The Internet of Things is a recent computing paradigm, de- fined by networks of highly connected things – sensors, actuators and smart objects – communicating across networks of homes, buildings, vehicles, and even people. The Internet of Things brings with it a host of new problems, from managing security on constrained devices to processing never before seen amounts of data. While cloud computing might be able to keep up with current data processing and computational demands, it is unclear whether it can be extended to the requirements brought forth by Internet of Things. Fog computing provides an architectural solution to address some of these problems by providing a layer of intermediary nodes within what is called an edge network, separating the local object networks and the Cloud. These edge nodes provide interoperability, real-time interaction, routing, and, if necessary, computational delegation to the Cloud. This paper attempts to evaluate Go, a distributed systems language developed by Google, in the context of requirements set forth by Fog computing. Similar methodologies of previous literature are simulated and benchmarked against in order to assess the viability of Go in the edge nodes of Fog computing architecture.

More and more devices around us are connected to the internet and communicate to each other. This includes devices such as radon sensors. Radon is a radio active gas and is the cause of several hundred cases of lung cancer. Smart connected radon sensors can be helpful to reduce the levels of radon as they provide remote access to the user. This study examines the opportunity to connect an already existing radon sensor to the “Internet of Things”. The aim of this study has been to answer the problem “find a better solution for the IoT system and develop it”. The study was performed with a literature study of three Internet of Things platforms. This resulted in one Internet of Thing platform being used throughout the study. A database system and a system with the chosen platform was implemented and a time measurement of the different systems was performed. The study has shown that a less secured system is faster and it is also shown that the Amazon Web Service IoT Core is fast with respect to the many features offered. The study concludes that the choice of system depends on where and how the system is supposed to be implement. If the system just needs to send and store data, a regular MySQL database is enough. If the system in the future is supposed to be able to communicate with other devices, a IoT platform should be used.

Today at various locations and factories we have a lot of sensors and actuators that interact with each other and a control-unit. The control-unit is in most cases a cloud-based solution. This is in most cases a good solution. However, there is a rise in expected devices and sensors which will most likely be too much data for the existing network to handle. This paper researches if a fog-unit might be the solution to this problem. The setup of the fog-unit in the network is a unit between the cloud and the sensors and actuators. In this paper the fog-unit and sensors/actuators have been emulated on Raspberry Pi’s. The sensors are emulated using python-threads and communicate with the fog-unit using the UDP-based protocol CoAP and the fog communicates to the cloud using the TCP- based protocol MQTT. After a prototype was built it using said Raspberry Pi’s it was sent through a few measurements in the fields of bandwidth, cloud-utilization and response times. This was later compared to another setup without the fog-unit as the control setup. The result with this kind of setup was that a fog-unit lowers the cloud-utilization and use of bandwidth, however it increases the round trip time of a request from the cloud by a large amount. Which leads to the conclusion that a fog-unit in this kind of setup might be a good network solution if the response time to the cloud isn’t important.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-14T10:44:09Z No. of bitstreams: 1 DIS_MATHEUS_CRESPI_SCHENFELD_COMPLETO.pdf: 6989470 bytes, checksum: 4a16f12e8953d43da2cb18cc63c6119a (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-14T10:44:28Z (GMT) No. of bitstreams: 1 DIS_MATHEUS_CRESPI_SCHENFELD_COMPLETO.pdf: 6989470 bytes, checksum: 4a16f12e8953d43da2cb18cc63c6119a (MD5) / Made available in DSpace on 2017-11-14T10:44:39Z (GMT). No. of bitstreams: 1 DIS_MATHEUS_CRESPI_SCHENFELD_COMPLETO.pdf: 6989470 bytes, checksum: 4a16f12e8953d43da2cb18cc63c6119a (MD5) Previous issue date: 2017-03-23 / Internet of Things (IoT) is considered a computational evolution that advocates the existence of a large number of physical objects embedded with sensors and actuators, connected by wireless networks and communicating through the Internet. From the beginning of the concept to the present day, IoT is widely used in the various sectors of industry and also in academia. One of the needs encountered in these areas was to be connected to IoT devices or subsystems throughout the world. Thus, cloud computing gains space in these scenarios where there is a need to be connected and communicating with a middleware to perform the data processing of the devices. The concept of cloud computing refers to the use of memory, storage and processing of shared resources, interconnected by the Internet. However, IoT applications sensitive to communication latency, such as medical emergency applications, military applications, critical security applications, among others, are not feasible with the use of cloud computing, since for the execution of all calculations and actions messaging between devices and the cloud is required. Solving this limitation found in the use of cloud computing, the concept of fog computing arises and whose main idea is to create a federated processing layer, still in the local network of the computing devices of the ends of the network. In addition to fog computing, there is also edge computing operating directly on the devices layer, performing some kind of processing, even with little computational complexity, in order to further decrease the volume of communication, besides collaborating to provide autonomy in decision making yet in the Things layer. A major challenge for both fog and edge computing within the IoT scenario is the definition of a system architecture that can be used in different application domains, such as health, smart cities and others. This work presents a system architecture for IoT devices capable of enabling data processing in the devices themselves or the closest to them, creating the edge computing layer and fog computing layer that can be applied in different domains, improving Quality of Services (QoS) and autonomy in decision making, even if the devices are temporarily disconnected from the network (offline). The validation of this architecture was done within two application scenarios, one of public lighting in smart city environment and another simulating an intelligent agricultural greenhouse. The main objectives of the tests were to verify if the use of the concepts of edge and fog computing improve system efficiency compared to traditional IoT architectures. The tests revealed satisfactory results, improving connection times, processing and delivery of information to applications, reducing the volume of communication between devices and core middleware, and improving communications security. It also presents a review of related work in both academia and industry. / Internet das Coisas (IoT) ? considerada uma evolu??o computacional que preconiza a exist?ncia de uma grande quantidade de objetos f?sicos embarcados com sensores e atuadores, conectados por redes sem fio e que se comunicam atrav?s da Internet. Desde o surgimento do conceito at? os dias atuais, a IoT ? amplamente utilizada nos diversos setores da ind?stria e tamb?m no meio acad?mico. Uma das necessidades encontradas nessas ?reas foi a de estar conectado com dispositivos ou subsistemas de IoT espalhados por todo o mundo. Assim, cloud computing ganha espa?o nesses cen?rios, onde existe a necessidade de estar conectado e se comunicando com um middleware para realizar o processamento dos dados dos dispositivos. O conceito de cloud computing refere-se ao uso de mem?ria, armazenamento e processamento de recursos compartilhados, interligados pela Internet. No entanto, aplica??es IoT sens?veis ? lat?ncia de comunica??o, tais como, aplica??es m?dico-emergenciais, aplica??es militares, aplica??es de seguran?a cr?tica, entre outras, s?o invi?veis com o uso de cloud computing, visto que para a execu??o de todos os c?lculos e a??es ? necess?ria a troca de mensagens entre dispositivos e nuvem. Solucionando essa limita??o encontrada na utiliza??o de cloud computing, surge o conceito de fog computing, cuja ideia principal ? criar uma camada federada de processamento ainda na rede local dos dispositivos de computa??o das extremidades da rede. Al?m de fog computing tamb?m surge edge computing operando diretamente na camada dos dispositivos, realizando algum tipo de processamento, mesmo que de pouca complexidade computacional, a fim de diminuir ainda mais o volume de comunica??o, al?m de colaborar para prover autonomia na tomada de decis?es ainda na camada das coisas. Um grande desafio tanto para fog quanto para edge computing dentro do cen?rio de IoT ? a defini??o de uma arquitetura de sistema que possa ser usada em diferentes dom?nios de aplica??o, como sa?de, cidades inteligentes entre outros. Esse trabalho apresenta uma arquitetura de sistema para dispositivos IoT capaz de habilitar o processamento de dados nos pr?prios dispositivos ou o mais pr?ximo deles, criando a camada de edge e fog computing que podem ser aplicadas em diferentes dom?nios, melhorando a Qualidade dos Servi?os (QoS) e autonomia na tomada de decis?o, mesmo se os dispositivos estiverem temporariamente desconectados da rede (offline). A valida??o dessa arquitetura foi feita dentro de dois cen?rios de aplica??o, um de ilumina??o p?blica em ambiente de IoT e outro simulando uma estufa agr?cola inteligente. Os principais objetivos das execu??es dos testes foram verificar se a utiliza??o dos conceitos de edge e fog computing melhoram a efici?ncia do sistema em compara??o com arquiteturas tradicionais de IoT. Os testes revelaram resultados satisfat?rios, melhorando os tempos de conex?o, processamento e entrega das informa??es ?s aplica??es, redu??o do volume de comunica??o entre dispositivos e core middleware, al?m de melhorar a seguran?a nas comunica??es. Tamb?m ? apresentada uma revis?o de trabalhos relacionados tanto no meio acad?mico como no da ind?stria.

Increasing number of devices demand for low latency and high-speed data transmission require that the computation resources to be closer to users. The emerging Mobile Edge Computing (MEC) technology aims to bring the advantages of cloud computing which are computation, storage and networking capabilities in close proximity of user. MEC servers are also integrated with cloud servers which give them flexibility of reaching vast computational power whenever needed. In this thesis, leveraging the idea of Mobile Edge Computing, we propose algorithms for cost-efficient and energy-efficient the placement of Mobile Edge nodes. We focus on uplink energy-efficiency which is essential for certain applications including augmented reality and connected vehicles, as well as extending battery life of user equipment that is favorable for all applications. The experimental results show that our proposed schemes significantly reduce the uplink energy of devices and minimizes the number of edge nodes required in the network.

Clouds are powerful computer centers that provide computing and storage facilities that can be remotely accessed. The flexibility and cost-efficiency offered by clouds have made them very popular for business and web applications. The use of clouds is now being extended to safety-critical applications such as factories. However, cloud services do not provide time predictability which creates a hassle for such time-sensitive applications. Moreover, delays in the data communication between clouds and the devices the clouds control are unpredictable. Therefore, to increase predictability an intermediate layer between devices and the cloud is introduced. This layer, the Fog layer, aims to provide computational resources closer to the edge of the network. However, the fog computing paradigm relies on resource-constrained nodes, creating new potential challenges in resource management, scalability, and reliability. Solutions such as lightweight virtualization technologies can be leveraged for solving the dichotomy between performance and reliability in fog computing. In this context, container-based virtualization is a key technology providing lightweight virtualization for cloud computing that can be applied in fog computing as well. Such container-based technologies provide fault tolerance mechanisms that improve the reliability and availability of application execution.  By the study of a robotic use-case, we have realized that persistent data storage for stateful applications at the fog layer is particularly important. In addition, we identified the need to enhance the current container orchestration solution to fit fog applications executing in container-based architectures. In this thesis, we identify open challenges in achieving dependable fog platforms. Among these, we focus particularly on scalable, lightweight virtualization, auto-recovery, and re-integration solutions after failures in fog applications and nodes. We implement a testbed to deploy our use-case on a container-based fog platform and investigate the fulfillment of key dependability requirements. We enhance the architecture and identify the lack of persistent storage for stateful applications as an important impediment for the execution of control applications. We propose a solution for persistent fault-tolerant storage at the fog layer, which dissociates storage from applications to reduce application load and separates the concern of distributed storage. Our solution includes a replicated data structure supported by a consensus protocol that ensures distributed data consistency and fault tolerance in case of node failures. Finally, we use the UPPAAL verification tool to model and verify the fault tolerance and consistency of our solution.

The rise of IoT-based system is making an impact on our daily lives and environment. Fog Computing is a paradigm to utilize IoT data and process them at the first hop of access network instead of distant clouds, and it is going to bring promising applications for us. A mature framework for fog computing still lacks until today. In this study, we propose an approach for monitoring fog nodes in a distributed system using the FogFlow framework. We extend the functionality of FogFlow by adding the monitoring capability of Docker containers using cAdvisor. We use Prometheus for collecting distributed data and aggregate them. The monitoring data of the entire distributed system of fog nodes is accessed via an API from Prometheus. Furthermore, the monitoring data is used to perform the ranking of fog nodes to choose the place to place the serverless functions (Fog Function). The ranking mechanism uses Analytical Hierarchy Processes (AHP) to place the fog function according to resource utilization and saturation of fog nodes’ hardware. Finally, an experiment test-bed is set up with an image-processing application to detect faces. The effect of our ranking approach on the Quality of Service is measured and compared to the current FogFlow.

Philosophiae Doctor - PhD / Cloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods.

The number of IoT devices and their respective data is increasing for each day impacting the traditional architecture model of solely using the cloud for processing and storage in a negative way. This model may therefore need a supporting model to alleviate the different challenges for future IoT applications. Several researchers have described and presented algorithms and models with focus on distributed architecture models. The main issues with these however is that they fall short when it comes to the implementation and distribution of tasks. The former issue is that they are not implemented on actual hardware but simulated in a constrained environment. The latter issue is that they are not considering sharing a single task but to distribute a whole task. The objective of this thesis is therefore to present the different challenges regarding the traditional architecture model, investigate the research gap for the IoT and the different computing paradigms. Together with this implementing and evaluating a future architecture model capable of collaboration for the completion of a generated task on multiple off-the-shelf hardware. This model is evaluated based on task completion time, data size, and scalability. The results show that the different testbeds are capable communicating and splitting a single task to be completed on multiple devices. They further show that the testbeds containing multiple devices are performing better regarding completion time and do not suffer from noticeable scalability issues. Lastly, they show that the completion time drops remarkably for tasks that are split and distributed.

A smart home is an apartment or a house in which smart devices communicate with each other to improve key areas of daily life, such as comfort, security or energy consumption. Therefore, the smart home domain is user-centric and exhibits characteristics that distinguish it from other application domains of the Internet of Things. Specifically, this concerns the existence of different 'regular' and 'smart' devices, but also the basic arrangement of each household, which is highly individual. As a result, the realization of analytics scenarios in the smart home domain is influenced by household-specific requirements regarding the configuration, composition and execution of analytics tasks. Existing approaches in scientific literature cover the resulting architectural challenges only insufficiently. With the emergence of new computing paradigms, architectural concepts and technologies, new opportunities for analytics approaches, which enable individual household insights, become evident. For this reason, the objective of this work is the design of an Internet of Things analytics architecture for smart home applications, which supports the flexible deployment of analytics pipelines, therefore enabling the generation of individual household insights. In order to achieve this goal, challenges for Internet of Things analytics architectures are identified and analyzed by conducting a literature review. Based on the resulting challenges catalog, an architectural model is designed that facilitates the processing and analysis of streaming data from smart devices of different kinds. The developed architecture utilizes the fog computing paradigm, therefore allowing the deployment and execution of analytics pipelines in the cloud as well as at edge of the network. The architectural model is the foundation for a prototype, which is implemented to evaluate the proposed solution. The evaluation is performed by conducting several experiments, which are designed in order to validate the prototypes feasibility to address the found challenges. The main contributions of this work are a challenges catalog for Internet of Things analytics architectures, an architectural model for analytics in smart home applications as well as a prototype, which is based on it.