The theoretical part of the thesis outlines the entire topic from the broad perspective created namely by foreign publications. Then the thesis presents the theories of two American sociologists whose conclusions represent the anticipation of the subject analysed here. This is followed by the definition of coworking and its brief history, the perspectives on it that management has adopted by comparing coworking to clusters as defined by Michael Porter. The purpose of the practical part of the thesis is to describe the functioning of coworking in real life situations, this from the perspective of the managers (or owners) of coworking centres, and their users. The data was obtained by quality and quantitative research, namely by multicase analysis and questionnaires data analysis. Individual chapters of the thesis describe conclusions reached from this research. The conclusion of the thesis summarizes the most important facts and results obtained by the research, it also confirms the hypothesis which was the basis of the thesis.

No description available.

<div>For a long time, the collection of data through sensors and other means was seen as inconsequential. However, with the somewhat recent developments in the areas of machine learning, data science, and statistical analysis, as well as in the rapid growth of computational power being allotted by the ever-expanding computer industry, data is not just being seen as secondhand information anymore. Data collection is showing that it currently is and will continue to be a major driving force in many applications, as the predictive power it can provide is invaluable. One such area that could benefit dramatically from the use of predictive techniques is the steel industry. This thesis applied several machine learning techniques to predict steel deformation issues collectively known as the hook index problem [1].</div><div><br></div><div>The first machine learning technique utilized in this endeavor was neural networking. The neural networks built and tested in this research saw the use of classification and regression prediction models. They also implemented the algorithms of gradient descent and adaptive moment estimation. Through the employment of these networks and learning strategies, as well as through the line process data, regression-based networks made predictions with average percent error ranging from 106-114%. In similar performance to the regression-based networks, classification-based networks made predictions with average accuracy percentage ranges of 38-40%.</div><div><br></div><div>To remedy the problems relating to neural networks, Bayesian networking techniques were implemented. The main method that was used as a model for these networks was the Naïve Bayesian framework. Also, variable optimization techniques were utilized to create well-performing network structures. In the same vein as the neural networks, Bayesian networks used line process data to make predictions. The classification-based networks made predictions with average accuracy ranges of 64-65%. Because of the increased accuracy results and their ability to draw causal reasoning from data, Bayesian networking was the preferred machine learning technique for this research application.</div>

Cloud computing is a fast-growing sector among software companies. Cloud platforms provide services such as spreading out storage and computational power over several geographic locations, on-demand resource allocation and flexible payment options. Virtualization is a technology used in conjunction with cloud technology and offers the possibility to share the physical resources of a host machine by hosting several virtual machines on the same physical machine. Each virtual machine runs its operating system which makes the virtual machines hardware independent. The cloud and virtualization layers add additional layers of software to the server environments to provide the services. The additional layers cause an overlay in latency which can be problematic for latency sensitive applications. The primary goal of this thesis is to investigate how the networking components impact the latency in an OpenStack cloud compared to a traditional deployment. The networking components were benchmarked under different load scenarios, and the results indicate that the additional latency added by the networking components is not too significant in the used network setup. Instead, a significant performance degradation could be seen on the applications running in the virtual machine which caused most of the added latency in the cloud environment. / Molntjänster är en snabbt växande sektor bland mjukvaruföretag. Molnplattformar tillhandahåller tjänster så som utspridning av lagring och beräkningskraft över olika geografiska områden, resursallokering på begäran och flexibla betalningsmetoder. Virtualisering är en teknik som används tillsammans med molnteknologi och erbjuder möjligheten att dela de fysiska resurserna hos en värddator mellan olika virtuella maskiner som kör på samma fysiska dator. Varje virtuell maskin kör sitt egna operativsystem vilket gör att de virtuella maskinerna blir hårdvaruoberoende. Moln och virtualiseringslagret lägger till ytterligare mjukvarulager till servermiljöer för att göra teknikerna möjliga. De extra mjukvarulagrerna orsakar ett pålägg på responstiden vilket kan vara ett problem för applikationer som kräver snabb responstid. Det primära målet i detta examensarbete är att undersöka hur de extra nätverkskomponenterna i molnplattformen OpenStack påverkar responstiden. Nätverkskomonenterna var utvärderade under olika belastningsscenarion och resultaten indikerar att den extra responstiden som orsakades av de extra nätverkskomponenterna inte har allt för stor betydelse på responstiden i den använda nätverksinstallationen. En signifikant perstandaförsämring sågs på applikationerna som körde på den virtuella maskinen vilket stod för den större delen av den ökade responstiden.

No description available.

The underlying architecture of the Internet has been mostly the same since its beginning in the 1960s and the TCP/IP protocol stack remains ubiquitous. However the Internet is today used for much wider purposes than what was originally intended and now the most common use of the Internet is for the distribution of various forms of content. Information Centric Networking (ICN) is an alternative architecture responding to this change in usage, intended to be more prepared to handle the new requirements of the Internet not only today but also in the future. The primary concern in ICN is the secure and efficient distribution of content. Current ICN research often concerns applications on various disaster scenarios as it is believed that ICN has properties that match the requirements of such scenarios. In this thesis that research is continued by developing an especially designed information retention solution, using the existing ICN implementation of Content Centric Networking (CCN). The aim is to maximisise and prolong the availability of as much content as possible in disaster-stricken networks by preemptively replicating content across the network topology. The solution is then evaluated against a scenario set in a network topology consisting of virtual machines. The final result is that the solution performs satisfactorily and thus demonstrate the potential of ICN when applied to such scenarios. / Internets underliggande arkitektur har varit i stort sett oförändrad sedan sin begynnelse på 1960-talet, och TCP/IP protokollstacken är fortsatt universell. Dock så används Internet idag för betydligt bredare ändamål än de ursprungliga syftena, och nu används Internet främst för att distribuera olika former av innehåll. Information Centric Networking (ICN) är en alternativ arkitektur som svarar på denna förändring i använding, avsedd att vara mer förberedd att hantera de nya kraven på Internet inte bara idag men också i framtiden. Den största angelägenheten i ICN är att distribuera innehåll på ett säkert och effektivt vis. Nuvarande forskning inom ICN handlar ofta om tillämpningar på olika sorters katastrofscenarier då tron är att ICN har egenskaper som motsvarar kraven hos sådana scenarier. I den här uppsatsen fortsätts denna forskning genom att en speciellt formgiven informationsbevaringslösning utvecklas, som nyttjar den existerande ICN-implementationen Content Centric Networking (CCN). Målet är att maximera och förlänga tillgängligheten av så mycket innehåll som möjligt i katastrofdrabbade nätverk genom att i förebyggande syfte replikera innehåll genom nätverkstopologin. Lösningen evalueras sedan mot ett scenario som utspelas i en nätverkstopologi utav virtuella maskiner. Det slutgiltiga resultatet är att lösningen presterar tillfredsställande och på så vis demonstrerar potentialen hos ICN vid tillämpning på sådana scenarion.

Time-critical networks of various types are widely used in fields such as industrial automation. Many of these time-critical networking solutions are proprietary and closed, which can make them costly to work with. An alternative to these legacy solutions is Time-Sensitive Networking. Time Sensitive Networking, or TSN, is an open standard for time-critical communication over Ethernet hardware and protocols. Compared to proprietary and closed legacy solutions, a TSN can be easier to set up. There is still however a challenge in configuring a TSN since the configuration process is hardware dependent. This thesis sets out to ease the configuration process, making it more user-friendly by providing a tool for the generation of end-host configurations. Currently, no such readily available tool exists for configuration of Linux end-hosts in TSNs. This is done by implementing extensions to the incomplete TSN configuration middleware DETD to a state where it is a suitable solution to this problem. The extensions made to DETD consist of implementing support for configuring listener streams, adding the ability to configure the TAPRIO queueing discipline, and adding support for an additional network interface card in the form of the Intel I210. To verify the functionality of these extensions a simple testbed using two real-time Linux machines is used.

<p>This thesis presents the lessons learned from building an IEEE 802.11 wireless mesh network (WMN) test-bed. Each network node consists of a Linux processor with multiple IEEE 802.11b/g transceivers operating in the 2.4 GHz band. Each transceiver consists of a medium access control (MAC) and base-band processor (BBP) in addition to a radio. A device driver was modified to control some of the key transceiver functions. The test-bed's Wi-Fi interfaces can be programmed to implement any mesh communication topology. All Wi-Fi interfaces use omni-directional antennas and the IEEE 802.11b operation mode.</p> <p>The test-bed design is easily extendable to incorporate newer Wi-Fi technologies. Measurements of co-channel interference in each Wi-Fi channel including received signal strength (RSS) and signal-to-interference-and-noise ratio (SINR) are presented. The AutoMin algorithm was developed in order to use the captured physical layer (PHY) metrics to avoid Wi-Fi congestion during test-bed operation. A comparison of a software-based spectrum analyzer to a commercial one is described. Key Wi-Fi functions in the Ralink driver source code are explored in depth. The compliance of the Ralink chip-set to the IEEE 802.11b spectral mask was verified. The maximum driver-induced retuning rate for the popular Ralink radio was found experimentally. This data can be used to optimize the performance of IEEE 802.11 WMNs.</p> / Master of Applied Science (MASc)

<p>We examine energy harvesting sensor networks, more specifically, a sensor network using the Geographic Routing with Environmental Energy Supply (GREES) algorithm. We start with a discussion of other sources of energy conservation both in energy harvesting and non-energy harvesting sensor networks. Ideas presented in these works are combined where possible with the GREES algorithm. A sensor network was actually built to test and (if possible) improve the algorithm. There were problems along the way, but they were overcome to produce a functioning energy harvesting sensor network that used solar cells as the energy harvesting unit. Tests were run on the network by giving a consistent light and battery supply, and then changing parameters of the algorithm to see their effect on the lifetime of the network, indicating the network's sensitivity to individual parameters. These results are presented, along with their interpretation, as well as an error analysis detailing the behaviour of the algorithm. We discuss how sensitive the network is to each parameter, indicating which parameters are more important to calibrate or measure correctly.</p> / Master of Applied Science (MASc)

<p>Selfish routing is frequently discussed. The general framework of a system of non-cooperative users can be used to model many different optimization problems such as network routing, traffic or transportation problems.</p> <p>It is well known that the Wardrop user equilibria (i.e. the user optima) generally do not optimize the overall system cost in a traffic routing problem.</p> <p>In order to induce the equilibrium flow to be as close to the optimal flow as possible, the term “toll” is introduced. With the addition of tolls, a traffic system does not show the actual cost to the users but the displayed cost of users, which is the summation of the actual cost and the toll. A common behavioral assumption in traffic network modeling is that every user chooses a path which is perceived as the shortest path, then the whole system achieves the equilibrium of the displayed cost. It is proved that there exists an optimal toll which can induce the equilibrium flow under displayed cost to be the optimal flow in reality.</p> <p>However, this conclusion holds only if the selfish routing executes only once. If the game is played repeatedly, the users will detect the difference between the actual and displayed costs. Then, they will not completely trust the information given by the system and calculate the cost. The purpose of this thesis is to find out the optimal strategy given by the system–how to set tolls in order to maintain the flow as close to the optimal flow as possible.</p> / Master of Science (MSc)