Nowadays, there is a mobile application for almost every need, for instance, training, games, and news applications. Almost all the companies have also their own mobile applications that facilitate the daily communication between their employees and supervisors. In this thesis, the development process of an Android application for the company Askås is created, where the employees can for example send a Flex application or sick leave application, see their registered personal information and edit them. The final result of the project is almost what Askås asked for, An Android application that facilitates their employees’ daily needs. The development process, the results, and the encountered problems are also represented in a more detailed manner in the thesis. 

The web application Testpersonbokaren is used by Nordic Medtest to book fake test persons for various actors in healthcare so that they can test and validate their environments without being dependent on real personal data. The purpose of this project is to develop a new version of Testpersonbokaren with new technologies and frameworks as the existing version has several problems and limitations. This was achieved by creating a Node.js server and developing two new web interfaces for Testpersonbokaren, where one is only used by Nordic Medtest and the other is used by Sweden's regions, municipalities and private actors. Both web interfaces are developed with the React framework. The new Testpersonbokaren has a more modern look and more functionality than the old one. Unlike the old version, the new one has good documentation, which makes the further development of the web application easier. The new Testpersonbokaren is ready to be put into operation at Nordic Medtest.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.

In-Band Network Telemetry (INT) is a new technique in the area of Software-defined networking (SDN) for monitoring SDN enabled networks. INT monitoring provides fine-grained INT data with less load on the control plane since monitoring is done directly at the data plane. The collected INT data is added as packet headers "In-band" at each INT device along the flow path. The INT data is later composed into telemetry packets which are sent to a collector that is responsible for processing the INT data. The collector of the INT data needs to have good performance since there is a large amount of data that has to be processed quickly. INTCollector, a high performance collector of INT data, is a response to this challenge. The performance of INTCollector is optimized by implementing critical parts in eXpress Data Path (XDP), enabling fast packet processing. INTCollector is, moreover, able to reduce processing of INT data and the need for storage space since it employs a strategy where only important INT data is collected, decided by an internal event detection mechanism.The event detection mechanism in INTCollector can however be offloaded to the INT device itself, with possible perfomance benefits for the collector. Programming Protocol-Independent Packet Processors (P4) opens up this possibility by providing a language for programming network devices. This thesis presents an implementation of INT in P4 with offloaded event detection. We use a programmable P4 testbed to perform an experimental evaluation, which reveal that offloading does indeed benefit INTCollector in terms of performance. Offloading also comes with the advantage of  allowing parameters of the event detection logic at the data plane to be accessible to the control plane.

The Helios voting scheme is well studied including formal proofs for verifiability and ballot privacy. However, depending on its version, the scheme provides either participation privacy (hiding who participated in the election) or verifiability against malicious bulletin board (preventing election manipulation by ballot stuffing), but not both at the same time. It also does not provide receipt-freeness, thus enabling vote buying by letting the voters construct receipts proving how they voted. Recently, an extension to Helios, further referred to as KTV-Helios, has been proposed that claims to provide these additional security properties. However, the authors of KTV-Helios did not prove their claims. Our contribution is to provide formal definitions for participation privacy and receipt-freeness that we applied to KTV-Helios. In order to evaluate the fulfillment of participation privacy and receipt-freeness, we furthermore applied the existing definition of ballot privacy, which was also used for evaluating the security of Helios, in order to show that ballot privacy also holds for KTV-Helios

Change point detection (CPD) is the method of detecting sudden changes in timeseries, and its importance is great concerning network traffic. With increased knowledge of occurring changes in data logs due to updates in networking equipment,a deeper understanding is allowed for interactions between the updates and theoperational resource usage. In a data log that reflects the amount of network traffic, there are large variations in the time series because of reasons such as connectioncount or external changes to the system. To circumvent these unwanted variationchanges and assort the deliberate variation changes is a challenge. In this thesis, we utilize data logs retrieved from a network equipment vendor to detect changes, then compare the detected changes to when firmware/signature updates were applied, configuration changes were made, etc. with the goal to achieve a deeper understanding of any interaction between firmware/signature/configuration changes and operational resource usage. Challenges in the data quality and data processing are addressed through data manipulation to counteract anomalies and unwanted variation, as well as experimentation with parameters to achieve the most ideal settings. Results are produced through experiments to test the accuracy of the various change pointdetection methods, and for investigation of various parameter settings. Through trial and error, a satisfactory configuration is achieved and used in large scale log detection experiments. The results from the experiments conclude that additional information about how changes in variation arises is required to derive the desired understanding.

SDN aims to facilitate the management of increasingly complex, dynamic network environments and optimize the use of the resources available therein with minimal operator intervention. To this end, SDN controllers maintain a global view of the network topology and its state. However, the extraction of information about network flows and other network metrics remains a non-trivial challenge. Network applications exhibit a wide range of properties, posing diverse, often conflicting, demands towards the network. As these requirements are typically not known, controllers must rely on error-prone heuristics to extract them. In this work, we develop a framework which allows applications deployed in an SDN environment to explicitly express their requirements to the network. Conversely, it allows network controllers to deploy policies on end-hosts and to supply applications with information about network paths, salient servers and other relevant metrics. The proposed approach opens the door for fine grained, application-aware resource optimization strategies in SDNs

Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

Collaboration involves people working together and sharing information toward a common goal. The Architecture, Engineering, and Construction (AEC) industry is known for being the least digitalized and highly fragmented work environment involving many processes across the supply chain. It creates a challenge to encourage collaboration among geographically distributed collaborators. Cloud computing has become more commonly used to outsource data for its efficient storage with timely updates and easy accessibility. Information is available for all collaborators and enables easy sharing and storing for collaborative work in real-time. The benefits of using cloud computing follow up with risks and challenges in usability and security.

     This study aims to investigate and describe construction collaborators’ experiences, expectations, and requirements of cloud services in construction projects concerning information security and usability. The study is based on a qualitative method with its focus on obtaining in-depth information through open-ended semi-structured interviews with seven specialists from the AEC industry.

     Participants’ experiences and thoughts are thematically categorized and analyzed. The result shows that the usability of cloud computing has become an important core for efficient collaboration with features of sharing, storage, and interaction among collaborators. Cloud-based collaborations security is more vulnerable to external attacks than internal human error. International cloud-based collaborations could have legal problems due to the compliance issues of GDPR with international laws.

The increase in traffic volumes as well as the heterogeneity in network infrastructure in the upcoming 5G cellular networks will lead to a dramatic increase in volumes of control traffic, i.e., signaling traffic, in the networks. Moreover, the increasing number of low-power devices with an on-off behavior to save energy will generate extra control traffic. These increased traffic volumes for signaling traffic, often generated as bursts of messages, will challenge the signaling application timing requirements on transmission. One of the major transport protocols deployed for signaling traffic in cellular networks is the Stream Control Transmission Protocol (SCTP), with support for multiple paths as well as for independent data flows. This paper evaluates transmission over several paths in SCTP to keep the latency low despite increasing traffic volumes. We explore different transmission strategies and find that concurrent multipath transfer over several paths will significantly reduce latency for transmission over network paths with the same or similar delay. Still, over heterogeneous paths, careful, continuous sender scheduling is crucial to keep latency low. To this end, we design and evaluate a sender scheduler that considers path characteristics as well as queuing status and data flows of different priority to make scheduling decisions. Our results indicate that by careful dynamic sender scheduling, concurrent multipath transfer could lead to reduced latency for signaling traffic irrespective of path or traffic characteristics.

Artificial intelligence has become commercialized and has created a huge demand on the market. However, few people know what the technology means and even scientist struggle to find a universal definition. The technology is complex and versatile with elements making it controversial. The concept of artificial intelligence has been studied in the technical field and several publications has made efforts to predict the impact that the technology will have on our societies in the future. Even if the technology has created a great demand on the market, empirical findings of how this technology is affecting organizations is lacking. There is thus no current research to help organizations adapt to this new technology.The purpose of this study is to start cover that research gap with empirical data to help organizations understand how they are affected by this paradigm shift in technology. The study is conducted as a single case study at a public organization with middle technological skills. Data has been collected through interviews, observations and reviewing of governing documents. Seventeen interviews were held with employees with different work roles in the administration. The data was then analyzed from a combined framework including technology, organizational adaptation and social sustainability.The study found that the organization is reactive in its adaptation process and lacking an understanding of the technology. The findings show that the concept of artificial intelligence is hard to understand but applicable and tangible examples facilitates the process. A better information flow would help the investigated organization to become more proactive in its adaptation and better utilize its personnel. The findings also show that there are ethical issues about the technology that the organization needs to process before beginning an implementation. The researcher also argues the importance of a joint framework when analyzing the organizational impact of artificial intelligence due to its complexity.

This paper describes the approach and preliminary results from the research project EUX2010sec. The project works closely with Voice-over-IP (VoIP) companies and users. It aims at providing better security of opera source VoIP installations. The work towards this goal is organized by gathering researchers and practitioners around several scientific activities that range from security modeling and verification up to testbed testing. The expected outcomes of the project are a solid scientific and practical understanding of the security options for setting tip VoIP infrastructures, particular guidance on secure, typical setups of such infrastructures, The project's special focus is on producing results relevant to the practitioners in the project, aiming at the stimulation of innovation and the provision of highest quality in open-source based VoIP products and services.

Knowledge Graph (KG) is an emerging topic of research. The promise of KGs is to be able to turn data into knowledge by supplying the data with context at the source. This could in turn allow machines to make sense of data by inference; looking at the context of the data and being able to derive knowledge from its context and relations, thus allowing for new ways of finding value in the sea of data that the world produces today. Working with KGs today involves many steps that are open to simplification and improvement, especially in regards to usability. In this thesis, we've aimed to design and produce an application that can be used to modify, extend and build KGs. The work includes the front-end library VueJS, the Scalable Vector Graphics (SVG) library D3 and the graph database Stardog. The project has made use of Scrum methodology to distribute and plan the work that took place over a span of six months, with two developers working halftime (20 hours/week). The result of the project is a working application that can be used by developers within the KG domain who want to be able to test and modify their graphs in a visual manner.

This thesis will be a risk analysis of a RFID-system for a logistical application. The system works as follows: Around Karlstad in Sweden there are three new weighing machines for lorries. The load weight will be measured for the police to control overweight and for logistical reasons such as issuing invoices and optimising the supply chain. The lorries do not have to stop to be weighed. They have to drive slowly over the weighing machine, so the loss of time is minimal. The lorries will be identified via RFID-tags. So every time a lorry will be driven over the weighing machine, the identification number and the measured weight will be logged and send to a database. In the future it is planed to store the weight on the tag itself. The task is now to analyse the RFID-communication and the transmission to the database. The thesis will contain several parts. First RFID in general and how RFID will be used in the application-scenario will be described. Next sections will be about the security and privacy requirements and the risks in detail. Then possible solutions are outlined and concrete suggestions are presented. Finally a conclusion will be drawn, which will show that the application has a low level of security.

As the number of APIs on the internet increases, so does the need for guidance on how to use these. In APIs where the users have varying experience, the necessary documentation and instructions needed becomes harder to pinpoint from a developer's point of view. In this project, the objective is to conclude what constitutes a good documentation for an API and apply it to an existing API, as requested from Askås. The pre-existing material was analysed to build an understanding for which API calls were supported, and was to be re-built in such a way that it would be easily implemented in Askås' new API documentation site. To bring clarity to what constitutes good API documentation, field studies were examined. With this, the project could be executed according to the examined studies, as well as feedback from the client. The result is a tool that can be used to make API calls where the parametres are explained in a dynamic fashion - developed in React, expressed in Docusaurus, waiting to be merged into Askås new documentation site.

Machine learning algorithms that run on Internet of Things sensory data requires high data quality to produce relevant output. By providing data cleaning at the edge, cloud infrastructures performing AI computations is relieved by not having to perform preprocessing. The main problem connected with edge cleaning is the dependency on unsupervised pre-processing as it leaves no guarantee of high quality output data. In this thesis an IoT gateway is extended to provide cleaning and live configuration of cleaning parameters before forwarding the data to a server cluster. Live configuration is implemented to be able to fit the parameters to match a time series and thereby mitigate quality issues. The gateway framework performance and used resources of the container was benchmarked using an MQTT stress tester. The gateway’s performance was under expectation. With high-frequency data streams, the throughput was below50%. However, these issues are not present for its Glava Energy Center connector, as their sensory data generates at a slower pace.

The market-leading wage and PA-.system for the Swedish public sector Palasso is developed in Karlstad. In order to achieve the highest quality of code , the developing team behind Palasso has been conducting extensive testing. Testing is done to ensure that the software does what it should do. However, the testing has been done at a high level. In order to continue to evolve, the group that develops Palasso has started to look at the new development methods that have become popular in the computer industry in recent years. These methods are based on the use of tests as low as possible so-called unit tests.

To examine the possibility for Palasso to use unit testing, the study has been divided into two parts. First, a study of existing frameworks to find the best suited one for Palasso. Then, by using the selected framework, a second study is conducted to see if it is possible integrate unit testing with the Palasso system. This part was divided into two methods.

In the first method a existing functionality was redeveloped to show how the code should be structured and implemented to be suitable for unit tests. In the second method unit testing was introduced on legacy code to prove that it was possible to introduce unit test and that small changes can make the code more testable.

The best suited framework for Palasso was TestNG. TestNG was chosen because it was easy to use and it had scalability. The second study showed that it is possible to integrate unit tests with the Palasso system. But it should be introduced when new modules are developed or when existing code is modified. The introduction of unit testing provides many advantages. It is concluded that the quality of the Palassos system would benefit from using unit testing.

Developing a new product can be time- and money-consuming. Knowing whether there is a market for the product is not always obvious. This is why pretotyping is a good idea. A pretotype tests the concept and interest in products among customers. It is therefore an advantage to be able to develop pretotypes quickly. In this essay, the question of how a framework for pretotyping can be built will be answered, as well as how a pretotype of a ”smart belt” was developed. The framework consists of three components: microcontroller, cloud services and application. Communication between microcontroller and cloud is handled with an MQTT library from Microsoft. The cloud services are based in Microsoft Azure and include IoT Hub and SQL database. The application has been developed by the supervisor from the company. The framework supports communication from device to application and application to device.

Privacy notices and consent forms are the means of conveying privacy policy information to users. In Europe, a valid consent needs to be confirmed by a clear affirmative action. Despite previous research, it is not yet clear whether user engagement with consent forms via different types of interactions for confirming consent may play a significant role in effectively drawing user attention to the content, even after repeated exposure. We investigate, in a laboratory study, how different types of interactions that engage users with consent forms differ in terms of their effectiveness, efficiency, and user satisfaction. In addition, we examine if and how habituation affects user attention and satisfaction, and the time they spend on giving their consent. We conducted a controlled experiment with 80 participants in four different groups where people either were engaged actively with the policy content via Drag and Drop (DAD), Swipe, or Checkboxes, or were not actively engaged with the content (as the control condition) in a first-exposure phase and in a habituation phase. We measured user attention to consent forms along multiple dimensions, including direct, objective measurements and indirect, self-reported measures. Our results show that the different types of interactions

may affect user attention to certain parts of policy information. In particular, the DAD action results in significantly more user attention to the data items compared to other groups. However, with repeated exposure to consent forms, the difference disappears. We conclude that user engagement with policy content needs to be designed with care, so that attention to substantial policy information is increased and not negatively affected.

Machine learning and its wide range of applications is becoming increasingly prevalent in both academia and industry. This thesis will focus on the two machine learning methods convolutional neural networks and reinforcement learning. Convolutional neural networks has seen great success in various applications for both classification and regression problems in a diverse range of fields, e.g. vision for self-driving cars or facial recognition. These networks are built on a set of trainable weights optimized on data, and a set of hyperparameters set by the designer of the network which will remain constant. For the network to perform well, the hyperparameters have to be optimized separately.

The goal of this thesis is to investigate the use of reinforcement learning as a method for optimizing hyperparameters in convolutional neural networks built for classification problems. The reinforcement learning methods used are a tabular Q-learning and a new Q-learning inspired algorithm denominated max-table. These algorithms have been tested with different exploration policies based on each hyperparameter value’s covariance, precision or relevance to the performance metric. The reinforcement learning algorithms were mostly tested on the datasets CIFAR10 and MNIST fashion against a baseline set by random search.

While the Q-learning algorithm was not able to perform better than random search, max-table was able to perform better than random search in 50% of the time on both datasets. Hyperparameterbased exploration policy using covariance and relevance were shown to decrease the optimizers’ performance. No significant difference was found between a hyperparameter based exploration policy using performance and an equally distributed exploration policy.

Selective encryption is a concept in which the main goal is to reduce computational cost while providing confidentiality by encrypting only chosen parts of the information to be protected. Previous work on selective encryption has mainly been aimed towards multimedia applications in order to reduce the overhead induced by encryption while still making the information perceptually secure to a desired protection level. This was accomplished by utilizing the fact that different parts of the information have different impacts on our perception senses, i.e., eyes and ears. How computationally secure the information is when using selective encryption has however only briefly been mentioned or rudimentarily analyzed. In this paper, we therefore investigate the security implications of selective encryption by generalizing the work on entropy of selectively encrypted strings to several dimensions and applying it to bitmap images. The generalization is done by constructing information neighborhoods that capture and model information dependencies in several dimensions.

Next generation, i.e., fifth generation (5G), cellular networks will provide a significant higher capacity per area to support the ever-increasing traffic demands. In order to achieve that, many small cells need to be deployed that are connected using a combination of optical fiber links and millimeter-wave (mmWave) backhaul architecture to forward heterogeneous traffic over mesh topologies. In this paper, we present a general optimization framework for the design of policies that optimally solve the problem of where to associate a user, over which links to route its traffic towards which mesh gateway, and which base stations and backhaul links to switch off in order to minimize the energy cost for the network operator and still satisfy the user demands. We develop an optimal policy based on mixed integer linear programming (MILP) which considers different user distribution and traffic demands over multiple time periods. We develop also a fast iterative two-phase solution heuristic, which associates users and calculates backhaul routes to maximize energy savings. Our strategies optimize the backhaul network configuration at each timeslot based on the current demands and user locations. We discuss the application of our policies to backhaul management of mmWave cellular networks in light of current trend of network softwarization (Software-Defined Networking, SDN). Finally, we present extensive numerical simulations of our proposed policies, which show how the algorithms can efficiently trade-off energy consumption with required capacity, while satisfying flow demand requirements.

Face recognition using machine learning is a changing field and is used in many contexts

in today’s society, for example as an authentication method in mobile phones. Most face

recognition systems have had large budgets and strong developers behind them, but is it

possible to create a working system with a limited amount of resourses and data? This

project investigates how much data is required to produce a working face recognition

module for an office environment based on image classification. This project used a pretrained

Convolutional Neural Network (ResNet34), data collected with the help of the

client, and an image database from NVIDIA. The results show that the amount of data

required to produce and reliable model probably exceeds the amount that is reasonable

to collect from the user.

Model-based risk analysis methods use graphical models to facilitate participation, risk communication and documentation and thereby improve the risk analysis process. Currently, risk analysis methods for identity management systems (IDMSs) mainly rely on time consuming and expensive manual inspections and lack graphical models. This article introduces the executable model-based risk analysis method (EM-BRAM) with the aim of addressing these challenges. The EM-BRAM employs graphical models to enhance risk analysis in IDMSs. It identifies risk contributing factors for IDMSs and uses them as inputs to a colored petri nets (CPNs) model of a targeted IDMS. It then verifies the system’s risk using CPNs’ state space analysis and queries. Currently, risk assessment methods for identity management systems (IDMSs) are lacking. This makes it difficult to compare IDMSs based on how they enhance privacy and security of system stakeholders. This article proposes the executable model-based risk assessment method (EM-BRAM) with the aim of addressing this challenge. The EM-BRAM identifies risk factors inherent in IDMSs and uses them as inputs to a colored petri nets (CPNs) model of a targeted IDMS. It then estimates or verifies the system’s security and privacy risks using CPNs’ state space analysis and queries.

In today's society there is a significant focus on energy efficiency. Despite this, people leave lamps, televisions and computer screens etc. powered on unnecessarily. The goal of this project is to build a simple prototype for home automation where a user, via an application on a mobile phone, can switch on and off the power of an electrical products in the home and as well as being able to check whether such products are switched on or off. The prototype will also collect data on power consumption for these electrical products. This thesis describes the development of the prototype and addresses the choices made during the project. The project resulted in a prototype which after tests showed that there is potential to save energy by using home automation.

När man arbetar så blir det lätt att man faller in i rutiner. Dessa rutiner kan göra detsvårt att se att ens uppgifter kan ses som repetitiva. Det är också svårt för någon utom-stående att se dessa repetitiva mönster. Målet med detta arbete är att kunna visa för enanvändare vilka delar av dennes arbete som skulle kunna automatiseras. Detta uppnåsmed hjälp av två verktyg som vi utvecklat; en Chrome-extension som loggar vad enanvändare gör under arbetsdagen, samt ett analysverktyg i Python som analyserar datanfrån extensionen och visar möjliga repetitiva moment.Chrome-extensionen loggar varje gång användaren interagerar med ett element påen hemsida till en händelselogg. Denna händelselogg kan sedan exporteras som enJSON-fil och matas in i ett Python-skript för att analyseras och hitta repetitiva mönsteroch arbetsmoment.Efter att ha testat processen på två testfall kom vi fram till att den inte fungerar heltsom planerat, då extensionen behöver samla in mer data och analysen behöver leta eftermönster som är snarlika, inte bara identiska. Verktyget har potential att användas inomfaktiskt verksamhet men då skulle det krävas en vidareutveckling av både Chrome-extensionen samt analys-skriptet.

Time Sensitive Networking (TSN) is a standard that extends Ethernet, providing deterministic guarantees to internal communications. The standard is finding use in many different fields such as modern cars whose networks are getting increasingly complex. One problem with TSN is the NP-hard problem of scheduling network flows, the process in which schedules are made to ensure that network flows demanding real-time guarantees have their requirements met.The goal of thesis is to implement two different scheduling algorithms. The evaluation of the implementations measure how they perform in regards to finding possible schedules and for how much time they take with an increasing number of network flows to schedule. The algorithms approaches to scheduling are then discussed and judged in comparison with the results.

This thesis implements two different algorithms, a fine-grained algorithm and a coarse-grained algorithm. The difference between the algorithms lie in how fine-grained their scheduling is. Where the fine-grained algorithm adopts the lowest resolution permissible, the coarse-grained algorithm achieves the highest resolution possible in the schedule. The results show that the coarse-grained algorithm becomes unacceptably slow because of its fine-grained scheduling. In a scenario where algorithms 1 and 2 work with the same flows, the coarse-grained algorithm is 42x slower than the fine-grained algorithm in the worst case.The conclusion is that the finest possible scheduling is not a feasible approach because of it taking too much time to execute for a very small amount of flows.

In this article, we presentfindings from a European survey with 10 countries on the subject sharing of personal information and concernsof the citizens. We compare the age group 60-65 years old with the age group 50-59, and in addition compare the Nordic region with the non-nordic population.There are more similarities than differences. The survey indicates that many of the elderly 60-65 take steps to protect their personal data.

There is a rapidly growing number of advanced applications running over the internet that requires ultra-low latency and high throughput. Bufferbloat is one of the most known problems which add delay in the form of packets being enqueued into large buffers before being transmitted. This has been solved with the developments of various Active Queue Management (AQM) schemes to control how large the queue buffers are allowed to grow. Another aspect that is important today is how the available bandwidth can be shared between applications with different priorities. The Per-Packet Value (PPV) concept has been presented as a solution for resource-sharing by marking packets according to predefined marking policies. The packet value will be taken into consideration to make drop/mark decisions, which leads to higher packet values being prioritized at bottleneck links. 

In this thesis, a design of a packet value-based AQM on a programmable Barefoot Tofino switch will be presented. It will use a combination of the Proportional Integral Controller (PIE) AQM scheme and the PPV concept to make drop decisions when queuing delay is discovered. Packet value statistics are collected through the P4 programmable data plane to maintain knowledge about the distribution of packet values. With the dropping probability calculated through the PIE AQM scheme, a decision can be made about which packets should be dropped. 

An evaluation shows that with the implemented PV AQM, a low queuing delay can be achieved by dropping an appropriate amount of packets. It also shows that the PV AQM controls the resource-sharing between different traffic flows according to a predefined marking policy. 

Availability is important for a web service. It is therefore valuable to know when interferences occurs. With the help of monitoring of web services, one can acheive increased availability.Ping-Win is a monitoring service developed for the consultancy Knowit. Knowit is focusing on delivering web services for their clients. Ping-Win was developed with the purpose to monitor the services that Knowit are providing.The work was practised with an agile method, with five sprints in total, to be able to adjust the direction of the project  to fit the expectations that Knowit had on Ping-Win.The result was a web service with a complementing background service for continuous monitoring and also a database which was used to store the status messages.Ping-Win  was evaluated with a test were we let it monitor four websites during the Christmas holidays of 2017.

To meet the challenges of consistent performance, low communication latency, and a high degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a Mobile Cloud Network that incorporates public cloud infrastructures with cloud augmented Telecom nodes in forthcoming mobile access networks. A Mobile Cloud Network is composed of distributed cost- and capacityheterogeneous resources that host applications that in turn are subject to a spatially and quantitatively rapidly changing demand. Such an infrastructure requires a holistic management approach that ensures that the resident applications’ performance requirements are met while sustainably supported by the underlying infrastructure. The contribution of this paper is three-fold. Firstly, this paper contributes with a model that captures the cost- and capacity-heterogeneity of a Mobile Cloud Network infrastructure. The model bridges the Mobile Edge Computing and Distributed Cloud paradigms by modelling multiple tiers of resources across the network and serves not just mobile devices but any client beyond and within the network. A set of resource management challenges is presented based on this model. Secondly, an algorithm that holistically and optimally solves these challenges is proposed. The algorithm is formulated as an application placement method that incorporates aspects of network link capacity, desired user latency and user mobility, as well as data centre resource utilisation and server provisioning costs. Thirdly, to address scalability, a tractable locally optimal algorithm is presented. The evaluation demonstrates that the placement algorithm significantly improves latency, resource utilisation skewness while minimising the operational cost of the system. Additionally, the proposed model and evaluation method demonstrate the viability of dynamic resource management of the Mobile Cloud Network and the need for accommodating rapidly mobile demand in a holistic manner.

As cloud computing has become the more popular choice to host clusters in recent times there are multiple providers that offer their services to the public such as Amazon web services, Google cloud platform and Microsoft Azure. The decision of cluster provider is not only a decision of provider, it is also an indirect decision of cluster infrastructure. The indirect choice of infrastructure makes it important to consider any potential differences in cluster performance caused by the infrastructure in combination with the workload type, but also the cost of the infrastructure on the available public cloud providers.

To evaluate whether or not there are any significant differences in either cluster cost or performance between the available public cloud providers, a performance comparison study was conducted. The study consisted of multiple clusters hosted on Amazon Web Services and the Google Cloud Platform. The clusters had access to five different instance types that each correspond to a specific number of available cores, amount of memory and storage. All clusters executed a CPU intensive, I/O intensive, and MapReduce workload while simultaneously having its performance monitored with regard to CPU, memory, and disk usage. The performance comparison study revealed that there are significant performance differences between clusters hosted on Amazon web services and Google cloud platform for the chosen workload types. Since there are significant differences it can be concluded that the choice of provider is crucial as it impacts the cluster performance. Comparing the selected instance types against each other with regard to performance and cost, reveals that a subset of them have better performance as well as lower cost. The instance types that is not a part of this subset, have either better performance or lower cost than its counterpart on the other provider.

Packet losses are known to affect the performance of latency sensitive Internet applications such as media streaming and gaming. Transport protocols recover from packet loss in order to provide reliable end-to-end communication and improve user experience. The efficiency of loss recovery mechanisms influences the completion time of flows, and thus also the application performance as perceived by the end user. In this paper we focus on state-of-the-art loss recovery mechanisms for TCP and Multipath TCP. We use controlled tail loss scenarios to evaluate the performance of loss recovery mechanisms and, based on the observations, we propose an enhanced tail loss recovery mechanism for Multipath TCP, to improve the loss recovery time. Our experiment results, using the Linux Multipath TCP implementation, show consistent end-to-end latency performance improvement in considered scenarios.

As energy consumption by cooling data center IT equipment can be over 40 % of total energy consumption, efficient cooling for large data centers is essential for reducing operation costs. Modern data centers are complex systems involving IT facilities, power system, cooling and ventilation systems. In our previous work, literature study was made to investigate available data center energy consumption models; and energy consumption models for data center IT room with distributed air flow control were developed. In this paper, the models are further extended and developed to cover the combined distributed air flow control and vertical placed servers in raised floor ventilation system. Simulation of the three types of ventilation systems with Even load, Idle server and Uneven load scenarios showed that significant cooling energy consumed by a traditional ventilation system can be saved by applying the proposed new concept and method. (C) 2017 The Authors. Published by Elsevier Ltd.

A dense deployment of small cells is one of the key characteristics envisioned for future 5G mobile networks in order to provide the required capacity increase. To cut cabling costs, small cells are foreseen to form multihop topologies using high capacity backhaul wireless links in the mmWave bands. Such small cells are deployed within the coverage area of a macro cell (eNB) to provide localized capacity where needed according to the well known Heterogeneous Network concept (HetNet). However, green networking will be very important because powering on unnecessarily a massive amount of small cells may lead to increased OPEX and CO2 emission. In this paper, we propose an optimization model that minimizes the total power consumption of 5G HetNets deployments while providing the required capacity and coverage. Our model jointly optimizes the user association, routing in the multihop backhaul and decides to power on or off the small cells to serve the user demands. Our numerical evaluation show significant power savings over a large range of traffic demand distributions while keeping the blocking probability low.

The purpose of this paper was to investigate the integration between Life Cycle Assessment (LCA) and Computer-Aided Design (CAD). The project has been carried out as a degree project at Karlstad University in the spring of 2023 on behalf of the company TECHNIA. The project was a master's thesis of 22.5 higher education credits for the higher education engineering program in Innovation Technology and Design.

The project has been structured to achieve the purpose and goals of the assignment. The work consisted of investigating the capacity and user-friendliness of an integrated LCA and CAD system by using literature as support and investigating and analysing the system and performing case studies. To achieve the purpose and goal, Dassault Systèmes 3DEXPERIENCE has been used as support.

The issue of sustainability today has great weight, and companies increasingly choose sustainable solutions, which is partly due to pressure from external stakeholders. This project highlights the most important trends in sustainability. It is an attractive factor on the market which is relevant for all types of industries. Surveys have also been carried out to map facts about the LCA and CAD integration. Through theoretical investigations, a comprehensive understanding of the integration between LCA and CAD as well as its importance for sustainable development has been highlighted.

The case studies carried out were conducted as interviews at two companies, EDAG and Mölnlycke Health Care, where the interviews were crucial in creating an approach to the use of an integrated system. Use of the integrated system then contributed to a deeper understanding of the functions. By combining the methods used, the project was able to meet its goals.

The methods generated results describing advantages, disadvantages, practices, tools, and approaches when using integrated LCA and CAD. The conclusion is that an integration between LCA and CAD has many advantages compared to a traditional LCA tool. An integrated system is more efficient and easier to use.