Deliverable JRA1.1: Evaluation of current network control and management planes for multi-domain network infrastructure

This deliverable includes a compilation and evaluation of available control and management architectures and protocols applicable to a multilayer infrastructure in a multi-domain Virtual Network environment.The scope of this deliverable is mainly focused on the virtualisation of the resources within a network and at processing nodes. The virtualization of the FEDERICA infrastructure allows the provisioning of its available resources to users by means of FEDERICA slices. A slice is seen by the user as a real physical network under his/her domain, however it maps to a logical partition (a virtual instance) of the physical FEDERICA resources. A slice is built to exhibit to the highest degree all the principles applicable to a physical network (isolation, reproducibility, manageability, ...). Currently, there are no standard definitions available for network virtualization or its associated architectures. Therefore, this deliverable proposes the Virtual Network layer architecture and evaluates a set of Management- and Control Planes that can be used for the partitioning and virtualization of the FEDERICA network resources. This evaluation has been performed taking into account an initial set of FEDERICA requirements; a possible extension of the selected tools will be evaluated in future deliverables. The studies described in this deliverable define the virtual architecture of the FEDERICA infrastructure. During this activity, the need has been recognised to establish a new set of basic definitions (taxonomy) for the building blocks that compose the so-called slice, i.e. the virtual network instantiation (which is virtual with regard to the abstracted view made of the building blocks of the FEDERICA infrastructure) and its architectural plane representation. These definitions will be established as a common nomenclature for the FEDERICA project. Other important aspects when defining a new architecture are the user requirements. It is crucial that the resulting architecture fits the demands that users may have. Since this deliverable has been produced at the same time as the contact process with users, made by the project activities related to the Use Case definitions, JRA1 has proposed a set of basic Use Cases to be considered as starting point for its internal studies. When researchers want to experiment with their developments, they need not only network resources on their slices, but also a slice of the processing resources. These processing slice resources are understood as virtual machine instances that users can use to make them behave as software routers or end nodes, on which to download the software protocols or applications they have produced and want to assess in a realistic environment. Hence, this deliverable also studies the APIs of several virtual machine management software products in order to identify which best suits FEDERICA’s needs.Postprint (published version

Applying OGC sensor web enablement to ocean observing systems

The complexity of marine installations for ocean observing systems has grown significantly in recent years. In a network consisting of tens, hundreds or thousands of marine instruments, manual configuration and integration becomes very challenging. Simplifying the integration process in existing or newly established observing systems would benefit system operators and is important for the broader application of different sensors. This article presents an approach for the automatic configuration and integration of sensors into an interoperable Sensor Web infrastructure. First, the sensor communication model, based on OGC's SensorML standard, is utilized. It serves as a generic driver mechanism since it enables the declarative and detailed description of a sensor's protocol. Finally, we present a data acquisition architecture based on the OGC PUCK protocol that enables storage and retrieval of the SensorML document from the sensor itself, and automatic integration of sensors into an interoperable Sensor Web infrastructure. Our approach adopts Efficient XML Interchange (EXI) as alternative serialization form of XML or JSON. It solves the bandwidth problem of XML and JSON.Peer ReviewedPostprint (author's final draft

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table