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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 06, JUNE 2015 ISSN 2277-8616 

369IJSTR©2015www.ijstr.org 

Design And Planning Of E- LearningEnvironment/E-Education System On

Heterogeneous Wireless Network Control SystemThandarOo, HlaMyo Tun

Abstract: The purpose of this research is to provide a more efficient and effective communication method between teacher and student with the use ofheterogeneous network. Moreover, the effective use of heterogeneous network can be emphasized. The system of e-education can develop utilizingwireless network.The e-Education system can help students to communicate with their teacher more easily and effectively using a heterogeneouswireless network system. In this wireless network system, students, who are blind or dumb, will also be able to communicate and learn from the teacheas normal students can do. All the devices or laptops will be connected on wireless LAN. Even when the teacher is not around, he will be able to help hisstudents with their study or give instructions easily by using the mobile phone to send text or voice signal. When the teacher sends information to thedumb student, it will be converted into sign language for the student to be able to understand. When the dumb student sends the information to theteacher, it will be converted into text for the teacher to understand. For the blind student, text instructions from the teacher will be converted into audiosignal using text-to-speech conversion.Thus, the performance of heterogeneous wireless network model can evaluate by using Robust OptimizationMethod. Therefore, the e-Education system’s performance improves by evaluating Robust Optimization Method.

Keywords: Heterogeneous Wireless Network, e-Education, Robust Optimization Method, Network Control System———————————————————— 

I. INTRODUCTION To provide high-speed communications services to severalmobile users in a seamless manner, multiple wireless accesstechnologies are integrated to form a heterogeneous networkwhere the users are able to choose between different wirelessnetworks according to their preference, performance and cost.Because of the diverse usage patterns and QoS requirementsfor wireless data services, the users are able to reduce thecosts by exploiting different radio access technologies.Amobile network operator could accomplish a related increasein network capacity in disparate customs in such a way that hecould prefer toadvance the air interfaces in cellular systems,to arrange denser (heterogeneous) networks, to leasecapacity from specialized (WLAN) network contributors and tosplit (radio access) transportation with additional operators. Inthis research, the management of radio source for thisintegrated heterogeneous wireless network control system willbe proposed. At the mobile terminal, an algorithm will bedeveloped to find the most efficient path for transmitting thedata which can provide required QoS with least connectioncost. An optimal network model will be created to get theoptimal decision for intelligent network control system.Theessential idea behind this paper is to provide a platform to thestudents by using heterogeneous wireless network system.The proposed culture and association system, which has beenpilot-tested, is expected to provide the best possible e-Learning to community so that they can be confident and

situate compact in this humankind and struggle withindividuals.

Heterogeneous wireless access networks are todayconsidered to be a key enabler for affordable wireless accessto the internet. While mobile systems hold out high-qualitycoverage and reliability for low and realistic data ratesWireless Local Area Network (WLAN) technologies harmonizefixed broadband connectivity with local area experience forhigher data rates. Thanks to the diverse usage patterns andquality of service requirements for mobile and wireless dataservices, operators may reduce their costs significantly byexploiting different radio access technologies.Heterogeneouswireless network are purposed for future wireless accessnetworks because it consists of multiple radio accessstandards and base stations technologies shaping. To whacoverage these preferences are dominated in practice will ocourse be enclosure unambiguous and ultimately depend on anumber of technical, financial, marketing and dictatoriafactors. Hence, identifying universal supplies for futuresystems is a complicated work which is of great significancenot only for the operators, but also for equipment retailer. Inthis research, Robust Optimization in Linear Programmingwasproposed for uncertain demand of minimum channel gain flowThe approach presented in this research is based on atransformation of uncertainty in the supply/demand vector touncertainty in the gain vector.Many dynamic sub-carrieassignment algorithms have been described in todayCompared to static schemes these dynamic assignmenalgorithms can provide a performanceincrease of 100% perterminal, simply by utilizingthe given bandwidth and transmipower much better. Inthe latter case each sub-carrier receivesan equal amountof transmit power. Together with a target biterror probability the suitable modulation type can be obtainedat once. In addition, the total transmit power is limited.

II. METHODOLOGY There are many approaches to address data uncertainty inrobust optimization method.Robust optimization method canpresent a different approach to handle datauncertainty.Robust optimization method is to find a solutionthat can cope best with all possible realization of the uncertaindata.It is also well-known because it immunes againstuncertainty.According to robust optimization method, it is

 ________________________

  ThandarOo, HlaMyo Tun  Department of Electronic Engineering, Mandalay

Technological University, Mandalay  thandaroo25.tdo@gmail.com, hlamyotun@gmai.com 

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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 06, JUNE 2015 ISSN 2277-8616 

370IJSTR©2015www.ijstr.org 

called cost.In this thesis, cost is assumed as distance.It isused to be good algorithms more in the system.In robustoptimization method, cost efficient and data in the constraintsof an integer programming problem are subject to uncertainty.As both cost efficient and data in the constraints of an integerprogramming problem are subject to uncertainty, robustinteger programming problem of moderately large size isproposed.Since only the cost coefficient is subject to

uncertainty and the problem is 0-1 discrete optimizationproblem on n variable, the problem can be calculated bysolving at most n+1 instance of the original problem.Whenonly the cost coefficient is subject to uncertainty and theproblem is minimum cost flow problem, the feasible solution iscaptured by solving a collection of minimum cost flow problemin a modified network. In this thesis, it is concerned that robustminimum cost flows can also be solved by explaining acollection of modified nominal minimum cost flows. Given adirected graph G = (N, A), the minimum cost flow can bedescribed as below:

min z(x)= xijcij

i,j

∈A

 

bi= xij

 j\ i,j∈A- x ji

 j\  j,i∈A 

for all i∈ N 

0≤xij≤uijfor all (i,j)∈A

It can be assumed that X is a feasible solution and theuncertain cost entry is inserted, the robust minimum cost flowproblem is as follow.

Z∗ = minc'x+max{S\S⊂A,S≤Γ}(i,j)∈Sdijxij 

Subject to x

∈  X

In this equation, both minimization and maximizing includes.According to equation,

Z∗ = minθ≥0 Z(θ) Where,

Zθ = Γθ + min c'x + pij

(i,j)∈A

 

Subject top

ij≥dijxij-θ 

pij≥0 

x∈X

For a fixed θ ≥ 0, the minimum cost flow problem can becalculated by eliminating the variables p

ij.

Zθ=Γθ+minc'x+ max [xij-θ

dij

,0]

(i,j)∈A

 

The nominal cost values csj   for new arcs are chosen higher

than the costs of the longest used path in the basic demandminimum cost flow problem. Nominal cost,

csj = Ibasici+1

Extra cost or the uncertainty cost,

dsj= Ifullmax+3- csj 

Possible extra demand for new arc,

usj  = -zi 

Where zi = the difference from basic demand to full demand

Let G΄ = (N΄, A΄), be the new directed graph. It can illustratethat explaining a linearminimum cost flow problem with dataas above.

i   j

i   i   j

 j

(cij,uij)

(cij,uij) (0,θ /dij)

 Figure 1. Inserting new nodes and new arcs

For every arc (i,j) ∈ A, two new nodes i΄ and j΄ replace the arc(i,j) with arcs (i, i΄), (i΄, j΄),(j΄, j) and (i΄, j) with the next costsand capacities as shown in Fig.1.

cii΄=cij 

uii΄=uij 

c j  ́j=0 

u j  ́j

=∞ 

ci  ́j=0 

ui  ́j=θ

dij

 

ci  ́j΄=dij 

ui  ́j΄=∞ 

The optimal solution of the linear minimum cost flow problemcan be calculated with data as above.The widespread lineaprogramming problem with robust optimization using MATLABis the simulation in feasible solution for constraint problemsAs a consequence, the simulation does not crutch up very welcommon linear programming features such as minimum cosnetwork flow with uncertain demand. However, the

widespread linear programming simulation will not be able tobe handled by shifting the cost vector to gain vector. Therelatively new robust optimization minimum channel gain flowproblem is the best simulation which can switch minimumchannel gain flow problem for maximizing the minimumthroughput in heterogeneous wireless network. Graph Theoryis responsible for modeling given situation into a network ofnodes and arcs.Nodes assume as locations such as plantsfactories and market. The transportation links betweenlocations are considered as arcs. The amount of commoditiesis taken into account as the flow amount on arc. Nodes andarcs are specified depending on the given situations.Networkmodel can be represented in either directed graph o

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