Automated deployment and scaling of automotive safety services in 5G-Transformer

There is a growing interest of verticals (in this case, the automotive industry) to reap the benefits of 5G networks. At the same time, there is a clear trend of the telco industry to under-stand their needs. These are also some of the main goals of the EU 5G-TRANSFORMER (5GT) project. This demo focuses on the need of verticals to dynamically deploy services at the edge and to adapt the vertical service to network operational conditions. In particular, it is presented the extended virtual sensing (EVS) service, which deployed on demand at the distributed computing infrastructure (i.e. in the network), complements sensing and processing functions running in the car to detect the risk of collisions and take appropriate action, even if there is no direct communication between cars. The stringent latency constraints imposed by the EVS network service leave a limited processing budget at the vertical service level. Since such processing time is correlated with the CPU consumption of a virtual machine running a VNF of the EVS network service, in this demo we also show how the vertical service exploits the automated scaling capabilities offered by the 5GT service orchestrator to deploy a new instance of the EVS VNF upon reception of a CPU consumption alert generated by the available 5GT monitoring platform.Grant numbers : grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

The usability of semantic search tools: a review

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web

Cross-Media Knowledge Acquisition: A Case Study

The paper describes an approach to cross-media knowledge acquisition which combines text and raw data. The approach has been applied in a real-world use case concerning wind tunnel reports within the EU-funded project X-Media. The goal is to identify the source of wind noise in a vehicle and find the most suitable solution to reduce it. Information is extracted from the textual parts of the reports and provided to the raw data tool to improve its performance. The results of the initial experiments are encouraging

Edge-assisted Federated Learning in Vehicular Networks

Given the plethora of sensors with which vehicles are equipped, today’s automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning mod- els, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction – a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform