scholarly journals Network-Cloud Slicing Definitions for Wi-Fi Sharing Systems to Enhance 5G Ultra Dense Network Capabilities

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Maxweel Carmo ◽  
Felipe S. Dantas Silva ◽  
Augusto Venâncio Neto ◽  
Daniel Corujo ◽  
Rui Aguiar
Keyword(s):  
Mobile Network ◽  
Resource Control ◽  
System Level ◽  
Proof Of Concept ◽  
Dense Network ◽  
Network Systems ◽  
Wide Range ◽  
Wireless Broadband ◽  
End To End ◽  
Ultra Dense Network

Ultradense Networks (UDNs) seek to scale the 5th-Generation mobile network systems at unforeseen amounts of networks, users, and mobile traffic. We believe that the Wi-Fi sharing service is an asset in expanding 5G UDN capacity requirements for higher coverage and ubiquitous wireless broadband connectivity. However, the limitations of the Wi-Fi sharing pioneer deployment, along with other related works, has led our team to carry out further research. As a result, it was found that FOg CloUd Slicing for Wi-Fi sharing (FOCUS) is a suitable means of expanding 5G UDN capacities. FOCUS applies end-to-end Network-Cloud slice definitions on top of the Wi-Fi sharing technology, with the aim of offering multitenancy and multiservice support for a wide range of services, while meeting carrier-grade requirements and resource control at runtime and making full use of a “softwarized” approach. The feasibility of the FOCUS system is assessed in a real testbed deployment prototype, which allows an accurate view to be obtained of the basic functional principles and system-level proof-of-concept alongside the FON de facto Wi-Fi sharing service. The results suggest that FOCUS offers much greater benefits than FON, owing to its capacity to provide end-to-end Network-Cloud Slices while ensuring independent/isolated service delivery with resource adaptation at runtime.

A Composite Process for Establishing and Continuously Maintaining End-to-End Visibility in Multi-Tier Supplier Network Systems

2013 ◽  
Vol 2 (1) ◽  
pp. 17-44
Author(s):  
Joel Jolayemi ◽  
Festus Olorunniwo ◽  
Chunxing Fan ◽  
Xiaoming Li
Keyword(s):  
Network Systems ◽  
End To End

scholarly journals 5G Mobile Services and Scenarios: Challenges and Solutions

2018 ◽  
Vol 10 (10) ◽  
pp. 3626 ◽  
Author(s):  
Yousaf Zikria ◽  
Sung Kim ◽  
Muhammad Afzal ◽  
Haoxiang Wang ◽  
Mubashir Rehmani
Keyword(s):  
Mobile Network ◽  
Future Internet ◽  
Autonomous Driving ◽  
Mobile Services ◽  
Data Traffic ◽  
Fifth Generation ◽  
Wide Range ◽  
Industrial Iot ◽  
5G Network ◽  
Massive Number

The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.

A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network

2021 ◽  
pp. 1-1
Author(s):  
Kai Sun ◽  
Jiarun Yu ◽  
Wei Huang ◽  
Haijun Zhang ◽  
Victor C.M. Leung
Keyword(s):  
Dense Network ◽  
Ultra Dense Network ◽  
Handover Algorithm

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

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