices keeps growing. It relies on constant connectivity to all involved devices to make decisions, such as which vehicles to involve in the process. Designing, automating, managing, and monitoring such processes can greatly be supported using the standards and software systems provided by the field of Business Process Management (BPM). However, BPM techniques are not directly applicable to new computing paradigms, such as Fog Computing and Edge Computing, on which the future of IoT relies. Here, a lot of decision-making and processing is moved from central data-centers to devices in the network edge, near the end-users and IoT sensors. For example, video could be processed in mini-datacenters deployed throughout the city, e.g., at bus stops. This load distribution reduces the risk of the ever-growing number of IoT devices overloading the data center. This thesis studies how to reorganise the process execution in this decentralised fashion, where processes must dynamically adapt to the volatile edge environment filled with moving devices. Namely, connectivity is intermittent, so decision-making and planning need to involve factors such as the movement trajectories of mobile devices. We examined this issue in simulations and with a prototype for Android smartphones. We also showcase the STEP-ONE toolset, allowing researchers to conveniently simulate and analyse these issues in different realistic scenarios, such as those in a smart city.  https://www.ester.ee/record=b552551
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