Simulation Study of four Routing Protocols in MANET using Realistic Parameter
Praveen Goyal
SKS Institute of Technology & Science, Indore
Rau, near IIM Indore, Pithampur bypass road, Indore, MP 453 331
()
Abstract
Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. Efficient routing protocols can provide significant benefits to mobile ad hoc networks, in terms of both performance and reliability. Many routing protocols for such networks have been proposed so far. The scope of this paper to test routing performance of four different routing protocols AODV, OLSR, FSR and LAR in variable pause times and node density in large-scale networks. We have used QualNet Simulator 4.0 from Scalable Networks to perform the simulations. This paper describes the different routing protocols and the experiment setup and finally presents and discusses the results
Keywords: Mobile Ad hoc Networks, QualNet, routing protocols, Mobility Models.
I. Introduction
Mobile ad hoc networks (MANETs) [1] [5] are collections of mobile nodes, dynamically forming a temporary network without pre-existing network infrastructure or centralized administration. These nodes can be arbitrarily located and are free to move randomly at any given time, thus allowing network topology and interconnections between nodes to change rapidly and unpredictably. MANET is likely to be use in many practical applications, including personal area networks, home area networking, and military environments, and so on recent advances in wireless technology have enhanced the feasibility and functionality of wireless mobile ad hoc networks (MANETs). There has been significant research activity over the past 10 years into performance of such networks with the view to develop more efficient and robust routing protocols. In order for ad hoc networks to operate as efficiently as possible, appropriate on demand routing protocols have to be incorporated,
To find efficient routes from source to destination, taking into consideration the node mobility. Mobility affects ongoing transmissions, since a mobile node that receives and forwards packets may move out of range. As a result, links fail over time. In such cases a new route must be established. Thus, a quick route recovery procedure should be one of the main characteristics of a routing protocol. Our goal is to carry out a systematic performance study of four routing protocols for high density ad hoc networks: the Ad Hoc On-Demand Distance Vector protocol (AODV), Optimized Link State Routing (OLSR), Fisheye State Routing (FSR) and Location-Aided Routing (LAR).
The rest of the article is organized as follows: In the following section, we briefly review the AODV, OLSR, FSR and LAR protocols. We present a detailed critique of the four protocols, focusing on the differences in their dynamic behaviors that can lead to performance differences. We describe the simulation environment. We present the simulation results, followed by their interpretations. We finally draw conclusion.
II. Description of the protocols
1. AODV
AODV [1] [3] [4] [5] routing protocol is based on DSDV and DSR algorithm. It uses the periodic beaconing and sequence numbering procedure of DSDV and a similar route discovery procedure as in DSR. However, there are two major differences between DSR and AODV. The most distinguishing difference is that in DSR each packet carries full routing information, whereas in AODV the packets carry the destination address. This means that AODV has potentially less routing overheads than DSR. The other difference is that the route replies in DSR carry the address of every node along the route, whereas in AODV the route replies only carry the destination IP address and the sequence number.
2. OLSR
OLSR [12] [5] is a point-to-point routing protocol based on the traditional link-state algorithm. In this strategy, each node maintains topology information about the network by periodically exchanging link-state messages. The novelty of OLSR is that it minimizes the size of each control message and the number of rebroadcasting nodes during each rout hello messages, each node selects a subset of one hop neighbors, which covers all of its two hop Neighbors. OLSR [19] is based on the following mechanisms:
• Neighbor sensing based on periodic exchange of HELLO messages.
• Efficient flooding of control traffic using the concept of multipoint relays.
• Computation of an optimal route using the shortest-path algorithm.
3. FSR
FSR [1] [14] [15] [16] is an implicit hierarchical routing protocol. It uses the “fisheye” technique proposed by Kleinrock and Stevens, the eye of a fish captures with high detail the pixels near the focal point. The detail decreases as the distance from the focal point increases. In routing, the fisheye approach translates to maintaining accurate distance and path quality information about the immediate neighborhood of a node, with progressively less detail as the distance increases. The FSR concept originates from Global State Routing (GSR).GSR can be viewed as a special case of FSR, in which there is only one fisheye scope level. As a result, the entire topology table is exchanged among neighbors. Clearly, this consumes a considerable amount of bandwidth when network size becomes large. The link state packets are exchanged periodically instead of event driven. Through updating link state information with different frequencies depending on the scope distance, FSR scales well to large network size and keeps overhead low without compromising route computation accuracy when the destination is near.
4. LAR
LAR [1] [17] is based on flooding algorithms (such as DSR). However, LAR attempts to reduce the routing overheads present in the traditional flooding algorithm by using location information. This protocol assumes that each node knows its location through a GPS. Two different LAR scheme were proposed in [18], the first scheme calculates a request zone which defines a boundary where the route request packets can travel to reach the required destination. The second method stores the coordinates of the destination in the route request packets. These packets can only travels in the direction were the relative distance to the destination becomes smaller as they travel from one hop to another. Both methods limit the control overhead \transmitted through the network and hence conserve bandwidth. They will also determine the shortest path (in most cases) to the destination, since the route request packets travel away from the source and towards the destination.
III Related Works
Young Bae Ko and Nitin H Vaidya in 1998 [18] first discuss the LAR (Location Aided Routing) paper describe the architecture of the LAR also describe the working of the protocol with an example. Guangyu Pei, Mario Gerla, Tsu Wei Chen in 2000 present the FSR[17] (Fisheye State Routing) which provides an efficient and more desirable for mobile networks where mobility is high and the bandwidth is low, scalable solution for wireless, MANETs. Charles E. Perkins Elizabeth M Royer in 2003 [3] imposed by worst case route establishment latency as determined by the network diameter, AODV is an excellent choice for Ad-hoc network. T. Clausen, P. Jacquet in 2003 [12] defines OLSR (Optimized Link State Routing) the key concept used in the protocol is that of multipoint relays (MPRs). MPRs are selected nodes which forward broadcast messages during the flooding process. [13]Ioannis Broustis, Gentian Jakllari, Thomas Repantis, and Mart Molle in 2004 discuss the performance of common MANET routing protocol under realistic scenarios protocols include AODV, DSR, TORA and LAR. John Novatnack, Lloyd Greenwald, and Harpreet Arora in [7] 2005 Evaluating Ad hoc Routing Protocols With Respect to Quality of Service include OLSR, DSR, AODV which compare how each protocol affects quality of service is important to designing a reliable and robust QoS framework.
IV Simulation Environment
The simulation work is done in Qualnet wireless network simulator version 4.0. Mobility model used is Random Way Point (RWP). In this model a Mobile node is initially placed in a random location in the simulation area, and then moved in a anomaly chosen direction between [0, 2] at a random speed between [SpeedMin, SpeedMax]. The movement proceeds for a specific amount of time or distance, and the process is repeated a predetermined number of times. We chose Min speed = 0 m/s, Max speed = 10m/s, and pause time = vary and number of nodes = very. All the simulation works was carried out using four routing protocols. Network traffic is provided by using Constant Bit Rate (CBR) sources. A CBR traffic source provides a constant stream of packets throughout the whole simulation.
IV.1 Efficiency Metrics Used
Throughput It is the measure of the number of packets successfully transmitted to their final destination per unit time. It is the ratio between the numbers of sent packets vs. received packets.
Avg End to END Delay It signifies the average time taken by packets to reach one end to another end (Source to Destination).
Avg Jitter Effect It signifies the Packets from the source will reach the destination with different delays. A packet's delay varies with its position in the queues of the routers along the path between source and destination and this position can vary unpredictably.
Packet Delivery Ratio (PDR) It is the packet delivery ratio in this simulation is defined as the ratio between the number of packets sent by constant bit rate sources (CBR, “application layer”) and the number of received packets by the CBR sink at destination.
The control parameters we used in our simulation experiments were very the pause time and number of nodes with respect of four routing protocols.
In the first simulation, the pause time have varied from 0s to 100s with a size of 30 nodes. Other parameters such as simulation time and outgoing end time remain constant
In second simulation, the number of nodes has varied from 10 to 60 with constant 40s pause time. Other parameters such as simulation time and outgoing end time remain constant.
The network terrain size was fixed for 1,500 * 1,500 meters. The simulation time was 180 seconds for all the experiments
V Simulation Results
In this section, we present our simulation efforts to evaluate the routing protocols.
V [1] Effects of Varying Pause Time
In the first simulation, the pause time have varied. Other parameters such as simulation time and outgoing end time remain constant.
Figure 1 Comparison of throughput of routing protocols in constant node density by varying Pause Time.
Figure 2 Comparison of End to End Delay of routing protocols in constant node density by varying Pause Time.
Figure 3 Comparison of Avg. Jitter Effect of routing protocols in constant node density by varying Pause Time.
Figure 4 Comparison of Packet Delivery Ratio of routing protocols in constant node density by varying Pause Time.
V [2] Effects of Varying Number of Nodes
In second simulation, the number of nodes has varied from 10 to 60 with constant 40s pause time. Other parameters such as simulation time and outgoing end time remain constant.
Figure 5 Comparison of throughput of routing protocols in constant pause time by varying Number of Nodes.
Figure 6 Comparison of End to End Delay of routing protocols in constant pause time by varying Number of Nodes.
Figure 7 Comparison of Avg. Jitter Effect of routing protocols in constant pause time by varying Number of Nodes.
Figure 8 Comparison of Packet Delivery Ratio of routing protocols in constant pause time by varying Number of Nodes.
VI Conclusions and Future Work
We assume that the summery for all simulation result therefore, 1 for Very good, 2 for High, 3 for Middle, 4 for Low.
TABLE I
SUMMARY OF THE SIMULATION RESULTS (Change in Pause Time)
Metrics / AODV / OLSR / FSR / LARThroughput / 2 / 3 / 4 / 1
E to E Delay / 3 / 1 / 4 / 2
Avg. Jitter / 1 / 3 / 4 / 2
PDR / 2 / 3 / 4 / 1
Sum / 8 / 10 / 16 / 6
Statically in the above table when we change the pause time also change in the all four routing protocols so that LAR shows best performance in all metrics and OLSR shows worst performance and AODV and FSR shows relative result.
TABLE II
SUMMARY OF THE SIMULATION RESULTS (Change in Number of Nodes)
Metrics / AODV / OLSR / FSR / LARThroughput / 2 / 3 / 4 / 1
E to E Delay / 3 / 1 / 4 / 2
Avg. Jitter / 1 / 3 / 4 / 2
PDR / 2 / 3 / 4 / 1
Sum / 8 / 10 / 16 / 6
Statically in the above table when we change the pause time also change in the all four routing protocols so that LAR shows best performance in all metrics and FSR shows worst performance and AODV and OLSR shows relative result. We conclude from Table1 and Table2 LAR shows the best result in over all scenarios.
As part of our future work we simulate Routing protocol by varying pause time and check its performance. Also check the performance of Test other protocols in the hybrid situation. The implementations of different routing protocols in QualNet have to be adapted to use them in hybrid simulations. Development of a new routing protocol specialized for hybrid networks.
References
[1]. Imrich Chlamtac, Marco Conti, Jennifer J.N.Liu “Mobile ad hoc networking: imperatives and challenges”, 2003, Ad Hoc Networks.
[2]. Layuan, Li Chunlin, Yaun Peiyan “Performance evaluation and simulation of routing protocols in ad hoc networks”, February 2007, Computer Communication.
[3]. Charles E. Perkins, Elizabeth M. Belding-Royer, Ian D. Chakeres, “AdHoc on Demand Distance Vector (AODV) routing, Mobile Ad Hoc Networking Working Group” 19 October 2003, Internet-Draft.
[4]. Charles E. Perkins, Elizabeth M. Belding-Royer, S. Das, “AdHoc on Demand Distance Vector (AODV) routing, Mobile Ad Hoc Networking Working Group” July 2003, Internet-Draft.
[5]. Arun Kumar B.R., Lokanatha C. Reddy, Prakash S. Herimath, “Performance Comparison of Wireless Mobile Ad-Hoc Network Routing Protocols” June 2008, International Journal of Computer Science and Network Security.
[6]. E. Ahvar, and M. Fathy, “Performance Evaluation of Routing Protocols for High Density Ad Hoc Networks based on Qos by GlomoSim Simulator” 23 August 2007, Proceeding of World Academy of Science, Engineering And Technology.
[7]. John Novatnack, Lloyd Greenwald, and Harpreet Arora “Evaluating Ad hoc Routing Protocols With Respect to Quality of Service” 2005, IEEE.
[8]. A.A. Pirzada, C. McDonald, and A. Datta, ,“Performance Comparison of Trust Based Reactive Routing Protocols”, 2006 IEEE Transactions on Mobile Computing Vol. 5 No.6 pp. 695-710.
[9]. S Gowrishankar, T G Basavaraju and S. K. Sarkar, “Effect of Random Mobility Models Pattern in Mobile Ad hoc Networks”, 2007 IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.6, pp. 160-164.
[10]. D. B. Johnson, D. A. Maltz, and Y.C. Hu, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks”, 2005 IETF Draft -MANET working group.
[11]. Geetha Jayakumar, Gopinath Ganapathy, “Performance Comparison of Mobile Ad hoc Network Routing Protocol”, November 2007, IJCSNS International Journal of Computer Science and Network Security.
[12]. T. Clausen and P. Jacquet. “Optimized link state routing protocol”, RFC 3626, October 2003.
[13]. Ioannis Broustis, Gentian Jakllari, Thomas Repantis, Mart Molle, “A Performance Comparison of Routing Protocols for Large-Scale Wireless Mobile Ad Hoc Networks”.
[14]. Erika Johansson, Katarina Persson, Mattias Skold and Ulf Sterner, “An Analysis of the Fisheye Routing Technique in Highly Mobile Ad Hoc Networks”, 2004, IEEE.
[15]. Ting-Hung Chiu, Shyh-In Hwang,”Efficient Fisheye State Routing Protocol using Virtual Grid in High-Density Ad-Hoc Networks”, 2006, ICACT.
[16]. Mario Gerla, Xiaoyan Hong, Guangyu Pei, “Fisheye State Routing Protocol (FSR) for Ad Hoc Networks” December 2002, IETF MANET Working Group, INTERNET DRAFT.
[17]. Young-Bae Ko and Nitin H. Vaidya Location-Aided Routing (LAR) in Mobile Ad Hoc Networks.
[18]. Ljubica Blazevic, Member, IEEE, Jean-Yves Le Boudec, Fellow, IEEE, and Silvia Giordano, Member, IEEE, “A Location-Based Routing Method for Mobile Ad Hoc Networks” 2005 IEEE.
[19]. Thomas Heide Clausen, Gitte Hansen, Lars Christensen Gerd Behrmann, “The Optimized Link State Routing Protocol, Evaluation through Experiments and Simulation”.