In a heavy vehicle, internet connection is not reliable, primarily because the truck often travels to a remote location where network might not be available. The data generated from the sensors in a vehicle might not be sent to the internet when the connection is poor and hence it would be appropriate to store and do some basic computation on those data in the heavy vehicle itself and send it to the cloud when there is a good network connection. The process of doing computation near the place where data is generated is called Edge computing. Scania has its own Edge computation solution, which it uses for doing computations like preprocessing of sensor data, storing data etc. Scania’s solution is compared with a commercial edge computing platform called as AWS (Amazon Web Service’s) Greengrass. The comparison was in terms of Data efficiency, CPU load, and memory footprint. In the conclusion it is shown that Greengrass solution works better than the current Scania solution in terms of CPU load and memory footprint, while in data efficiency even though Scania solution is more efficient compared to Greengrass solution, it was shown that as the truck advances in terms of increasing data size the Greengrass solution might prove competitive to the Scania solution.One more topic that is explored in this thesis is Digital twin. Digital twin is the virtual form of any physical entity, it can be formed by obtaining real-time sensor values that are attached to the physical device. With the help of sensor values, a system with an approximate state of the device can be framed and which can then act as the digital twin. Digital twin can be considered as an important use case of edge computing. The digital twin is realized with the help of AWS Device shadow. / I ett tungt fordonsscenario är internetanslutningen inte tillförlitlig, främst eftersom lastbilen ofta reser på avlägsna platser nätverket kanske inte är tillgängligt. Data som genereras av sensorer kan inte skickas till internet när anslutningen är dålig och det är därför bra att ackumulera och göra en viss grundläggande beräkning av data i det tunga fordonet och skicka det till molnet när det finns en bra nätverksanslutning. Processen att göra beräkning nära den plats där data genereras kallas Edge computing. Scania har sin egen Edge Computing-lösning, som den använder för att göra beräkningar som förbehandling av sensordata, lagring av data etc. Jämförelsen skulle vara vad gäller data efficiency, CPU load och memory consumption. I slutsatsen visar det sig att Greengrass-lösningen fungerar bättre än den nuvarande Scania-lösningen när det gäller CPU-belastning och minnesfotavtryck, medan det i data-effektivitet trots att Scania-lösningen är effektivare jämfört med Greengrass-lösningen visades att när lastbilen går vidare i Villkor för att öka datastorleken kan Greengrass-lösningen vara konkurrenskraftig för Scania-lösningen. För att realisera Edge computing används en mjukvara som heter Amazon Web Service (AWS) Greengrass.Ett annat ämne som utforskas i denna avhandling är digital twin. Digital twin är den virtuella formen av någon fysisk enhet, den kan bildas genom att erhålla realtidssensorvärden som är anslutna till den fysiska enheten. Med hjälp av sensorns värden kan ett system med ungefärligt tillstånd av enheten inramas och som sedan kan fungera som digital twin. Digital twin kan betraktas som ett viktigt användningsfall vid kantkalkylering. Den digital twin realiseras med hjälp av AWS Device Shadow.

Fog Computing is an emerging computing paradigm that shifts certain processing closer to the Edge of a network, generally within one network hop, where latency is minimized, and results can be obtained the quickest. However, not a lot of research has been done on the effectiveness of Fog in real-world applications. The aim of this research is to show the effectiveness of the Fog Computing paradigm as the middle layer in a 3-tier architecture between the Internet of Things (IoT) and the Cloud. Two applications were developed: one utilizing Fog in a 3-tier architecture and another application using IoT and Cloud with no Fog. A quantitative and qualitative analysis followed the application development, with studies focused on application response time and walkthroughs for AWS Greengrass and Amazon Machine Learning. Furthermore, the application itself demonstrates an architecture which is of both business and research value, providing a real-life coffee shop use-case and utilizing a newly available Fog offering from Amazon known as Greengrass. At the Cloud level, the newly available Amazon Machine Learning API was used to perform predictive analytics on the data provided by the IoT devices. Results suggest that Fog-enabled applications have a much lower range of response times as well as lower response times overall. These results suggest Fog-enabled solutions are suitable for applications which require network stability and reliably lower latency.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Examining representations of gender from a postcolonial feminist perspective, Patriarchs, Pugilists, and Peacemakers: Masculinity in Irish Film analyzes select works of three popular filmmakers whose careers, taken together, span the period from 1939 to the present.1 I argue that these three artists--John Ford, Jim Sheridan, and Paul Greengrass--explore fundamental questions about patriarchy and violence within Irish and Irish-American contexts, and that, in the process, they upset conventional notions of masculine authority. Investigating alternative conceptions of manhood presented in these films, as well as these filmmakers’ complex engagement with Hollywood film genres, I offer a fuller understanding of their subtle critiques of patriarchy. I contend that their illustrations of socially sanctioned male dominance in the lives of women, as well as their portrayals of male and female resistance to patriarchy, constitute a subversive challenge to traditional order. In the process, I address gendered archetypes that are prevalent in Irish and American cinemas and analyze the ways in which Ford, Sheridan, and Greengrass employ and critique these masculine types through their portrayals of fathers, sons, boxers and pacifists. Ultimately, I argue that the recent Irish films of Sheridan and Greengrass gesture toward future modes of manhood that completely disavow patriarchy and violence. In sum, this project plots a trajectory of Irish cinema during the twentieth and early twenty-first centuries, charting a progression from ambivalent critique of patriarchy (in the films of John Ford) to outright rejection of patriarchal masculinity (in Jim Sheridan’s work) to reconceptualization of manhood and the family (in the Irish films of Sheridan and Paul Greengrass). / text