Nonhomogeneous Network Traffic Control System

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International Journal of Engineering INTERNATIONALComputer VolumeOF COMPUTER ENGINEERING – JOURNAL 3, Issueand Technology (IJCET), ISSN 0976 6367(Print), ISSN 0976 – 6375(Online) 3, October-December (2012), © IAEME & TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 3, Issue 3, October - December (2012), pp. 394-405 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2012): 3.9580 (Calculated by GISI) www.jifactor.com

IJCET
©IAEME

NONHOMOGENEOUS NETWORK TRAFFIC CONTROL SYSTEM USING QUEUEING THEORY
Dr.K.PRASADH, 2 Mr.R.SENTHILKUMAR, 1 PRINCIPAL, MOOKAMBIKA TECHNICAL CAMPUS, MUVATTUPUZHA, KERALA, INDIA. 2, RESEARCH SCHOLOR, SINGHANIA UNIVERSITY, RAJASTHAN, INDIA. E-Mail: {ksprasaadh, trskme }@gmail.com.
1

ABSTRACT In computer networking, network traffic control is the progression of running, prioritizing, calculating or minimizing the network traffic across different network environment. It is essential to compute the network traffic for an efficient communication to establish the sources of network congestion and harass those problems particularly. To make the network traffic flow and communication as an effective one, the previous work used an optimal set of distributed traffic control laws (DCLs) for growing demand of heavy internet application in heterogeneous network environment. The optimality of the traffic control is achieved through multi-path based rate adaptation and load balancing schemes. But there is a great extent of optimal values to be misspecified and the rate adaptation has less congestion control and fairness for network traffic control. To make the network traffic control more defined, in this work, we are going to present a queuing theory for controlling the network traffic in non-homogeneous environment. This work discovers how to construct the vital model of network traffic study based on Queuing Theory. Using this, the network traffic forecasting ways and the firm congestion rate formula are obtained. By integrating the general network traffic monitor parameters, the inference and monitor process for the non-homogeneous network traffic reasonably computed. The performance of the proposed non-homogeneous network traffic control scheme using queuing theory is estimated with different set of nodes contrast to an existing optimal set of distributed traffic control laws.

Key words: Traffic control, Heterogeneous network, queuing theory, congestion rate.

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

1. INTRODUCTION Nowadays, it is mere significant to maintain computers up to date with safety measures. Worms and viruses can utilize a set of network usage, and are usually banned or predetermined by concerning the proper operating system patches. It is necessary to keep the network traffic more precise from network jamming characteristics. Network access is an integral and often-critical part of day-to-day business for most computed users because of network traffic. Network traffic monitoring is an imperative method for network presentation analysis and observe. The present study seeks out to discover how to construct the necessary form of network traffic analysis based on Queuing Theory. Therefore we can apprehend the inference and tracing process for the network traffic judiciously. Queuing Theory, besides called random service theory evaluate the arbitrary instruction of queuing occurrence, and constructs up the arithmetical model by examining the date of the network. During the calculation of the system, we can disclose the directive about the filing probability and decide the finest scheme for the system. Assuming Queuing Theory to guess the network traffic, it turn out to be the significant ways of network performance calculation, examination and inference and, through this way, we can reproduce the true system, it is practical and consistent for organizing, monitoring and defending the network. The quickly promising multimedia applications advertise in today’s active bureau has established severe confronts to network bandwidth on confined area networks. An approach to normalize and organize the network resource proficiently is mission decisive for endeavor networks. Organizing the network proficiently regularly adjourns the requirement to promote the network and reduce costs. The environment of relevance traffic might be exemplified by constant or variable bit rate, permanent or burst distribution of bandwidth, movable or permanent instant relationships among the end points and delay understanding. Local area network covers after current broadband networks in eminence of service and group of service technologies. The network strategy must state suitable network access rights and resources to examine the discriminated types of traffic. Application precise computation is imperative for rational traffic load and bandwidth organization. User-specific categorization is practical for substantiation, security and obligation of privileged behavior to definite users. 2. LITERATURE REVIEW
To control the network traffic in the network environment, several approaches have been presented earlier for controlling the traffic schemes at different forms. The existing approaches utilized algorithms centered on TCP kinds of traffic counting both experiential algorithms supported on control theory [1]. The widespread strategy for network traffic control is top-down control by concerning socalled development. A network can be splitted into numerous sub-networks and each sub-network can be splitted into some structure blocks. By using this approach [5], the scenario-based top-down control turns out to be more active, stretchy and more adaptive to the present traffic pattern [3]. For the network traffic organization concern, [6] proposed an improved ant algorithm with response to purpose conservatory and active pheromone devised the course variety behavior of each ant will be subjective according to their distinction. Privacy threat is one of the serious concerns in multi-hop wireless networks, where attacks such as transfer study [4] and stream tracing can be simply started, [7] proposed a new network coding supported privacy-preserving strategy against traffic study in multi-hop wireless systems.

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

The difficulty underneath multicast transfer in heterogeneous wireless networks [9] with system conventions is determined by MLMR algorithm [8] in order to attain the best coding sub-graph. The supervising of the network traffic based on queuing theory in heterogeneous environment [10] the examining of network traffic is required for estimating the effectiveness and self-reliance from steady operations of network [11] which we converse the presentation and calculation of network traffic organization and will give a proposal for manage the presentation of effort traffic based on queuing theory [12]. To improve the network traffic control scheme, in this work, the queuing theory is used for controlling the network traffic in the non-homogeneous network environment. 3. HETEROGENEOUS NETWORK TRAFFIC CONTROL SCHEME USING QUEUING THEORY The proposed work used queuing theory for efficiently controlling the network traffic schemes raised in the non-homogeneous network environment. Using queuing theory, the non-homogeneous network traffic is controlled by following the queue based the packet data arrival time and sending rate to the destination. The process of the queuing theory for network traffic control scheme in a non-homogeneous environment is explained briefly under this section. The architecture diagram of the proposed non-homogeneous network traffic control scheme using queuing theory is shown in fig 3.1.

Heteroge neous network

Set of source & destination nodes

Apply queuing theory

Network traffic occurs

N/w traffic controlled by computing arrival time and service time of nodes Fig 3.1 Architecture Diagram of the proposed non-homogeneous network traffic control scheme using queuing theory 396

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 3.1 Model of queuing theory In network communication, the sending and receiving of packet data and the arrangement of the data policy, interpret and distributing to the superior layer, in all these procedure, we can discover an easy queuing model. According to the Queuing Theory, this communicate practice can be distracted as Queuing theory model like fig. 3.2. In view of this type of easy data broadcasting system suits the queue model.

λ'

TJ

TD

TC

Fig 3.2 Queuing theory model From the above fig. 3.2, WhereTs=TJ+TD+TC..… (eqn 1) Parameter λ' TN λ Nq γ Ts TJ TD TC a µ Description Packet distributing rate of the sender. Transportation delay time Packet data incoming speed Amount of data packets accumulated in the buffer (temporary storage). Packets error rate in sending from receiver Packet data servicing time Decoding time Dispatching time Calculating time. inter-arrival time Service rate

Table 1 Constraints Description
The arrival rate, λ, is the average rate new nodes arrive measured in arrivals per time period. Common units are access/second. The inter-arrival time, a, is the average time between nodes arrivals. It is measured in time per nodes. A common unit would be seconds/access. a = 1 / λ ………………………….. (eqn 2) Queuing systems are usually described by three values separated by slashes Arrival distribution / service distribution / # of servers Where • M = Markovian or exponentially distributed • D = Deterministic or constant. • G = General or binomial distribution The pseudo code below described the queuing model for different types of services.

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

Step 1: Compute λ, µ, d-packet data Step 2: Evaluate ρ, ρ = λ/µ Step 3: If incoming of d is random with deterministic service, then The avg queue length = (2 ρ – ρ2) / 2 (1 ρ) Step 4: Else If incoming of d is random with random service, then The avg queue length = 2 – ρ / 2 µ (1 - ρ) Step 5: End If Fig 3.3 Pseudo code for Queuing theory model The above figure (Fig 3.3) described the process of computing the queue length based on incoming speed, service rate of the packet data enters into the network. Based on the deputation of services, the incoming data packet is defined. By computing the service rate, incoming rate of data packet in the non-homogeneous network environment, the proposed network traffic control scheme in non-homogeneous environment using queuing theory is done by using differential equations. The table (table 1) described the parameters used in the queuing theory concepts of non-homogeneous environment. 3.2 Queuing theory and non-homogeneous network traffic control The network traffic is very common in the non-homogeneous environment. The structure will be in inferior form, when the non-homogeneous network traffic becomes under tremendous state, in which guides to the network congestion. There are huge contracts of study about tracing the congestion at present, besides, the credentials which make utilize of Queuing Theory to explore the non-homogeneous network traffic rate emerge more and more. For forecasting the traffic rate, we often test the data disposal function of the router used in the network. Considering a router’s arrival rate of data flow in groups is λ, and the average time which the routers use to dispose each group is 1/µ, the buffer of the routers is B, if a certain group arrives, the waiting length of the queue in groups has already reached, so the group has to be lost. When the arriving time of group timeouts, the group has to resend the packet data. Suppose, the group’s average waiting time is 1/µ, we identify Pi (l) to be the arrival probability of the queue length for the routers group at the moment of t, supposing the queue length is i, P (t) = (P0 (l), P1 (l), . , Pi (l) ), i = 0,1, . . . B+1 ………….. (eqn 3) The table below described the parameters used in the queuing theory and non-homogeneous network traffic control schemes. 398

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Parameter B i P (t) 1/µ AC (t) Pn(t) Pn(t+ t) o(∆t) Description Buffer space of the routers Queue Length Probability of incoming speed of packet data at time t Average Waiting Time Jamming rate Probability of n incoming speed of packet data at time t Probability of n incoming speed of packet data at time t+ t No data packets arrived at time t Table 2 Constraints Description

Evaluation of non-homogeneous network congestion rate

Network congestion rate is varying all the time. The instant jamming rate and the steady jamming rate are frequently used to examine the network traffic in non-homogeneous network monitor. The instant rate AC (t) is the jamming rate at the instant of t. The AC (t) can be attained by explaining the system length of the queue’s prospect distributing, which is called Pn+1(t). Let Pn (t) (n=0,1,. . .,i+1) to be the incoming probability of the queue time-span for the routers set at the instant of t by allowing for the queue time-span is n . Then the queuing system of the router’s date sets suits simple Markov Process. In proportion to Markov Process, Pn(t) satisfies the subsequent system of discrepancy difference equations. Let, Pn(t) = prob { n data packets in the system in time t }………….. (eqn 4) Pn(t+ t) = prob {n data packets present in the system in time (t + t)} ………… (eqn 5) Case 1: For n ≥ 1 Pn(t+∆t) = Prob { n no. of data packets present in the system at time t } × prob { no data Packets coming in time (∆t)} × prob {no data packet leaving in time ∆t} + Prob { ( n -1) no. of data packets present in the system at time t } × prob { 1 data packet coming in time (∆t)} × prob { no data packet leaving in time ∆t } + Prob {(n +1) no. Of data packets present in the system at time t} × prob { No data packets coming in time (∆t)} × prob {1 data packet leaving in time ∆t}+. . . ⇒ Pn (t+∆t) = Pn (t) × {1- λ n ∆t + o (∆t)} × {1- µ n ∆t + o (∆t)} + Pn-1 (t) {λ n-1 ∆t + o (∆t) } × {1- µ n-1 ∆t + o(∆t) } + Pn+1 (t) {1- λ n+1 ∆t + o (∆t)} × {µ n+1 ∆t + o(∆t) } + o(∆t) …………. (eqn 6) Case 2: For n =0, P0 (t+∆t) = prob {no data packet present in the system in time (t+∆t) } = prob {no data packet present in time t } × prob { no data packet coming in time ∆t } + prob {one data packet present in time t} × prob {no data packet coming In time ∆t } × prob { one data packet leaving in time ∆t } . ⇒ P0 (t+∆t) = P0 (t) × {1- λ 0 ∆t + o(∆t)} + P1 (t) × {1- λ 1 ∆t + o(∆t)} …….. (eqn 7) 399

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Case 3: For n =C+1, PB+1(t+∆t) = prob {(B+1) no. of data packet present in the system in time (t+∆t )} = Prob {B no. Of data packet present in time t} × prob {1 data packet coming In time ∆t} × prob {no data packet leaving in time ∆t} + Prob {(B+1) no of data packets present in time t} × prob {no data packet leaving in time ∆t } ⇒ PB+1(t+∆t) = PB (t) × {λB ∆t + o (∆t)} × {1- µB ∆t + o (∆t)} + PB+1 (t) × {1- µB+1 ∆t + o(∆t)} …………… (eqn 8) The instant jamming rate of the non-homogeneous network can not be utilized to estimate the steady operating state of the system, so it is necessary to acquire the constant jamming rate of the system. The steady jamming rate means it will not modify with the time varying, when the system mechanism in a steady operating situation. The classification of the steady jamming rate is AC (t) = lim AC (t) ………. (eqn 9)
t− ∞

In view of dispensing of the steady extent of the queue and C as the defense of the router, the steady jamming rate can be attained in two ways: initially, we acquire the instant jamming rate, and then build its bound out. According to its classification, it can be attained with the dispensing of the extent of the queue. The second method is distribution is done through steady state equations. The pseudo code below (Fig 3.4) described the process of queuing theory with non-homogeneous network environment. Step 1: Identify the incoming packet data d Step 2: If more number of d arrives in non-homogeneous network environment Step 3: Apply Queuing theory Step 4: Compute traffic rate To test the data packet lost Step 5: Find the incoming speed of data packet λ Step 6: Find the average time of packet data left the group 1/µ Step 7: If λ exceeds the time t, Step 8: resend the packet data Step 9: Compute P (t) Step 10: End If Step 11: If network congestion occurs Compute AC (t) Step 12: End if Step 13: Based on queue length, the probability of nonhomogeneous network Congestion rate evaluated network traffic occurs

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Step 14: If n>=1 Use eqn 6 Step 15: else If n = 0 Use eqn 7 Step 16: Else Use eqn 8 Step 17: End If Step 18: End if Step 19: Control the network traffic by computing the traffic rate and congestion rate Step 20: End

Fig 3.4 Pseudo code for Queuing theory model in non-homogeneous network environment
The above fig (Fig 3.4) described the entire process of controlling the non-homogeneous network environment using queuing theory model with different set of equations. Based on incoming speed of the data packet, the service rate of the network is evaluated and the traffic rate is also being identified to test the data packet lost. If network congestion occurs, the congestion rate is computed based on the queue length and the probability of the non-homogeneous network traffic is computed based on the packet data entering and leaving the group into the nonhomogeneous network environment. Based on the queuing system described, the nonhomogeneous network traffic is controlled efficiently.

4. EXPERIMENTAL EVALUATION
The proposed non-homogeneous network traffic control method is efficiently done through queuing theory. Based on queuing theory, the incoming data packets of the nonhomogeneous network environment followed the queue based on incoming and service rate. The experimental results on the non-homogeneous network of different internet application are achieved from the test data sets of traffic streams used from Internet Service Providers from their universal connectivity servers. The diverse environment of universal connectivity servers presents dynamically unreliable traffic streams as per the user insist and the variable character of the non-homogeneous metrics vital for the direct schemes to be accepted. The performance of the proposed non-homogeneous network traffic control method using queuing theory is measured in terms of i) Packet data traffic ii) Queuing efficiency iii) Network congestion

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

5. RESULTS AND DISCUSSION
From this work, it is being observed that the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory (NHNTC by QT) and the network traffic is keenly observed by maintaining queue and the network congestion are also been eradicated by eomputign the traffic rate. Compared to an existing optimal set of distributed traffic control laws (OSDTC), the proposed network traffic scheme using queuing theory performs well. The below table and graph described the performance of the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory.

No. of packets (p)
25 50 75 100 125

Packet data traffic (kbps) Proposed NHNTC by QT Existing OSDTC 75 100 90 135 110 210 124 280 150 350 Table 5.1 No. of packets vs. Packet data traffic

The above table (table 5.1) described the packet data traffic arised when more number of packets entered into the network for packet data communication. The outcome of the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory (NHNTC by QT) is compared with an existing optimal set of distributed traffic control laws (OSDTC).

350 300 250 Packet data 200 traffic (kbps) 150 100 50 0 25 50 75 100 125

No. of packets Proposed NHNTC by QT Existing OSDTC

Fig 5.1 No. of packets vs. Packet data traffic
Fig 5.1 described the process of packet data traffic arised in the non-homogeneous network environment. The proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory used queuing models for organizing the data packets according to the servicing and incoming time. The packet data traffic management is being efficient in the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory compared to an existing optimal set of distributed traffic control laws only distribute and formed the packet data based on control laws. In the proposed NHNTC by QT, the queuing model is efficiently formed by evaluating the traffic rate and the network congestion rate. The variance in efficiency of packet data traffic is 40-60% low in the proposed NHNTC by QT.

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

No. of packets (p)
25 50 75 100 125

Queuing Efficiency (%) Proposed NHNTC by QT 20
38 62 84 100

Existing OSDTC 14
25 54 76 87

Table 5.2 No. Of packets vs. Queuing efficiency
The above table (table 5.2) described the queuing efficiency when more number of packets entered into the network for packet data communication. The outcome of the proposed nonhomogeneous network traffic scheme efficiently controlled by queuing theory (NHNTC by QT) is compared with an existing optimal set of distributed traffic control laws (OSDTC).

100 80 60 Queuing Efficiency (%) 40 20 0 25 50 75 100 125

No. of packets Proposed NHNTC by QT Existing OSDTC

Fig 5.2 No. Of packets vs. Queuing efficiency
Fig 5.2 described the queuing efficiency of the non-homogeneous network environment. The proposed non-homogeneous network traffic scheme efficiently controlled by identifying the packet data lost in the network. The packet data traffic management is being efficient, since it followed the queue based on the probability of the nodes in the network in the proposed nonhomogeneous network traffic scheme efficiently controlled by queuing theory compared to an existing optimal set of distributed traffic control laws only distribute and formed the packet data based on control laws. In the proposed NHNTC by QT, the queuing model is efficiently formed by evaluating the service ti. The variance in efficiency of packet data traffic is 20-25% high in the proposed NHNTC by QT.

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

Network Congestion Rate (%) Proposed Existing NHNTC by OSDTC QT 10 5 9 20 13 16 30 21 27 40 28 36 50 34 49 Table 5.3 No. Of nodes vs. Network Congestion rate
The above table (table 5.3) described the network congestion rate when more number of packets entered into the network for packet data communication. The outcome of the proposed nonhomogeneous network traffic scheme efficiently controlled by queuing theory (NHNTC by QT) is compared with an existing optimal set of distributed traffic control laws (OSDTC).
50 40 Network 30 Congestion rate 20 (%) 10 0 10 20 30 40 50

No. of nodes (n)

No. of nodes Proposed NHNTC by QT Existing OSDTC

Fig 5.3 No. Of nodes vs. Network Congestion rate
Fig 5.3 described the process of network congestion rate in the non-homogeneous network environment. Since the proposed non-homogeneous network traffic scheme efficiently used the queuing models for organizing the data packets according to the servicing and incoming time, the network congestion rate is low. The packet data traffic management is being efficient in the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory compared to an existing optimal set of distributed traffic control laws only distribute and formed the packet data based on control laws. In the proposed NHNTC by QT, the network congestion is efficiently cleared and the variance in efficiency of network congestion rate is 20-30% low in the proposed NHNTC by QT. Finally, it is considered that the proposed NHNTC by QT efficiently followed the queuing model by distributing the packet data based on the servicing time and arrival rate of the packet data in the non-homogeneous network environment. The network traffic also be controlled in a reliable manner. 404

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME

6. CONCLUSION
In existing optimal set of distributed traffic control laws, multi-path based rate adaptation and load-balancing schemes are used for traffic control schemes. The issues raised over existing optimal set of distributed traffic control laws are well handled by the proposed nonhomogeneous network traffic scheme efficiently controlled by queuing theory. The proposed non-homogeneous network traffic scheme, at first, formed the queue based on the packet data arrival. If more number of packets arrived, the proposed used queuing model by maintaining and servicing the packet data based on arrival rate. After that, if network congestion occurs, the proposed NHNTC by QT efficiently controlled the traffic by evaluating the traffic rate. The experimental results showed that the proposed non-homogeneous network traffic scheme efficiently controlled by queuing theory is efficient in terms of network congestion rate, queuing efficiency and the proposed one outperforms well in the network traffic control process in the non-homogeneous network environment.

REFERENCES:
[1] Bernardo A. Movsichoff, Constantino M. Lagoa, and Hao Che “ End-to-End Optimal Algorithms for Integrated QoS, Traffic Engineering, and Failure Recovery “ IEEE /ACM Transactions on networking, Vol. 15, No. 4, August 2007. [2] B. A. Movsichoff, C. M. Lagoa, and H. Che, “Decentralized optimal traffic engineering in connectionless networks,” IEEE J. Sel. Areas Commun., vol. 23, pp. 293–303, Feb. 2005. [3] F. Paganini, Z. Wang, J. C. Doyle, and S. H. Low, “Congestion control for high performance, stability, and fairness in general networks,” IEEE/ACM Trans. Netw., vol. 13, no. 1, pp. 43–56, 2005. [4] Ping Luo, Hui Xiong, et. Al., ‘ Information-Theoretic Distance Measures for Clustering Validation: Generalizationand Normalization ‘, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 9, SEPTEMBER 2009. [5] YubinWang VranckenJ. Et.Al., “Implementing scenarios coordination for road network traffic control”, 2010 IEEE International Conference on Systems Man and Cybernetics (SMC). [6] Qi Bing Jun Lu et. Al., ‘An Improved Ant Algorithm for Network Traffic Control’, 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. [7] Yanfei Fan Yixin Jiang et.al., “Network Coding Based Privacy Preservation against Traffic Analysis in Multi-Hop Wireless Networks”, IEEE Transactions on Wireless Communications, 2011. [8] Shah-Mansouri, V. et. Al., “Lifetime-resource tradeoff for multicast traffic in wireless sensornetworks”, IEEE Transactions on Wireless Communications, 2010. [9] Duk Kyung Kim Griffith, D. et. Al., „A New Call Admission Control Scheme for Heterogeneous Wireless Networks“, IEEE Transactions on Wireless Communications, 2010. [10] Kamali,S.H. Hedayati,M.et.Al.,“The Monitoring of the Network Traffic Based On Queuing Theory and Simulation in Heterogeneous NetworkEnvironment”, ICCTD '09. International Conference on Computer Technology and Development, 2009. [11] Jong-hwan Kim et. Al., “Active Queue Management for Flow Fairness and Stable Queue ength”, IEEE Transactions on Parallel and Distributed Systems, 2011.

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