ijarcet vol-2-issue-7-2311-2318

ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)

Volume 2, Issue 7, July 2013

2311 www.ijarcet.org

Improving End-to-End Delay Distribution in Wireless

Sensor Networks. R.J Lawande1, A.H. Ansari2

1 PDVVPCOE, Ahmednagar, Maharashtra,India 2P.R.E.C, Loni, Ahmednagar, Maharashtra,India.

Abstract— In today’s world emerging applications of

wireless sensor networks (WSNs) require real-time Qos.

average delay and end to end delay distribution is

important in WSNS. In a typical delay monitoring WSNS,

multiple reports are generated by several nodes when a

physical event occurs, and are then forwarded through

multi-hop communication to a sink that detects the event.

To improve the delay detection reliability, usually timely

delivery of a certain number of packets is required. The

previous delay analysis papers fail to give the single hop

delay distribution, also the busty traffic is not considered

so in this paper, a comprehensive cross-layer analysis

framework is used. The simulations on Network Simulator

2 show the average and end to end delay for both

deterministic and random deployments. Our model gives

closed form expressions for obtaining the average delay

and End to End delay characteristics and models each

node as a discrete time queue. Moreover, the simulation

and experiments show the Throughput and the packet loss

in WSNs. In this paper, the comparison of the CSMA/CA

Mac protocol and cross layer protocol for average delay

and End to end dealy,Throuhput and Packet loss is done

for WSNs.

Index Terms— Average Delay distribution, End to end

delay, real time systems, Throughput, wireless sensor

networks.

I. INTRODUCTION

Real time quality of service is necessary and

important for wireless sensor networks. The wireless sensor

networks are extensively used in the connectivity infrastructure and distributed data network.Timing and

reliability are the two important factors for the quality of

service gurantees.To characterize average delay and end to

end delay distribution is fundamental for the real time

Ravindra J . lawande

1, Department of Electronics and Telecommunication

PDVVPCOE, Ahmednagar, Maharashtra, India

Abdul H. Ansari2, Department of Electronics and Telecommunication, PREC

Loni, Maharashtra, India

communication applications with the probabilistic QoS

guarantees. Also to calculate the Throughput and the packet

loss is important for the real time wireless sensor networks

applications.[3] First, a accurate and reliable cross layer

framework is developed to characterize the average delay and

end to end delay distribution in both deterministic and random

deployments of nodes.[1] Second, Throughput and the Packet

loss of the CSMA/CA Mac protocol and Cross layer protocol

is calculated by the graphical analysis.

In existing system, CSMA/CA Mac protocol is conducted to illustrate how developed framework can

analytically predict the distribution of the end-to-end

delay.[1][2] It does not give the guarantee of quality of

service. In proposed system, present comprehensive cross-

layer analysis framework, which employs a stochastic queuing

model in realistic channel environments, is developed for

average delay and end to end delay in WSNs .The cumulative

distribution function (cdf) of the delay can be used as a metric

to calculate delay. The end-to-end delay distribution depends

on the deterministic deployment and random deployment. For

both deployments, focus on the steady-state behavior of the routing protocol.

This paper is organized as follows. Section I gives

the introduction. Section II reviews some previous work of

end-to-end delay. Section III introduces the software system

used for delay analysis. Section IV gives results of proposed

system. Section V concludes this paper.

II. Literature Survey

Yunbo Wang Mehmet C. Vuran Steve Goddard [1] have

proposed To improve the event detection reliability, usually

timely delivery of a certain number of packets is required.

Traditional timing analysis of WSNs are, however, either

focused on individual packets or traffic flows from individual

nodes a spatio-temporal fluid model is developed to capture

the delay characteristics of event detection in large-scale

WSNs. Mean delay and soft delay bounds are analyzed for

different network parameters. The resulting framework can be

utilized to analyze the effects of network and protocol

parameters on event detection delay to realize real-time

operation in WSNs. but fail to give single hop delay

distribution.




Yunbo Wang, Mehmet C. Vuran and Steve Goddard

have proposed that Limited energy resources in

increasingly sophisticated wireless sensor

networks(WSNs) call for a comprehensive crosslayer

analysis of energy consumption in a multi-hop network.

reliability analysis in such networks, the statistical

information about energy consumption and lifetime is

required Traditional energy analysis approaches only

focus on the average energy consumed. a stochastic

analysis of the energy consumption in a random network

environment. the distribution of energy consumption for

nodes in WSNs during a given time period is found. Fail

to analyze the energy consumption for more MAC

protocols, such as BMAC , XMAC , using our model.

Mehmet C. Vuran, Member, IEEE, and Ian F. Akyildiz,

Fellow, proposed that[3] Severe energy constraints of

battery-powered sensor nodes necessitate energy-

efficient communication in Wireless Sensor Networks

(WSNs). the vast majority of the existing solutions are

based on the classical layered protocol approach, which

leads to significant overhead a cross-layer protocol

(XLP) is introduced, which achieves congestion control

routing, and medium access control in a cross-layer

fashion.

The design principle of XLP is based on the

cross-layer concept of initiative determination, which

enables receiver-based contention, initiative-based

forwarding, local congestion control, and distributed

duty cycle operation to realize efficient and reliable

communication in WSN .Fail to investigate of various

networking functionalities such as adaptive modulation

,error control, and topology control in a cross-layer

fashion to develop a unified cross-layer communication

module Omesh Tickoo and Biplab Sikdar[2] proposed

that Traditional system fail to evaluating the queueing

delays and channel access times at nodes in wireless

networks paper presents an analytic model for evaluating

the queueing delays and channel access times at nodes in

wireless networks using the IEEE 802.11 Distributed

Coordination Function (DCF) as the MAC protocol. The

model can account for arbitrary arrival patterns, packet

size distributions and Number of nodes. Fail to give end

to end delay analysis for deterministic and random

deployment of nodes in WSN.

III. System Overview

Wireless sensor networks (WSNs) have been utilized in

many applications as both a connectivity infrastructure

and a distributed data generation network due to their

ubiquitous and flexible nature . Increasingly, a large

number of WSN application requires real-time quality-

of-service (QoS) guarantees. Such QoS requirements

usually depend on two common parameters: timing and

reliability. The resource constraints of WSNs, however,

limit the extent to which these requirements can be

guaranteed. Furthermore, the random effects of the

wireless channel prohibits the development of

deterministic QoS guarantees in these multihop

networks. Consequently, a probabilistic analysis of QoS

metrics is essential to address both timing and reliability

requirements.

In our analysis, we consider a network

composed of sensor nodes that are distributed in a 2-D field.Sensor nodes report their readings to a sink through

a multihop route in the network.Two different types of

network deployments are investigated.Figure.1shows

the architectural diagram of our cross layer framework

Network deployment is divided into two types

1.Determnistic deployment:-The deterministic

deployment has the position of sensor nodes is fixed

with deterministic locations which is useful to calculate

the single hop delay distribution with queuing model.

2.Random Deployment:-Random deployment uses

Poisson point process with log normal fading channel.

queuing model deals with inter arrival distribution and

discrete time Markov Process. Locally generated packets

gives the local packet information. End to end delay is

calculated by the sum of incoming relay traffic rate at

each of the next hop.[1] Figure.2 shows the activity

diagram for developing the cross layer framework.

Figure.3 shows the structure of Markov chain showing

successful transmission of the packets with 3 attempts.

As shown in figure successfully transmitted traffic rate

from one node should be equal to the sum of the

incoming relay traffic rate at each of the next-hop

neighbors of the node.The topology of the queueing

network depends on the routing protocol used which is

also elaborated in activity diagram also. The discrete

time Markov chain is made up of M+1 layers , where

each layer m(0<m<M) represents the state there are m

packets in the queue and M is the queue capacity. The

detailed knowledge of Discrete chain Markov Process is

essential while developing a cross layer. Before each

transmission , the packet in the queue is transferred from

the microcontroller to transreceiver. The time needed for

such transfer differs for various transreceivers, but it is

not negligible. Our Experiments with Network Simulator

2 suggest that the durations of loading time and after

radio transmission are constant and approximately 1.7

and 2.0 ms, respectively.




Figure 1: Architecture Diagram

Figure 2: Activity Diagram

End-to-End Delay Distribution:-

Figure 3: Structures of Markov chains are shown in (a) for

{xn} and (b) for {Yn}. The common structure of blocks{zn }

and{In} are shown in (c) and (d), respectively.[1]

With each hop modeled as a Geom/PH/1/M queue, the entire

network is considered as a queuing network. Nodes are

interrelated according to the traffic constraints. More

specifically, the successfully transmitted traffic rate from one

node should be equal to the sum of the incoming relay traffic

rate at each of the next-hop neighbors of the node. The

topology of the queuing network depends on the routing

protocol used. In this paper, we focus on the class of routing

protocols with which each node maintains a probabilistic

routing table for its neighbors, e.g., geographic routing

protocols [4].Nodes relay their packets to each of their neighbors according to a probability in their routing tables. By

first calculating the relaying traffic and the single hop delay

distribution for each pair of nodes, the end-to-end delay is

obtained using an iterative procedure.[1]

A. Constructing Markov Chain{Xn }

The discrete time Markov chain

{Xn} is made up of M+1 layers , where each layer m(0<m<M)

represents the state there are m packets in the queue and M is

the queue capacity. idle layer {In}and the communication

layers{Cn}m ,each of which consists of one or more states each of which consists of one or more states. The states and




the transitions among the states in each layer are determined

by the protocols used by each node and represent the

operations conducted by the nodes according to the protocols.

The idle layer{In}(m=0),represents the idle process, during

which the node does not have any packet to send and waits for new packets. The communication layers {Cn}m, (m>0)

represent the communication process in which packets are

transmitted

According to the MAC protocol

employed, and are respectively parameterized by the

following notations:

•PI and PC : the transition probability matrix among the states

in {In }and {Cn} ;

•αI and αC : the initial probability vector for {In}and

{Cn};

• tIS and tc

s : the probability vector from each state in{In} and {Cn} to complete the idle process and the transmission process

successfully;

•tcf : the probability vector from each state in to complete the

transmission process unsuccessfully;

the packet arrival probability vector for each state :2 ג and 1 ג•

in{In}and {Cn} . Each element in the vector is the probability

of a new packet arrival in a time unit when the process is in

the corresponding state.

Each communication layer {Cn }consists of Markov chain

blocks for each transmission attempt {Zn} , which is further

characterized by the transition probability matrix Pz , the initial probability vector αz, the success probability vector tz

s,

the failure probability vector tzf , and packet arrival probability

vector גz.

Accordingly, the transition probability matrix among the

states in a single layer {Cn} in {Xn} can be organized as rows

and columns of blocks

where the number of PZ blocks in PC is equal to x , i.e., the

maximum number of attempts for each packet transmission.

Similarly, the initial probability vector αc and the probability vectors tc

sand tcf to complete a layer in success and failure are

respectively organized as

αC = [ αZ 0 ……. 0 ] (1)

tcs = [tz

s tzs ……… tz

s ]T (2)

tcf = [0 0 ……tz

f ]T (3)

Note that since the idle layer does not have multiple attempts

like the communication layer does, there is no similar

organized internal pattern in the corresponding matrices and vectors for {In}. The states and the transitions related to {In}

and {Zn} depend on the MAC protocol employed. The

transition probability matrix Qx of the entire Markov chain

{Xn} can then be found according to transitions between

different states at each layer as explained next.[1]

For layer m ,1<m<M-1 , the queue is not full.

Whenever a packet arrives, the process transits to a higher

layer since the queue length increases. The probabilities of

such transitions are governed by the probability matrix

Au=(1 גc )T * Pc (4)

where is a properly dimensioned matrix containing all 1’s, and

* is the entrywise product operator.גc and Pc are parameterized

according to the MAC protocol. Note that element (v,v’) in Au represents the transition probability from the v th state in

previous layer to the v’ th state in the upper layer, and other

transition probability matrices in the following are defined the

similar way. The transition probability matrix at the same

level m ,1<m<M-1, is

As=(1גc)T*(tcαc) + (1-1גc)

T*Pc (5)

Where tc = tcs + tc

f is the probability vector from each layer to

complete the current communication process regardless of

success or failure. The first term in (5) captures the case where a locally generated packet arrives at the same time unit in

which a packet service is completed. The second term in (5) is

for the case where neither service completion nor new packet

arrival occurs during the time unit.

At layer m=M, the queue is full. Hence,

new arriving packets are directly dropped. Therefore, the

transition probability matrix in this layer is Au + As .When

there is no packet arrival and the current packet service is

completed, the Markov chain transits to one layer below.The

transition probability matrix from level m+1 to level m

,1<m<M-1 is Ad = (1-1גc)

T * tcαc (6)

The transition probabilities are similar when the idle layer is

involved as follows:

Au0= גIT * αC ( 7)

Ad0=( 1-1גc)T *tcαI (8)

As0 = ( 1-1גI)T * (PI + tI

SαI (9)

When a new packet arrives while there

is no packet in the system, the chain transits from the idle

layer to layer 1 according TO Au0 to in (8). When the service

is completed for the only packet in the system and no new

packet arrives, the chain transits from layer 1 to the idle layer

according to Ad0 in(8). Finally, the transition probabilities with

which the node stays in the idle layer are given in As0 in (9).

Using (4)–(9), the transition probability matrix Qxfor

the entire recurrent Markov chain {Xn} can be constructed as

follows:

Qx

Event Scheduler

To drive the execution of the simulation, to

process and schedule simulation events, NS makes use of

the concept of discrete event schedulers . In NS, network

components that simulate packet-handling delay or that

need timers use event schedulers. Figure 4 shows two network objects, each of it using an event scheduler. If an




network object issues an event, it has also to handle the

event later at scheduled time.

In NS, there are two different types of event schedulers –

real-time and non-real-time schedulers. There are three

implementations (List, Heap and Calendar) for non-real-

time schedulers; the default is Calendar.

Figure.4 The discrete event scheduler.[11]

Hardware Emulation

The real time scheduler (one of the two types of NS event

schedulers) is used for emulation. Emulation allow the

simulator to interact with a real live network NS is an OTcl

script interpreter with network simulation object libraries. But

NS in not only written in OTcl but also in C++. For efficiency

reasons, NS exploits a split-programming model. This is

because the developers of NS have found that separating the

data path implementation from the control path

implementation will reduce packet and event processing time.

Task such as low-level event processing and packet

forwarding requires high performance and are modified

infrequently, therefore the event scheduler and the basic network component objects in the data path are implemented

in a compiled language that is C++. [11]

NS2 provides users with an

executable command ns which takes on input argument, the

name of a Tcl simulation scripting file. Users are feeding the

name of a Tcl simulation script (which sets up a simulation) as

an input argument of an NS2 executable command ns. In most cases, a simulation trace file is created, and is used to plot

graph and/or to create animation.NS2 consists of two key

languages: C++ and Object-oriented Tool Command

Language (OTcl). While the C++ defines the internal

mechanism (i.e.,a backend) of the simulation objects, the OTcl

sets up simulation by assembling and configuring the objects

as well as scheduling discrete events (i.e., a frontend). The

C++ and the OTcl are linked together using TclCL. Mapped to

a C++ object, variables in the OTcl domains are sometimes

referred to as handles.

Conceptually, a handle (e.g., n as a

Node handle) is just a string (e.g.,_o10) in the OTcl domain,

and does not contain any functionality. Instead, the

functionality (e.g., receiving a packet) is defined in the

mapped C++ object (e.g., of class Connector). In the OTcl

domain, a handle acts as a frontend which interacts with users

and other OTcl objects. It may defines its own procedures and

variables to facilitate the interaction. Note that the member

procedures and variables in the OTcl domain are called

instance procedures instprocs) and instance variables

(instvars), respectively. Before proceeding further, the readers

are encouraged to learn C++ and OTcl languages. We refer the

readers to for the detail of C++, while a brief tutorial of Tcl

and OTcl tutorial are given in Appendices A.1 and A.2,

respectively. NS2 provides a large number of built-in C++

objects. It is advisable to use these C++ objects to set up a

simulation using a Tcl simulation script. However, advance

users may find these objects insufficient. They need to

develop their own C++ objects, and use a OTcl configuration

interface to put together these objects. After simulation, NS2

outputs either text-based or animation-based simulation

results. To interpret these results graphically and interactively,

tools such as NAM (Network AniMator) and XGraph are

used. To analyze a particular behavior of the network, users

can extract a relevant subset of text-based data and transform

it to a more conceivable presentation.[11]

Installation

NS2 is a free simulation tool, which can be

obtained from . It runs on various platforms including UNIX

(or Linux), Windows, and Mac systems.Being developed in

the Unix environment, with no surprise, NS2 has the

smoothest ride there, and so does its installation. Unless

otherwise specified,the discussion in this book is based on a

Cygwin (UNIX emulator) activated Windows system. NS2

source codes are distributed in two forms: the all-in-one suite

and the component-wise. With the all-in-one package, users

get all the required components along with some optional

components. [11]

The current all-in-one suite consists of the following main

components:

• NS release 2.30,

• Tcl/Tk release 8.4.13,

• OTcl release 1.12, and

• TclCL release 1.18.

and the following are the optional components:

• NAM release 1.12: NAM is an animation tool for viewing

network simulation traces and packet traces.

• Zlib version 1.2.3: This is the required library for NAM.




IV. Discussion and results

Figure 3(a): CSMA/CA Mac Protocol

Figure 3(b): Average and end to end delay for CSMA/CA

Mac protocol.

Figure 4(a): Graph of Throughput Vs Packetloss

Crosslayer protocol.

Figure 5(a): Comparison graph of Throughput Vs Packetloss Crosslayer and CSMA/CA Mac

Figure 3(b): Graph of Throughput Vs Packetloss For

CSMA/CA Mac protocol.

Figure 4(a): Crosslayer protocol

Figure 3(b): Average and end to end delay for Crosslayer

protocol.

Figure 5(b): Comparison graph of End to end delay Crosslayer and CSMA/CA Mac




The end-to-end delay distribution model has

been evaluated using NS2 to determine the single-hop and

multi hop delay distributions for the CSMA/CA MAC

protocol and the cross layer protocol . The computing

environment is a PC with a INTEL I3 working at 2.66

GHz and 4 GB RAM. Moreover, empirical experiments and NS2-based simulations have been conducted on our

WSN test bed to validate the results. The simulations are

conducted in the same PC environment. For the empirical

validations, The packet size is B. Each node generates

local traffic to be sent to sink according to a Poisson

distribution with rate . Our experiments with the NS2

suggest that it requires on the average 1.7 ms to transfer

each packet from the sender to the receiver. The

transmission power is set to 15 dBm for all the

experiments unless otherwise stated. In the experiments,

the single-hop delay and end-to-end delay are measured as

follows. When the source node generates a packet, it simultaneously sends an electric pulse to the destination

node through a pair of wires. The destination node starts a

timer when it receives the pulse and waits for the packet.

When the packet is received by the destination node, the

duration after the reception of the pulse is recorded as the

packet delay. This eliminates the need for synchronization

among all the nodes.

As shown in fig.3(a) a wireless topology

of nodes is implemented with CSMA/CA Mac protocol. A

packet transfer from sender to receiver is shown with the

shortest path possible with the CSMA/CA Mac protocol.

Algorithm finds the nearest node for transmitting the

packet in network. As shown in Fig.3(b) the graph of

throughput vs packetloss is drawn for CSMA/CA protocol.

Graph clearly shows that as packet loss is increased the

throughput is decreased. Fig.3(c) shows the average delay

for sending packet from sender to receiver as shown in the

network topology of module 2. Fig. also shows the average

end to end delay for sending packet from one end of

network to the other end, for CSMA/CA Mac

protocol.Fig.4(a) shows the network topology for Cross

layer protocol Fig. shows the packet transfer from sender

to receiver Sender nodes are shown by green and red color

while receiver nodes are shown by pink color. The average

delay for sending packet from sender to receiver is

calculated for cross layer protocol. Also average end to

end delay for sending packet from one end to other end of

network is calculated for cross layer protocol. As shown

in Fig.4(b) the graph of throughput vs packet loss is drawn

for Cross layer protocol. Graph clearly shows that as

packet loss is increased the throughput is decreased.

Fig.4(c) shows the average delay for sending packet from

sender to receiver as shown in the network topology of

module 2. Fig. also shows the average end to end delay for

sending packet from one end of network to the other end,

for Cross layer Mac protocol.

fig.5(a) shows the comparison of

CSMA and cross layer protocol for throughput vs packet

loss. as the red line indicates cross layer protocol and green

line shows the CSMA/CA Mac protocol. the graph clearly

shows that the packet loss is less in cross layer protocol as compared to CSMA/CA protocol. due to this throughput

is more for cross layer protocol as compared to CSMA

/CA Mac protocol.

The average delay for the CSMA/CA

protocol is 0.267msec considering transfer of 30 packets.

The End to end delay for CSMA/CA Mac protocol is

0.5112 msec. The average delay for Cross layer protocol

is 0.062 m sec considering transfer of 30 packets. The end

to end delay for Cross layer protocol is 0.11008 msec.

Following Table I shows the analytical results.

Table I

Sr.

No

.

Parameter No.of

Packets

CSMA Crosslayer

1. Average

Delay(msec)

30 0.267 0.5112

2. End-to-End

Delay(msec)

30 0.062 0.11008

3. Average

Delay(msec)

80 0.512 0.9843

4. End-to-End

Delay(msec)

80 0.342 0.29870

V. Conclusion and Future work

In this paper, an end-to-end analysis of the

communication delay is provided. Our model shows

comparatively stronger results for Cross layer protocol

than CSMA/CA Mac protocol as shown in Table I. A

Markov process is used to model the communication

process in network. Average and End to end delay for CSMA/CA protocol and Cross layer protocol is calculated.

The results show that the developed framework accurately

models the distribution of the end-to-end delay and

captures the heterogeneous effects of multi hop WSNs.

The developed framework can be used to find out the

Throughput and packet loss for the both CSMA/CA Mac

and Cross layer protocol. for WSNs In some applications, the traffic generated for the

physical event can be bur sty. For tractability, the bur sty




traffic pattern is not considered in this project. so in future

we can implement a system with bursty traffic. As future

work, we plan to analyze the delay for more MAC

protocols, such as BMAC [21], XMAC [3], using our

model. We also plan to extend the model to capture more

generic network topologies, and traffic types, such as periodic and bursty traffics. Moreover, other network

lifetime definitions will be investigated. We also plan

extend our model to proposals in IEEE 802.11e to reduce

these delays which allow a node to schedule a burst of

packets once they gainchannel access. Each node in now

modeled as a discrete time queue with interruptions.

VI. References

[1] Yunbo Wang, Member, IEEE, Mehmet C. Vuran,

Member, IEEE, and Steve Goddard, Member, IEEE

“Cross-Layer Analysis of the End-to-End Delay

Distribution in Wireless Sensor Networks.” IEEE/ACM

transactions on networking, vol. 20, no. 1, february 2012

[2] Omesh Tickoo and Biplab Sikdar, Member, IEEE

“Modeling Queueing and Channel Access Delay in

Unsaturated IEEE 802.11 Random Access MAC Based

Wireless Networks.” IEEE/ACM transactions on networking, vol. 16, no. 4, august 2008

[3] “Stochastic Analysis of Energy Consumption in

Wireless Sensor Networks.” Yunbo Wang, Mehmet C.

Vuran and Steve Goddard Department of Computer

Science and Engineering,University of Nebraska-Lincoln

[4] T. Abdelzaher, S. Prabh, and R. Kiran, “On real-time

capacity limits of multihop wireless sensor networks,” in

Proc. IEEE RTSS, Lisbon, Portugal, Dec. 2004, pp. 359–

370.

[5] K. Akkaya and M. Younis, “A survey on routing

protocols for wirelesssensor networks,” Ad Hoc Netw., vol.

3, no. 3, pp. 325–349, Sep. 2005.

[6] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “A

survey on wireless multimedia sensor networks,” Comput.

Netw. J., vol. 51, no. 4, pp. 921–960, Mar. 2007.

[7] I. F. Akyildiz,W. Su, Y. Sankarasubramaniam, and E.

Cayirci, “Wireless sensor networks: A survey,” Comput.

Netw. J., vol. 38, no. 4, pp. 393–422, Mar. 2002.

[8] G. Bianchi, “Performance analysis of the IEEE 802.11

distributed coordination function,” IEEE J. Sel. Areas

Commun., vol. 18, no. 3, pp.535–547, Mar. 2000.

[9] N. Bisnik and A. Abouzeid, “Queuing network models

for delay analysis of multihop wireless ad hoc networks,”

Ad Hoc Netw., vol. 7, no.1, pp. 79–97, Jan. 2009.

[10] A. Burchard, J. Liebeherr, and S. Patek, “A min-plus

calculus for end-to-end statistical service guarantees,”

IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 4105–4114,

Sep. 2006.

[11] Teerawat Issariyakul & EkramHossain”Introduction

to Network Simulator 2.”2009 Springer Science,Business

Media,

Ravindra J. Lawande 1

BE in Electronics from

Pune University,

pursuing ME in VLSI

and embedded form

PREC, loni, Pune

University, working as

an assistant professor at

PDVVPCOE,

Ahmednagar, Maharashtra ,India

His field of interest is

Wireless Comm. and

Microwave.

.

Abdul .H.Ansari 2

BE and ME from

S.S.G.MCE,Shegaon,

Amaravati University

has 16 years of teaching

Experience, presently working as Associate

professor at PREC,Loni

His field of interest is

Wireless Comm. and

Cognitive Radio.