Use Case Analysis Sample Clauses

Use Case Analysis. In order to provide an indication of the trust issues expected in 5G networks we present here an analysis of 21 of the 31 use cases defined by 5G-ENSURE in D2.1. This is justifiable given the “draft” nature of this document and the remainder of the use cases will be analysed in due course. We have taken care to align the use cases analysed with those analysed and reported on in the risk analysis of D2.3 so that a complete model of the risks and consequences, and specifically the consequences for trust and trustworthiness of the use case subset can be drawn.
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Use Case Analysis. In order to demonstrate the usage of our proposed framework, we describe some exemplary use-cases from section 3 in detail. • Query global pose: Assuming the vehicle has no prior knowledge about its pose in the world, we aim at querying the metric-map for a (rough) estimate of the 6DoF pose between the vehicle’s frame of reference and some well-defined global frame of reference. For this, the Online Localization Module fetches the latest image(s) from the DDS message bus and issues a query to the cloud-based backend. Only a Map ID and the host-name/IP of the Service Load Balancer are needed as prior knowledge which are both assumed to be known in any case. An HTTP GET web query is issued with the following URL http(s)://host/maps/<map_id>/actions/localize and the image(s) contained in the data parameter. The corresponding service endpoint in the backend processes the query by delegating it to the Metric Localization & Mapping application. The result, namely as 6DoF pose estimate wrt. a well-defined global coordinate frame, is returned to the vehicle as a response to the web query in a JSON message. • Query near-by metric-map data for on-car localization: Assuming there is rough estimate of the vehicle’s global pose, close-by landmarks with descriptors, or any other metric mapping data, ought to be fetched in order to run local localization on the vehicle in the near future. In addition to that, we again assume the Map ID as well as the host-name/IP of the Service Load Balancer to be known a priori. The following HTTP GET web-query is issued: http(s)://host/maps/<map_id>/landmarks_with_descriptors with the optional parameters pose and potentially radius and yaw_angle_deviation specified. As a result, a JSON message is returned containing the 3D position of the near-by landmarks wrt. the global frame of reference together with one or more feature descriptor for each landmark. • Upload a dataset for archiving: In this use case, we intend to upload a dataset file in the .dat format for later retrieval from the cloud object store. The retrieval task assumes that we will be able to resolve the object identifier of the cloud object based on characteristic information. This information may involve, but does not have to be limited to, a description of the acquisition time and location of the dataset. First, an HTTP POST call with the dataset file will be issued to: http(s)://host/objects This will store the dataset file in the object store and return the object ide...
Sample 1

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