SliceNet: end-to-end cognitive network slicing and slice management framework in virtualised multi-domain, multi-tenant 5G networks

©2018 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.Network slicing has emerged as a major new networking paradigm for meeting the diverse requirements of various vertical businesses in virtualised and softwarised 5G networks. SliceNet is a project of the EU 5G Infrastructure Public Private Partnership (5G PPP) and focuses on network slicing as a cornerstone technology in 5G networks, and addresses the associated challenges in managing, controlling and orchestrating the new services for users especially vertical sectors, thereby maximising the potential of 5G infrastructures and their services by leveraging advanced software networking and cognitive network management. This paper presents the vision of the SliceNet project, highlighting the gaps in existing work and challenges, the proposed overall architecture, proposed technical approaches, and use cases.Peer ReviewedPostprint (author's final draft

Smartphone-based real-time object recognition architecture for portable and constrained systems

Machine learning algorithms based on convolutional neural networks (CNNs) have recently been explored in a myriad of object detection applications. Nonetheless, many devices with limited computation resources and strict power consumption constraints are not suitable to run such algorithms designed for high-performance computers. Hence, a novel smartphonebased architecture intended for portable and constrained systems is designed and implemented to run CNN-based object recognition in real time and with high efciency. The system is designed and optimised by leveraging the integration of the best of its kind from the state-of-the-art machine learning platforms including OpenCV, TensorFlow Lite, and Qualcomm Snapdragon informed by empirical testing and evaluation of each candidate framework in a comparable scenario with a high demanding neural network. The fnal system has been prototyped combining the strengths from these frameworks and led to a new machine learning-based object recognition execution environment embedded in a smartphone with advantageous performance compared with the previous frameworks

Enable advanced QoS-aware network slicing in 5G networks for slice-based media use cases

© 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.Media use cases for emergency services require mission-critical levels of reliability for the delivery of media-rich services, such as video streaming. With the upcoming deployment of the fifth generation (5G) networks, a wide variety of applications and services with heterogeneous performance requirements are expected to be supported, and any migration of mission-critical services to 5G networks presents significant challenges in the quality of service (QoS), for emergency service operators. This paper presents a novel SliceNet framework, based on advanced and customizable network slicing to address some of the highlighted challenges in migrating eHealth telemedicine services to 5G networks. An overview of the framework outlines the technical approaches in beyond the state-of-the-art network slicing. Subsequently, this paper emphasizes the design and prototyping of a media-centric eHealth use case, focusing on a set of innovative enablers toward achieving end-to-end QoS-aware network slicing capabilities, required by this demanding use case. Experimental results empirically validate the prototyped enablers and demonstrate the applicability of the proposed framework in such media-rich use cases.Peer ReviewedPostprint (author's final draft

Analisa Aliran Fluida Pada Pipa Spiral Dengan Variasi Diameter Menggunakan Metode Computational Fluid Dinamics (CFD)

Pipa merupakan alat transportasi fluida yang sangat murah, pipa memiliki berbagai ukuran dan bentuk penampang. Penurunan tekanan aliran didalam pipa sangat penting untuk diketahui guna merancang sistem perpipaan. Kekasaran pipa, panjang pipa, diameter pipa, jenis fluida, kecepatan dan bentuk aliran adalah hal yang sangat terkait dengan penurunan tekanan (Pressure Drop). Tujuan penelitian ini adalah untuk melihat efek dari perubahan diameter terhadap penurunan tekanan (pressure drop) pada pipa spiral. Simulasi dalam penelitian ini dilakukan untuk mengetahui secara teknis faktor penting pada penurunan tekanan (pressure drop) pada pipa spiral. Dengan bantuan aplikasi CFD dilakukan variasi diameter yang mempengaruhi penurunan tekanan