Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing need for accurate weather predictions, we need large samples of data from different data sources for an accurate estimate. There are a number of data sources that keep publishing data periodically. These data sources have their own server protocols that a user needs to follow while writing client for retrieving data. This project aims at creating a generic semi-automatic client mechanism for retrieving scientific data from such sources. Also, with the increasing number of data sources there is also a need for a data model to accommodate data that is published in different formats. We have come up with a data model that can be used across various applications in the domain of scientific data retrieval.

The automotive domain is changing. On the way to more convenient, safe, and efficient vehicles, the role of electronic controllers and particularly software has increased significantly for many years, and vehicles have become software-intensive systems. Furthermore, vehicles are connected to the Internet to enable Advanced Driver Assistance Systems and enhanced In-Vehicle Infotainment functionalities. This widens the automotive software and system landscape beyond the physical vehicle boundaries to presently include as well external backend servers in the cloud. Moreover, the connectivity facilitates new kinds of distributed functionalities, making the vehicle a part of an Intelligent Transportation System (ITS) and thus an important example for a future Internet of Things (IoT). Manufacturers, however, are confronted with the challenging task of integrating these ever-increasing range of functionalities with heterogeneous or even contradictory requirements into a homogenous overall system. This requires new software platforms and architectural approaches. In this regard, the connectivity to fixed side backend systems not only introduces additional challenges, but also enables new approaches for addressing them. The vehicle-to-backend approaches currently emerging are dominated by proprietary solutions, which is in clear contradiction to the requirements of ITS scenarios which call for interoperability within the broad scope of vehicles and manufacturers. Therefore, this research aims at the development and propagation of a new concept of a universal distributed Automotive Service Delivery Platform (ASDP), as enabler for future automotive functionalities, not limited to ITS applications. Since Machine-to-Machine communication (M2M) is considered as a primary building block for the IoT, emergent standards such as the oneM2M service platform are selected as the initial architectural hypothesis for the realisation of an ASDP. Accordingly, this project describes a oneM2M-based ASDP as a reference configuration of the oneM2M service platform for automotive environments. In the research, the general applicability of the oneM2M service platform for the proposed ASDP is shown. However, the research also identifies shortcomings of the current oneM2M platform with respect to the capabilities needed for efficient communication and data exchange policies. It is pointed out that, for example, distributed traffic efficiency or vehicle maintenance functionalities are not efficiently treated by the standard. This may also have negative privacy impacts. Following this analysis, this research proposes novel enhancements to the oneM2M service platform, such as application-data-dependent criteria for data exchange and policy aggregation. The feasibility and advancements of the newly proposed approach are evaluated by means of proof-of-concept implementation and experiments with selected automotive scenarios. The results show the benefits of the proposed enhancements for a oneM2M-based ASDP, without neglecting to indicate their advantages for other domains of the oneM2M landscape where they could be applied as well.

Ericsson is one of the world's largest providers of communications technology and services. Reliable networks are important to deliver services that live up to customers' expectations. Tests are frequently run on Ericsson's systems in order to identify stability problems in their networks. These tests are not always completely reliable. The logs produced by these tests are gathered and analyzed to identify abnormal system behavior, especially abnormal behavior that the tests might not have caught. To automate this analysis process, a machine learning system, called the Awesome Automatic Log Analysis Application (AALAA), is used at Ericsson's Continuous Integration Infrastructure (CII)-department to identify problems within the large logs produced by automated Radio Base Station test loops and processes. AALAA is currently operable in two versions using different distributed cluster computing platforms: Apache Spark and Apache Hadoop. However, it needs improvements in its machine-to-machine communication to make this process more convenient to use. In this thesis, message communication has successfully been implemented in the AALAA system. The result is a message bus deployed in RabbitMQ that is able to successfully initiate model training and abnormal log identification through requests, and to handle a continuous flow of result updates from AALAA. / Ericsson är en av världens största leverantörer av kommunikationsteknologi och tjänster. Tillförlitliga nätverk är viktigt att tillhandahålla för att kunna leverera tjänster som lever upp till kundernas förväntningar. Tester körs därför ofta i Ericssons system med syfte att identifiera stabilitetsproblem som kan uppstå i nätverken. Dessa tester är inte alltid helt tillförlitliga, producerade testloggar samlas därför in och analyseras för att kunna identifiera onormalt beteende som testerna inte lyckats hitta. För att automatisera denna analysprocess har ett maskininlärningssystem utvecklats, Awesome Automatic Log Analysis Application (AALAA). Detta system används i Ericssons Continuous Integration Infrastructure (CII)-avdelning för att identifiera problem i stora loggar som producerats av automatiserade Radio Base Station tester. AALAA är för närvarande funktionellt i två olika versioner av distribuerad klusterberäkning, Apache Spark och Apache Hadoop, men behöver förbättringar i sin maskin-till-maskin-kommunikation för att göra dem enklare och effektivare att använda. I denna avhandling har meddelandekommunikation implementerats som kan kommunicera med flera olika moduler i AALAA. Resultatet är en meddelandebuss implementerad i RabbitMQ som kan initiera träning av modeller och identifiering av onormala loggar på begäran, samt hantera ett kontinuerligt flöde av resultatuppdateringar från pågående beräkningar.

Based on the description and definition of technology and processes falling within the vision of the fourth industrial revolution with the aim of creating intelligent factories, this diploma thesis deals with the principles of the Industry 4.0 concept in Hilti's production plant with a focus on transport and supply of production equipment. The aim of the work is to create a comprehensive proposal that takes into account all the necessary aspects associated with upgrading the existing state of inter-operational transport in a particular production line to fully automated, flexible and autonomous transport of materials and products in the context of Industry 4.0. A prerequisite for creating a design is the connection of automatically guided vehicles (AGVs) serving individual transport orders. The selection of the vehicle was made taking into account the safety of movement, the method of charging, the system and network integrity of existing and proposed technologies and components. The intention is not only to automate the inter-operational service, but also on the basis of the created automation concept, the ability to autonomously procure the flow of material and products. The mathematical calculation of capacity planning in the production line helped to determine the total load and the number of vehicles needed for continuous procurement of transport requirements. The result of the design part is also the design of specific transport routes and transport conditions that AGV vehicles must comply with in order to maintain a high level of safety. Transparency and a constant overview of transported products is provided by the presented scheme for identification of production batches, Auto-ID system. The financial efficiency of the whole project elaborated in the diploma thesis is evaluated as payable after 4 years from the implementation of the proposal. The financial efficiency of the whole project elaborated in the diploma thesis is evaluated as payable after 4 years from the implementation of the proposal due to high labor costs.