Framtidens fordon kommer vara väldigt annorlunda jämfört med dagens fordon. Stor del av förändringen kommer ske med hjälp av IoT. Världen kommer bli oerhört uppkopplat, sensorer kommer kunna ta fram data som de flesta av oss inte ens visste fanns. Mer data betyder även mer problem. Enorma mängder data kommer genereras och distribueras av framtidens IoT-enheter och denna data behöver analyseras och lagras på effektiva sätt med hjälp av Big data principer. Fog computing är en utveckling av Cloud tekniken som föreslås som en lösning på många av de problem IoT lider utav. Är tradionella lagringsmöjligheter och analyseringsverktyg tillräckliga för den enorma volymen data som kommer produceras eller krävs det nya tekniker för att stödja utvecklingen? Denna studie kommer försöka besvara frågeställningen: ”Vilka problem och möjligheter får utvecklingen av Fog computing i personbilar för konsumenter?” Frågeställningen besvaras genom en systematisk litteraturstudie. Den systematiska litteraturstudien syfte är identifiera och tolka tidigare litteratur och forskning. Analys av materialet har skett med hjälp av öppen kodning som har använts för att sortera och kategorisera data. Resultat visar att tekniker som IoT, Big data och Fog computing är väldigt integrerade i varandra. I framtidens fordon kommer det finns mycket IoTenheter som producerar enorma mängder data. Fog computing kommer bli en effektiv lösning för att hantera de mängder data från IoT-enheterna med låg fördröjning. Möjligheterna blir nya applikationer och system som hjälper till med att förbättra säkerheten i trafiken, miljön och information om bilens tillstånd. Det finns flera risker och problem som behöver lösas innan en fullskalig version kan börja användas, risker som autentisering av data, integriteten för användaren samt bestämma vilken mobilitetsmodell som är effektivast. / Future vehicles will be very different from today's vehicles. Much of the change will be done using the IoT. The world will be very connected, sensors will be able to access data that most of us did not even know existed. More data also means more problems. Enormous amounts of data will be generated and distributed by the future's IoT devices, and this data needs to be analyzed and stored efficiently using Big data Principles. Fog computing is a development of Cloud technology that is suggested as a solution to many of the problems IoT suffer from. Are traditional storage and analysis tools sufficient for the huge volume of data that will be produced or are new technologies needed to support development? This study will try to answer the question: "What problems and opportunities does the development of Fog computing in passenger cars have for consumers?" The question is answered by a systematic literature study. The objective of the systematic literature study is to identify and interpret previous literature and research. Analysis of the material has been done by using open coding where coding has been used to sort and categorize data. Results show that technologies like IoT, Big data and Fog computing are very integrated in each other. In the future vehicles there will be a lot of IoT devices that produce huge amounts of data. Fog computing will be an effective solution for managing the amount of data from IoT devices with a low latency. The possibilities will create new applications and systems that help improve traffic safety, the environment and information about the car's state and condition. There are several risks and problems that need to be resolved before a full-scale version can be used, such as data authentication, user integrity, and deciding on the most efficient mobility model.

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET ++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.

O aumento no número de dispositivos móveis, como smartphones, tablets e laptops, e o avanço em seu potencial computacional permitiu considerá-los como recursos computacionais. O uso de recursos computacionais com maior proximidade vem crescendo ano após ano, sendo chamado de Fog computing, em que os elementos na borda da Internet são explorados, uma vez que os serviços computacionais convencionais podem estar indisponíveis ou sobrecarregados. Dessa forma, este projeto de Mestrado tem como foco possibilitar o uso de dispositivos móveis no provimento de serviços computacionais entre si de forma colaborativa através da heurística Maximum Regret adaptada, que busca alocar tarefas computacionais em dispositivos locais de forma a minimizar o consumo de energia e evitar dispositivos não confiáveis. Também há uma meta-heurística em um nível global, que interconecta os diferentes aglomerados de dispositivos móveis na borda da Internet, e possui informações globais de Quality of Service (QoS). Foram realizados experimentos que mostraram que evitar dispositivos móveis como recursos com um baixo grau de confiabilidade possibilitou diminuir o impacto no consumo de energia, além de ser possível diminuir os tempos de resposta e de comunicação ao ajustar a política de seleção de aglomerados externos. / The increasing number of mobile devices, such as smartphones, tablets and laptops, as well as advances in their computing power have enabled us to consider them as resources, exploring the proximity. The use of near computing resources is growing year by year, being called as Fog computing, where the elements on the edge of the Internet are exploited, once the computer services providers could be unavailable or overloaded. Thus, this Masters project focuses on using mobile devices to provide computing services among them through a heuristic called Adapted Maximum Regret, which tries to minimize energy consumption and avoid untrustable devices. There is also top-level metaheuristic which interconnects different clusters of devices on the edge of the Internet with global information to guarantee Quality of Services (QoS). We conducted a set of experiments that showed us to avoid devices with a high degree of failures to save more energy when allocating tasks among them, as well as decreasing the applications response time and communication through adjusts in the selection algorithm of external agglomerates.

Alors que l’informatique en brouillard amène les ressources de traitement et de stockage à la périphérie du réseau, il existe un besoin croissant de placement automatisé (c.-à-d. La sélection de l'hôte) pour déployer des applications distribuées. Un tel placement doit être conforme aux besoins en ressources des applications dans une infrastructure de brouillard hétérogène et dynamique, et traiter la complexité apportée par les applications Internet des objets (IoT) liées aux capteurs / actionneurs. Cette thèse présente un modèle, une fonction objective et des heuristiques pour résoudre le problème de la mise en place d'applications IoT distribuées dans le brouillard. En combinant les heuristiques proposées, notre approche est capable de gérer les problèmes à grande échelle et de prendre efficacement des décisions de placement adaptées à l'objectif - en optimisant les performances des applications placées. L'approche proposée est validée par une analyse de complexité et une simulation comparative avec des tailles et des applications de tailles variables. / As fog computing brings processing and storage resources to the edge of the network, there is an increasing need of automated placement (i.e., host selection) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure, and deal with the complexity brought by Internet of Things (IoT) applications tied to sensors and actuators. This paper presents four heuristics to address the problem of placing distributed IoT applications in the fog. By combining proposed heuristics, our approach is able to deal with large scale problems, and to efficiently make placement decisions fitting the objective: minimizing placed applications' average response time. The proposed approach is validated through comparative simulation of different heuristic combinations with varying sizes of infrastructures and applications.

O aumento no número de dispositivos móveis, como smartphones, tablets e laptops, e o avanço em seu potencial computacional permitiu considerá-los como recursos computacionais. O uso de recursos computacionais com maior proximidade vem crescendo ano após ano, sendo chamado de Fog computing, em que os elementos na borda da Internet são explorados, uma vez que os serviços computacionais convencionais podem estar indisponíveis ou sobrecarregados. Dessa forma, este projeto de Mestrado tem como foco possibilitar o uso de dispositivos móveis no provimento de serviços computacionais entre si de forma colaborativa através da heurística Maximum Regret adaptada, que busca alocar tarefas computacionais em dispositivos locais de forma a minimizar o consumo de energia e evitar dispositivos não confiáveis. Também há uma meta-heurística em um nível global, que interconecta os diferentes aglomerados de dispositivos móveis na borda da Internet, e possui informações globais de Quality of Service (QoS). Foram realizados experimentos que mostraram que evitar dispositivos móveis como recursos com um baixo grau de confiabilidade possibilitou diminuir o impacto no consumo de energia, além de ser possível diminuir os tempos de resposta e de comunicação ao ajustar a política de seleção de aglomerados externos. / The increasing number of mobile devices, such as smartphones, tablets and laptops, as well as advances in their computing power have enabled us to consider them as resources, exploring the proximity. The use of near computing resources is growing year by year, being called as Fog computing, where the elements on the edge of the Internet are exploited, once the computer services providers could be unavailable or overloaded. Thus, this Masters project focuses on using mobile devices to provide computing services among them through a heuristic called Adapted Maximum Regret, which tries to minimize energy consumption and avoid untrustable devices. There is also top-level metaheuristic which interconnects different clusters of devices on the edge of the Internet with global information to guarantee Quality of Services (QoS). We conducted a set of experiments that showed us to avoid devices with a high degree of failures to save more energy when allocating tasks among them, as well as decreasing the applications response time and communication through adjusts in the selection algorithm of external agglomerates.

As the number of Internet of Things (IoT) devices that are used daily is increasing, the inadequacy of cloud computing to provide neseccary IoT-related features, such as low latency, geographic distribution and location awareness, is becoming more evident. Fog computing is introduced as a new computing paradigm, in order to solve this problem by extending the cloud‟s storage and computing resources to the network edge. However, the introduction of this new paradigm is also confronted by various security threats and challenges since the security practices that are implemented in cloud computing cannot be applied directly to this new architecture paradigm. To this end, various papers have been published in the context of fog computing security, in an effort to establish the best security practices towards the standardization of fog computing. In this thesis, we perform a systematic literature review of current research in order to provide with a classification of the various security threats and challenges in fog computing. Furthermore, we present the solutions that have been proposed so far and which security challenge do they address. Finally, we attempt to distinguish common aspects between the various proposals, evaluate current research on the subject and suggest directions for future research.

Fog computing is a distributed computing paradigm that brings data processing from remote cloud data centers into the vicinity of the edge of the network. The computation is performed closer to the source of the data, and thus it decreases the time unpredictability of cloud computing that stems from (i) the computation in shared multi-tenant remote data centers, and (ii) long distance data transfers between the source of the data and the data centers. The computation in fog computing provides fast response times and enables latency sensitive applications. However, industrial systems require time-bounded response times, also denoted as RT. The correctness of such systems depends not only on the logical results of the computations but also on the physical time instant at which these results are produced. Time-bounded responses in fog computing are attributed to two main aspects: computation and communication.    In this thesis, we explore both aspects targeting soft RT applications in fog computing in which the usefulness of the produced computational results degrades with real-time requirements violations. With regards to the computation, we provide a systematic literature survey on a novel lightweight RT container-based virtualization that ensures spatial and temporal isolation of co-located applications. Subsequently, we utilize a mechanism enabling RT container-based virtualization and propose a solution for orchestrating RT containers in a distributed environment. Concerning the communication aspect, we propose a solution for a dynamic bandwidth distribution in virtualized networks.

Historically virtual machines have been the backbone of the cloud-industry, allowing cloud-providers to offer virtualized multi-tenant solutions. A key aspect of the cloud is its flexibility and abstraction of the underlying hardware. Virtual machines can enhance this aspect by enabling support for live migration and failover. Live migration is the process of moving a running virtual machine from one host to another and failover ensures that a failed virtual machine will automatically be restarted (possibly on another host). Today, as containers continue to increase in popularity and make up a larger portion of the cloud, often replacing virtual machines, it becomes increasingly important for these processes to be available to containers as well. However, little support for container live migration and failover exists and remains largely experimental. Furthermore, no solution seems to exists that offers both live migration and failover for containers in a unified solution. The thesis presents a proof-of-concept implementation and description of a system that enables support for both live migration and failover for containers by extending current container migration techniques. It is able to offer this to any OCI-compliant container, and could therefore potentially be integrated into current container and container orchestration frameworks. In addition, measurements for the proof-of-concept implementation are provided and used to compare the proof-of-concept implementation to a current container migration technique. Furthermore, the thesis presents an overview of the history and implementation of containers, current migration techniques, and metrics that can be used for measuring different migration techniques are introduced. The paper concludes that current container migration techniques can be extended in order to support both live migration and failover, and that in doing so one might expect to achieve a downtime equal to, and total migration time lower than that of pre-copy migration. Supporting both live migration and failover, however, comes at a cost of an increased amount of data needed to be transferred between the hosts.

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.

With the dramatic growth of vehicles around the world, Vehicular Ad-hoc Networks (VANETs) have been proposed as a solution to advance road safety, improve transportation efficiency, and satisfy road users. In the VANET environment, vehicles communicate with each other and with road infrastructure in an ad-hoc manner. This communication may be safety-related or non-safety-related and may often include vehicle information (e.g., location, direction, speed, and control), road conditions, and events. A key component in assessing the veracity of the information is the trustworthiness of the information source. Thus, trust evaluation is one of the main requirements of VANET design. In this work, we investigate performance improvements in the trust evaluation framework of VANETs. First, we propose a risk-based trust evaluation model (RTEAM) to estimate the risk of taking action or refraining from action regarding a reported event (in case of receiving conflicting messages about the event's existence). Some trust metrics such as direct trust, hop-based trust values, proximity to the event, and consequences of acting on a wrong decision are used to estimate the risk of the vehicle’s actions. Vehicles make individual decisions by seeking the action with the lowest risk. Second, we propose a fog-based reputation evaluation model (FREM) to support trust management framework. We promote fog computing as a new paradigm since it can provide several services to users in the edge layer. In our work, Fog supports the decision-making process in the reputation evaluation framework. Fog nodes play a key role in collecting vehicles' reputation records and cooperating with the roadside units (RSUs) to update these records. We propose the use of Digital Trustworthiness Cards (DTC), where the latest reputation evaluation of a vehicle automatically appears on its card. The benefits of the DTC are twofold: 1) the communication load on vehicles is reduced, and 2) historical trust records are established for each vehicle. We also take advantage of fog’s familiarity and greater knowledge of the vehicles that frequently visit its zones; with more intimate knowledge, fog can smartly employ vehicles to perform specific tasks based on their experiences. Further, we implement a strategy for establishing trust based on specific task categories. This permits a nuanced evaluation of the vehicle best suited for the task at hand and has the further benefit of preventing malicious vehicles from being naively trusted based on successful completion of unimportant or non-safety-related tasks. Finally, we expand the role of the fog in the decision-making process when vehicles need to ensure the existence of serious events. We propose a fog-based event validation model (FEVM) to validate the event’s existence through cooperation between vehicles and fog nodes. The vehicles are used as mobile fog nodes, which compute their confidence in events based on the available information. Fog nodes then validate the event after combining vehicles’ confidence values by applying the Extended Dempster-Shafer (EDS) theory of evidence. To test our proposed models, we conduct many experiments to investigate their performance and compare them with other existing models.