scholarly journals Trace-based mobile network emulation

1997 ◽  
Author(s):  
Brian D. Noble ◽  
M. Satyanarayanan ◽  
Giao T. Nguyen ◽  
Randy H. Katz
Keyword(s):  
Mobile Network ◽  
Network Emulation

scholarly journals Trace-based mobile network emulation

1997 ◽  
Vol 27 (4) ◽  
pp. 51-61 ◽  
Author(s):  
Brian D. Noble ◽  
M. Satyanarayanan ◽  
Giao T. Nguyen ◽  
Randy H. Katz
Keyword(s):  
Mobile Network ◽  
Network Emulation

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

2009 ◽  
Vol E92-B (12) ◽  
pp. 3893-3902
Author(s):  
Hyeong-Min NAM ◽  
Chun-Su PARK ◽  
Seung-Won JUNG ◽  
Sung-Jea KO
Keyword(s):  
Video Streaming ◽  
Mobile Network ◽  
Bandwidth Estimation ◽  
Adaptive Video Streaming ◽  
Adaptive Video

Study of Ad hoc Networks with Reference to MANET, VANET, FANET

2017 ◽  
Vol 7 (7) ◽  
pp. 390 ◽  
Author(s):  
Bodhy Krishna .S
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc Network ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Network ◽  
Wireless Ad Hoc Network ◽  
Mobile Ad Hoc ◽  
Autonomous Nodes ◽  
Hoc Networks ◽  
Network Administration

A wireless ad hoc network is a decentralized type of wireless network. It is a type of temporary computer-to-computer connection. It is a spontaneous network which includes mobile ad-hoc networks (MANET), vehicular ad-hoc networks (VANET) and Flying ad-hoc networks (FANET). A MANET is a network that has many free or autonomous nodes often composed of mobile devices that can operate without strict top-down network administration [1]. A VANET is a sub form of MANET. It is a technology that uses vehicles as nodes in a network to create a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. This paper discusses the characteristics of these three ad-hoc networks.

SIMULASI NEURAL NETWORK UNTUK PREDIKSI LOKASI USER PADA MOBILE NETWORK

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander Driyarkoro ◽  
Nurain Silalahi ◽  
Joko Haryatno
Keyword(s):  
Neural Network ◽  
Mobile Station ◽  
Mobile Network ◽  
Learning Rate ◽  
Noise Factor ◽  
Optimal Learning ◽  
Air Interface ◽  
Location Area ◽  
Optimal Learning Rate

Prediksi lokasi user pada mobile network merupakan hal sangat penting, karena routing panggilan pada mobile station (MS) bergantung pada posisi MS saat itu. Mobilitas MS yang cukup tinggi, terutama di daerah perkotaan, menyebabkan pencarian (tracking) MS akan berpengaruh pada kinerja sistem mobile network, khususnya dalam hal efisiensi kanal kontrol pada air interface. Salah satu bentuk pencarian adalah dengan mengetahui perilaku gerakan yang menentukan posisi MS. Dari MSC/VLR dapat diketahui posisi MS pada waktu tertentu. Karena location area suatu MS selalu unik dari waktu ke waktu, dan hal itu merupakan perilaku (behaviour) MS, maka dapat dibuat profil pergerakannya. Dengan menggunakan Neural Network (NN) akan diperoleh location area MS pada masa yang akan datang. Model NN yang digunakan pada penelitian ini adalah Propagasi Balik. Beberapa parameter NN yang diteliti dalam mempengaruhi kinerja prediksi lokasi user meliputi noise factor, momentum dan learning rate. Pada penelitian ini diperoleh nilai optimal learning rate = 0,5 dan noise factor = 1.

Mobile network synthesis strategy

2019 ◽  
pp. 98-101
Author(s):  
V. Lyandres
Keyword(s):  
Average Distance ◽  
Mobile Network ◽  
Base Station ◽  
Service Area ◽  
Base Stations ◽  
Frequency Assignment ◽  
Network Synthesis ◽  
Universal Method ◽  
Synthesis Strategy ◽  
Adaptive Vector Quantization

Introduction:Effective synthesis of а mobile communication network includes joint optimisation of two processes: placement of base stations and frequency assignment. In real environments, the well-known cellular concept fails due to some reasons, such as not homogeneous traffic and non-isotropic wave propagation in the service area.Purpose:Looking for the universal method of finding a network structure close to the optimal.Results:The proposed approach is based on the idea of adaptive vector quantization of the network service area. As a result, it is reduced to a 2D discrete map split into zones with approximately equal number of service requests. In each zone, the algorithm finds such coordinates of its base station that provide the shortest average distance to all subscribers. This method takes into account the shortage of the a priory information about the current traffic, ensures maximum coverage of the service area, and what is not less important, significantly simplifies the process of frequency assignment.

An overview of the 5G mobile network architecture

2018 ◽  
Author(s):  
Phanidra Palagummi ◽  
Vedant Somani ◽  
Krishna M. Sivalingam ◽  
Balaji Venkat
Keyword(s):  
Wireless Network ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Communication Technologies ◽  
Future Generation ◽  
Network Function Virtualization ◽  
Low Latency ◽  
High Bandwidth ◽  
Network Technologies

Networking connectivity is increasingly based on wireless network technologies, especially in developing nations where the wired network infrastructure is not accessible to a large segment of the population. Wireless data network technologies based on 2G and 3G are quite common globally; 4G-based deployments are on the rise during the past few years. At the same time, the increasing high-bandwidth and low-latency requirements of mobile applications has propelled the Third Generation Partnership Project (3GPP) standards organization to develop standards for the next generation of mobile networks, based on recent advances in wireless communication technologies. This standard is called the Fifth Generation (5G) wireless network standard. This paper presents a high-level overview of the important architectural components, of the advanced communication technologies, of the advanced networking technologies such as Network Function Virtualization and other important aspects that are part of the 5G network standards. The paper also describes some of the common future generation applications that require low-latency and high-bandwidth communications.

INTEGRATED MEDICAL DATA MANAGEMENT SYSTEMS REVIEW

2019 ◽  
Author(s):  
Ihor Pysmennyi
Keyword(s):  
Health Informatics ◽  
Quality Care ◽  
Exchange Process ◽  
Knowledge Exchange ◽  
Clinical Information ◽  
Mobile Network ◽  
Developed Countries ◽  
Management Systems ◽  
Learning Techniques ◽  
Drastic Increase

In recent years we’ve seen breakthrough research success in medicine and computer science enabled by novel technology advancements, data analyses capabilities and learning techniques. Despite this, quality care doesn’t have full cove­ rage even in developed countries and access to care is recognised as one of the biggest challenges to the global healthcare system. Bound with population growth in remote areas in developing regions, which lack skilled professionals and medical resources, as well as aging in developed countries this caused a strong need for increasing healthcare effectiveness. Enabled by development of cloud technologies, quick expansion of mobile network coverage and internet access Clinical Information Management Systems integrated with decision support systems, Telemedicine (inclu­ ding distributed Virtual Healthcare Teams and medical imaging), Mobile Healthcare, medical Internet of Things (mIoT), Consumer Health Informatics with personal intelligent health assistants, Health Information Exchanges and deep learning techniques for diagnostics and knowledge extraction are among the state-of-the-art solutions which are more or less successfully used for coping with the problem mentioned above. This paper reviews current situation with implementing these novel informational systems, analyses their advantages, drawbacks, implementation impediments and outcome effectiveness suggesting platform for empowering their integration and maximizing output of each module. Such solution will have a synergy effect and result in a drastic increase of medical resource utilization effectiveness, service quality and providing bigger and fuller coverage with less spending at the same time empowering knowledge exchange process and laying foundation for future development and innovations in the whole healthcare domain.

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