Utilizing Edge Computing for Monitoring Plant Productivity in Print Industry

Automated monitoring of a whole production plant, equipped with a variety of different machines is a challenging task. Particular industries are introducing their own XML based schemas to ease the integration process. Print industry attempts to accomplish this with Job Description Format (JDF). However, a number of older print industry machines is rarely ready for such an integration. For integrating a real production plant, here is proposed a novel approach in utilizing a concept from Internet of Things (IoT) called edge computing, to enhance and integrate various printing and finishing equipment status in a unified manner. Edge computing assumes that a lot of processing is on a remote node and that the data is eventually aggregated to another location. For edge nodes small board computers (SBC) with wireless connectivity were used to collect data from machine sensors and store it locally. The data collected on the edge indicates status and operational speed over time of a machine and could be used for various analysis later. Edge node stores all data to a local database that could be accessed remotely or the node could be converted to a JDF compliant producer. The data from edges is then collected to establish a plant wide monitoring system that is a part of management information system. The concept presented here was successfully implemented in a real production environment. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities

To face the tough competition, changing markets and technologies in automotive industry, automakers have to be highly innovative. In the previous decades, innovations were electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal network. Furthermore, the growing expectations and preferences of customers oblige these manufacturers to adapt their business models and to also propose mobility-based services. One other hand, there is also an increasing pressure from regulators to significantly reduce the environmental footprint in transportation and mobility, down to zero in the foreseeable future. This dissertation investigates an architecture for communication and data exchange within a complex and heterogeneous ecosystem. This communication takes place between various third-party entities on one side, and between these entities and the infrastructure on the other. The proposed solution reduces considerably the complexity of vehicle communication and within the parties involved in the ODX life cycle. In such an heterogeneous environment, a particular attention is paid to the protection of confidential and private data. Confidential data here refers to the OEM’s know-how which is enclosed in vehicle projects. The data delivered by a car during a vehicle communication session might contain private data from customers. Our solution ensures that every entity of this ecosystem has access only to data it has the right to. We designed our solution to be non-technological-coupling so that it can be implemented in any platform to benefit from the best environment suited for each task. We also proposed a data model for vehicle projects, which improves query time during a vehicle diagnostic session. The scalability and the backwards compatibility were also taken into account during the design phase of our solution. We proposed the necessary algorithms and the workflow to perform an efficient vehicle diagnostic with considerably lower latency and substantially better complexity time and space than current solutions. To prove the practicality of our design, we presented a prototypical implementation of our design. Then, we analyzed the results of a series of tests we performed on several vehicle models and projects. We also evaluated the prototype against quality attributes in software engineering