With society undergoing a large-scale technological revolution, companies often find themselves having to adapt by developing digitized products and applications. However, these applications typically produce large amounts of data. An additional problem that companies in the 3D Home Design industry face is having to provide a massive amount of options for their products, which without proper usage metrics can lead to large amounts of wasted resources. This thesis aims to combat these problems by designing a prototype of a modular analytics platform, consisting of a data parser, a back-end server & API as well as a web interface. The system is capable of displaying highly customizable visual graphs of user statistics as well as breakdowns for how much each product gets picked by the client's users. The thesis system is built on a MERN stack, consisting of MongoDB, Express.js, React.js and Node.js, and is written purely in JavaScript. The thesis achieved moderate success by successfully implementing a modular analytics platform as well as correctly providing tools that can identify obsolete products, which in return could potentially reduce the amount of resources wasted by companies that adopt the solution.

Stora mängder data genereras dagligen av slutanvändare hos olika tjänster. Denna data tenderar att tillhandahållas av olika aktörer, vilket skapar en fragmenterad marknad där slutanvändare måste nyttja flera programvaror för att ta del av all sin data. Detta kan motverkas genom utvecklandet av aggregeringstjänster vilka samlar data från flera tjänster på en enskild ändpunkt. Utveckling av denna typ av tjänster riskerar dock att bli kostsamt och tidskrävande, då ny kod skrivs för flera projekt trots att stora delar av funktionaliteten är snarlik. För att undvika detta kan etablerade tekniker och ramverk användas för att på så vis återanvända mer generella komponenter. Vilka av dessa tekniker som är bäst lämpade och således kan anses vara mest effektiva ur ett utvecklingsperspektiv, kan dock vara svårt att avgöra. Därför baseras denna uppsats på vad som genom analys av akademisk litteratur kan utläsas som ett akademiskt konsensus.Innan denna uppsats påbörjades utvecklades en Java-baserad dataaggeringstjänst baserad på krav från ÅF i Malmö. Denna experimentella implementation har som syfte att samla in data från två separata tjänster, och tillgängliggöra denna på en enskild ändpunkt. Efter att implementationen färdigställts påbörjades arbetet på uppsatsen. Denna består av en litteraturstudie för att undersöka vilka tekniker och ramverk som akademisk forskning funnit bäst lämpad för användningsområdet. Vidare används resultaten från studien även för att analysera i vilken grad dessa korrelerar med de krav som ÅF presenterade inför den experimentella implementationen.Litteraturstudien visar på att de teknikmässiga val som gjordes av företaget i stor utsträckning korrelerar med de tekniker som akademisk forskning funnit bäst lämpade för användningsområdet. Detta innefattar bland annat OAuth 2.0 för autentisering, JSON som serialiseringsformat samt REST som kommunikationsarkitektur. Vidare visar denna litteraturstudie på en eventuell lucka inom den tillgängliga litteraturen, då sökningar kring specifika programvaror relaterade till området endast resulterar i en mindre mängd artiklar. / Large quantities of data are generated daily by the end users of various services. This data is often provided by different providers, which creates a fragmented market where the end users have to utilize multiple applications in order to access all of their data. This can be counteracted by the development of aggregation services that gather data from multiple services to a combined endpoint. The development of these kinds of services does however run the risk of becoming costly and time-consuming since new code is written for several projects even though large portions of the functionality is similar. To avoid this, established technologies and frameworks can be utilized, thereby reusing the more general components. Which of the technologies are the best suited, and thereby can be considered the most effective from a development perspective, can however be difficult to determine. This essay is therefore based on what can be considered an academic consensus through analysis of literature regarding earlier reasearch on the subject. Before the writing of the essay began a Java-based data aggregation service was developed, based on requirements from the company ÅF in Malmö. The purpose of this experimental implementation is to gather data from two separate services, and make them accessible on a unified endpoint.After the implementation was finished, work on the essay began. This consists of a literature review to investigate what technologies and frameworks that has been found best suited for this area of application by academic research. The results from this study are also used to analyze the extent of the correlation between the results and the requirements presented by ÅF regarding the experimental implementation. The literature review shows that the choices made by the company largely correlates with the technologies that the academic research has found best suited for this area of application. This includes OAuth 2.0 for authentication, JSON as a serialization format and REST for communications architecture. The literature review also indicates a possible gap within the available academic literature since searches regarding specific pieces of software related to the subject only results in a small amount of articles.

No / The last decade has seen an exponential rise in the use of wireless sensor networks (WSNs) in various applications. While these have been primarily used on their own, researchers are now looking into ways of integrating these WSNs with other existing communication technologies. One such network is the satellite network which provides significant advantage in providing communication access to remote locations due to their inherent large coverage areas. Combining WSNs and satellite will enable us to perform efficient remotely monitoring in areas where terrestrial networks may not be present. However in such a scenario, the placement of sensor nodes is crucial in order to ensure efficient routing and energy-efficiency. This paper presents four network architectures for sensor-satellite hybrid networks, sensor-satellite direct communication, connections via a gateway node employing random node layout, grid-based node layout and cluster-based node layout with data aggregation. These architectures were simulated using network simulator 2 (ns-2) and then their packet loss rate, average end-to-end packet delay, and overall energy consumption were compared. The paper concludes by proposing a suitable network topology for environmental monitoring applications.

Understanding the differences among public transit types allows for the development of more targeted policies at both local and regional levels. Examining how the built environment (BE) influences travel behavior (Delclòs-Alió et al.) and assessing data aggregation effects around different transit station types at the local level, along with identifying key predictors of ridership across transit modes at the regional level, offers valuable insights for policy efforts. Specifically, the dissertation comprises three studies that analyze BE-travel behavior associations and data aggregation effects locally, as well as variations in key predictors of rail and bus ridership at a regional scale within the United States. The findings emphasize the unique land-use and travel behavior associations for various public transit systems within transit catchment areas, the effects of data aggregation on BE-travel behavior models, and the critical predictors of rail and bus ridership at regional levels. The first study highlights nonlinear associations between BE attributes and commuting mode share within rail and Bus Rapid Transit (BRT) catchment areas, using data from approximately 2,790 transit stations across 34 U.S. metropolitan statistical areas. Applying a random forest model, this study reveals substantial differences between rail and BRT areas, with rail catchment areas showing greater sensitivity to BE factors in reducing car dependency. BRT, however, emerges as a viable alternative for sprawling areas lacking the compact development needed to support rail systems. The second study investigates how data aggregation influences the BE-mode share relationship around 2,794 rail and BRT stations, utilizing both inferential and machine learning approaches. Findings indicate that data aggregation affects BE-mode share models regardless of the analytical method. Optimal buffer sizes for capturing BE effects and minimizing sensitivity to data aggregation were identified as 800 meters for BRT stations and 1,000 meters for rail stations. Key BE features such as employment density, jobs per household, intersection density, residential density, distance from the central business district, job accessibility (active modes) demonstrated robustness against data aggregation for both rail and BRT stations. The third study examines changes in transit ridership predictors before and after the COVID-19 pandemic across 35 U.S. metropolitan areas. Using extreme gradient boosting on data spanning January 2019 to June 2023, the study identifies a shift from internal to external factors as key drivers of ridership post-pandemic. Socioeconomic factors, gasoline prices, telecommuting, population density, employment density and polycentric development emerged as influential for bus ridership post-pandemic, while traditional factors like vehicle revenue miles, fare, transit coverage, and service areas are more important for rail ridership. Additionally, the study uncovers unique threshold and interaction effects in the post-pandemic period, including positive interactions between African American population proportions and poverty rates for bus ridership, carless households and gasoline prices for bus ridership, and between VRM and polycentricity for rail ridership. This dissertation provides insights into the complex dynamics between BE, transit types, and travel behavior, offering valuable implications for urban transportation planning and policy development at multiple levels. / Doctor of Philosophy / This research explores how different aspects of urban design impact public transit use and helps identify what drives ridership on different types of transit. By studying connections between urban layouts and travel habits around transit stations, the findings offer guidance for creating tailored local and regional transit policies. Specifically, the research looks at three key areas: how built environments relate to travel choices locally, how data processing methods influence results, and what factors influence bus and rail ridership across U.S. cities. The first part reveals that the design of areas around rail and Bus Rapid Transit (BRT) stations affects travel patterns in unique ways. Rail stations tend to decrease car use in well-developed areas, while BRT stations work better in sprawling urban settings, where compact rail development isn't feasible. The second part shows that the way data is organized can change how we understand the link between urban form and transit use. For example, analyzing a broader area (up to 1,000 meters) around rail stations captures the effect of local design better, while an 800-meter radius is optimal for BRT. Certain features, like job density and proximity to the city center, consistently predict transit use, regardless of the data scale. Finally, the third part examines changes in what drives transit ridership since the COVID-19 pandemic. While pre-pandemic ridership was mostly influenced by operational factors like service coverage, post-pandemic ridership is more affected by external factors like gas prices and remote work trends. Unique patterns also emerge, such as links between certain demographics and bus ridership and between economic factors and rail use. Overall, this study helps planners and policymakers understand the unique needs of rail and bus systems, supporting strategies to make public transit more effective and responsive to community needs.

Les réseaux de capteurs sans fils sont composés de noeuds capteurs capables de mesurer certains paramètres de l’environnement, traiter l’information recueillie, et communiquer par radio sans aucune autre infrastructure. La communication avec les autres noeuds consomme le plus d’énergie. Les protocoles de collecte des données des réseaux de capteurs sans fils doit donc avoir comme premier objectif de minimiser les communications. Une technique souvent utilisée pour ce faire est l’agrégation des données. Les réseaux de capteurs sans fils sont souvent déployés dans des environnements ouverts, et sont donc vulnérables aux attaques de sécurité. Cette thèse est une contribution à la conception de protocoles sécurisés pour réseaux de capteurs sans fils. Nous faisons une classification des principaux protocoles d’agrégation de données ayant des propriétés de sécurité. Nous proposons un nouveau protocole d’agrégation (ESPPA). ESPPA est basé sur la construction d’un arbre recouvrant sûr et utilise une technique de brouillage pour assurer la confidentialité et le respect de la vie privée. Notre algorithme de construction (et re-construction) de l’arbre recouvrant sûr tient compte des éventuelles pannes des noeuds capteurs. Les résultats de nos simulations montrent que ESPPA assure la sécurité en terme de confidentialité et de respect de la vie privée, et génère moins de communications que SMART. Finalement, nous proposons une extension du schéma de construction de l’arbre recouvrant sûr qui identifie les noeuds redondants en terme de couverture de captage et les met en veille. Les résultats de nos simulations montrent l’efficacité de l’extension proposée. / WSNs are formed by sensor nodes that have the ability to sense the environment, process the sensed information, and communicate via radio without any additional prior backbone infrastructure. In WSNs, communication with other nodes is the most energy consuming task. Hence, the primary objective in designing protocols for WSNs is to minimize communication overhead. This is often achieved using in-network data aggregation. As WSNs are often deployed in open environments, they are vulnerable to security attacks. This thesis contributes toward the design of energy efficient secure and privacy preserving data aggregation protocol for WSNs. First, we classify the main existing secure and privacy-preserving data aggregation protocols for WSNs in the literature. We then propose an energy-efficient secure and privacy-preserving data aggregation (ESPPA) scheme for WSNs. ESPPA scheme is tree-based and achieves confidentiality and privacy based on shuffling technique. We propose a secure tree construction (ST) and tree-reconstruction scheme. Simulation results show that ESPPA scheme effectively preserve privacy, confidentiality, and has less communication overhead than SMART. Finally we propose an extension of ST scheme, called secure coverage tree (SCT) construction scheme. SCT applies sleep scheduling. Through simulations, we show the efficacy and efficiency of the SCT scheme. Beside the work on secure and privacy preserving data aggregation, during my research period, we have also worked on another interesting topic (i.e., composite event detection for WSNs). Appendix B presents a complementary work on composite event detection for WSNs.

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / Severe energy constraints and limited computing abilities of the nodes in a network present a major challenge in the design and deployment of a wireless sensor network. This thesis aims to present energy efficient algorithms for data fusion and information aggregation in a sensor network. The various methodologies of data fusion presented in this thesis intend to reduce the data traffic within a network by mapping the sensor network application task graph onto a sensor network topology. Partitioning of an application into sub-tasks that can be mapped onto the nodes of a sensor network offers opportunities to reduce the overall energy consumption of a sensor network. The first approach proposes a grid based coordinated incremental data fusion and routing with heterogeneous nodes of varied computational abilities. In this approach high performance nodes arranged in a mesh like structure spanning the network topology, are present amongst the resource constrained nodes. The sensor network protocol performance, measured in terms of hop-count is analysed for various grid sizes of the high performance nodes. To reduce network traffic and increase the energy efficiency in a randomly deployed sensor network, distributed clustering strategies which consider network density and structure similarity are applied on the network topology. The clustering methods aim to improve the energy efficiency of the sensor network by dividing the network into logical clusters and mapping the fusion points onto the clusters. Routing of network information is performed by inter-cluster and intra-cluster routing.