Classifier learning and decision making for a connection manager on a heterogeneous network

Sheng Tzong Cheng, Chih Wei Hsu, Gwo Jiun Horng, Jian Pan Li

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation-maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.

Original languageEnglish
Pages (from-to)2359-2389
Number of pages31
JournalWireless Personal Communications
Volume77
Issue number3
DOIs
Publication statusPublished - 2014 Aug

Fingerprint

Heterogeneous networks
Managers
Classifiers
Decision making
Network management
Embedded systems
Authentication
User interfaces
Software engineering
Wireless networks
Communication

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Cheng, Sheng Tzong ; Hsu, Chih Wei ; Horng, Gwo Jiun ; Li, Jian Pan. / Classifier learning and decision making for a connection manager on a heterogeneous network. In: Wireless Personal Communications. 2014 ; Vol. 77, No. 3. pp. 2359-2389.
@article{c92fef66fc0546ac86a0b307e64f702f,
title = "Classifier learning and decision making for a connection manager on a heterogeneous network",
abstract = "An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation-maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.",
author = "Cheng, {Sheng Tzong} and Hsu, {Chih Wei} and Horng, {Gwo Jiun} and Li, {Jian Pan}",
year = "2014",
month = "8",
doi = "10.1007/s11277-014-1642-1",
language = "English",
volume = "77",
pages = "2359--2389",
journal = "Wireless Personal Communications",
issn = "0929-6212",
publisher = "Springer Netherlands",
number = "3",

}

Classifier learning and decision making for a connection manager on a heterogeneous network. / Cheng, Sheng Tzong; Hsu, Chih Wei; Horng, Gwo Jiun; Li, Jian Pan.

In: Wireless Personal Communications, Vol. 77, No. 3, 08.2014, p. 2359-2389.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Classifier learning and decision making for a connection manager on a heterogeneous network

AU - Cheng, Sheng Tzong

AU - Hsu, Chih Wei

AU - Horng, Gwo Jiun

AU - Li, Jian Pan

PY - 2014/8

Y1 - 2014/8

N2 - An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation-maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.

AB - An attractive feature of the Connection Manager Intelligence Agent is its use of network traffic and multi-attribute behavior to locate the best network devices. This study has integrated this agent with a user interface; a network connection handoff; wired and wireless network device drivers; network management applications of the (plug-in) play interface; module-to-module communication authentication; and a DBus for added versatility. To reduce the time that developers of embedded systems spend on the software engineering of this module and to achieve rapid operational efficiency, an Open Source platform, such as MeeGo or Android, must be used. This study has implemented an interactive interface through the function (based on Fuzzy-AHP) of acquisition user behavior and machine designers, boosting iterations for User-Case. The algorithm maintains a set of weights as a distribution class table of cases, as in the parameter learning by user-case; it is quite possible that the expectation-maximization of maximum probability model can be classified by user behavior. In this study, user interaction showed that the agent satisfactorily matched user intent.

UR - http://www.scopus.com/inward/record.url?scp=84903700091&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903700091&partnerID=8YFLogxK

U2 - 10.1007/s11277-014-1642-1

DO - 10.1007/s11277-014-1642-1

M3 - Article

AN - SCOPUS:84903700091

VL - 77

SP - 2359

EP - 2389

JO - Wireless Personal Communications

JF - Wireless Personal Communications

SN - 0929-6212

IS - 3

ER -