A traffic model for the seamless integration of satellite and terrestrial mobile networks

1996 ◽  
Author(s):  
M. Ruggieri
Keyword(s):  
Mobile Networks ◽  
Traffic Model ◽  
Seamless Integration

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Flow-level traffic model for adaptive streaming services in mobile networks

2018 ◽  
Vol 137 ◽  
pp. 1-16 ◽  
Author(s):  
Yu-Ting Lin ◽  
Thomas Bonald ◽  
Salah Eddine Elayoubi
Keyword(s):  
Mobile Networks ◽  
Traffic Model ◽  
Adaptive Streaming ◽  
Streaming Services

Topic-Based Transparent Replication of Digital Assets

2010 ◽  
pp. 217-234
Author(s):  
Ulf Wehling
Keyword(s):  
Mobile Networks ◽  
Clustering Algorithm ◽  
Independent Manner ◽  
Seamless Integration ◽  
Physical Location ◽  
Hybrid Wireless Networks ◽  
Meta Information ◽  
Communication Flow ◽  
Rss Feeds ◽  
Digital Assets

In this chapter we propose a platform-independent concept for the transparent replication of digital assets in hybrid wireless networks. The concept allows a seamless integration with existing standards and technologies. It tackles and overcomes typical problems of common file sharing applications, such as the mixing of the logical property of a file being shared with its physical location. The identification mechanism introduced allows sharing on a per file basis, completely independent of their physical location, even tolerating subsequent relocation. RSS feeds are used as a basis to disseminate the files together with attached meta-information, such as tags, in a platform-independent manner. To optimize the communication flow among the mobile devices, a clustering algorithm for mobile networks is employed. The current prototype acts as proof-of-concept for the proposed concept.

Model for Wireless Base Station Goodput Evaluation in Vehicular Communication Systems

1970 ◽  
Vol 111 (5) ◽  
pp. 19-22
Author(s):  
A. Ipatovs ◽  
E. Petersons ◽  
J. Jansons
Keyword(s):  
Wireless Network ◽  
Mobile Networks ◽  
Communication Systems ◽  
Access Point ◽  
Base Station ◽  
Traffic Model ◽  
Vehicle Speed ◽  
Vehicular Communication ◽  
Point Model ◽  
Wireless Mobile

In this paper we present the model of wireless base station goodput evaluation. There was used access point model as queuing system with different kind of requests and cycling auto traffic model. Wireless mobile networks have different parameters, such as client stations distance to access point, number of clients in wireless network range, vehicle speed and traffic type. These parameters were analyzed and presented in this paper. Ill. 5, bibl. 17 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.348

scholarly journals Mobile microbiome networks across spatiotemporal gradients

2020 ◽  
Author(s):  
Rongling Wu ◽  
Libo Jiang ◽  
Christopher Griffin
Keyword(s):  
Mobile Networks ◽  
Scaling Laws ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Interaction Networks ◽  
Microbial Interaction ◽  
Seamless Integration ◽  
Microbial Network ◽  
Context Specific ◽  
The Impact

Abstract Background: The microbiome, a community of microbes that co-reside in biotic or abiotic environments, underlies biogeochemical cycling, plant and animal development, and human health. Increasing evidence shows that much of the role the microbiome plays is executed through complex interactions among microbes. Thus, network reconstruction has been increasingly used as a tool to disentangle internal workings within microbial communities.Results: We developed a general framework for recovering microbial interaction networks from any design of microbial experiment. This framework represents a quasi-dynamic game model (qdGM) derived from the seamless integration of evolutionary game theory and allometric scaling laws. The qdGM can not only characterize how individual microbes act singly, but also reveal how different microbes interact with each other to govern microbial community assembly. Beyond existing approaches that can only identify a single overall microbial network from a number of samples, our framework can track and visualize how interaction networks vary from sample to sample and covert sample-specific (personalized) networks into context-specific networks. More importantly, this framework can reconstruct such mobile microbial networks from steady-state data, facilitating the widespread use of network tools to understand the impact of the microbiome on natural processes.Conclusions: As proof of concept, we used the new framework to analyze human gut microbiota data and interspecific animal-associated microbiota data. Mobile networks reconstructed from each dataset can characterize previously unknown mechanisms that drive the change of microbial interaction architecture and organization along spatiotemporal gradients. This framework provides a tool to generate process-specific microbiome networks that can be readily translated into various biotechnological applications and evolutionary studies.

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