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  • 1. 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 2 P.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 . lawande1 , 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.
  • 2. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2312 www.ijarcet.org 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.
  • 3. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2313 www.ijarcet.org 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
  • 4. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2314 www.ijarcet.org 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}; • tI S and tc s : the probability vector from each state in{In} and {Cn} to complete the idle process and the transmission process successfully; •tc f : the probability vector from each state in to complete the transmission process unsuccessfully; •‫ג‬ 1 and ‫ג‬ 2: the packet arrival probability vector for each state 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 tz f , 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 s and tc f to complete a layer in success and failure are respectively organized as αC = [ αZ 0 ……. 0 ] (1) tc s = [tz s tz s ……… tz s ]T (2) tc f = [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 = tc s + 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= ‫ג‬I T * αC ( 7) Ad0=( 1-1‫ג‬c)T *tcαI (8) As0 = ( 1-1‫ג‬I)T * (PI + tI S αI (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
  • 5. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2315 www.ijarcet.org 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.
  • 6. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2316 www.ijarcet.org 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
  • 7. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2317 www.ijarcet.org 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
  • 8. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013 2318 www.ijarcet.org 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.