Performance Analysis of Multicast Routing Protocols MAODV, ODMRP and ADMR for MANETs

Olimpjon Shurdi, Rozeta Miho, Bexhet Kamo, Vladi Koliçi, Alban Rakipi Department of Electronic and Telecommunication

Polytechnic University of Tirana Mother Teresa Square, Nr.4, Tirana, Albania

Email: oshurdi@fti.edu.al, rmiho@fti.edu.al, bkamo@fti.edu.al, vkolici@fti.edu.al, alban.rakipi@fti.edu.al

Abstract— A Mobile Ad hoc Network (MANET) is a collection of wireless mobile terminals that are able to dynamically form a temporary network without any aid from fixed infrastructure or centralized administration. In recent years, MANETs are continuing to attract the attention for their potential use in several fields such as military activities, rescue operations and time-critical applications. A very important and necessary issue for mobile ad hoc networks is to finding the root between source and destination that is a major technical challenge due to the dynamic topology of the network. Routing protocols for MANETs could be differ depending on the application and network architecture. The efficiency of the wireless link can be increased by multicasting through sending single copy of messages to all group members. Multicast transmission is a more effective mechanism when compared to unicasting in supporting group communication applications and hence is an important aspect of future network development. This paper evaluates the performance of three multicast routing protocols for MANETs, notably MAODV, ODMRP and ADMR. Different performance aspects are investigated including, throughput, link delay, transmission and control overhead.

Keywords: MANET; ADMR; ODMRP; MAODV; Uniform; Manhattan; Exhibition; Random Waypoint and Battlefield.

I. INTRODUCTION A Mobile Ad hoc Network (MANET) is a collection of

wireless mobile terminals that are able to dynamically form a temporary network without any aid from fixed infrastructure or centralized administration. In recent years, MANETs are continuing to attract the attention for their potential use in several fields. Mobility and the absence of any fixed infrastructure make MANET very attractive for mobility and rescue operations and time-critical applications.

Most prior work in ad hoc network routing has focused on routing of unicast packets, but a number of protocols for multicast routing have been proposed over the past few years as well [1, 2, 3], using a variety of basic routing algorithms and techniques. One of the main goals of this study is to re-visit the relative performance merits of the existing multicast routing protocols. The routing protocols selected for the present evaluation study include Adaptive Demand-Driven Multicast Routing (ADMR), On-Demand Multicast Routing Protocol (ODMRP), and Ad Hoc On-

Demand Distance Vector (MAODV). We chose to compare ADMR against MAODV and ODMRP because they have been well-documented and have been shown to perform well. In addition, all of these protocols contain a significant on-demand (reactive) component, but they differ in how reactive and proactive mechanisms are combined to make the complete protocol: ADMR uses source-based trees and does not utilize any periodic control packet transmissions, MAODV uses a shared group tree and uses periodic Hello messages for link break detection and periodic group leader floods for group information dissemination, and ODMRP uses a group forwarding mesh for packet forwarding and utilizes periodic flood-response cycles for multicast state creation and maintenance.

The structure of the paper is as follows. In Section II we give a short description of the reactive multicast routing protocols considered in this evaluation study. In Section III, we describe the simulation scenarios in the evaluation. In Section IV, we analyze the performance results. Finally, conclusions are given in Section V.

II. REACTIVE MULTICAST ROUTING PROTOCOLS Traditional routing protocols such as On-Demand

Multicast Routing Protocol (ODMRP) and Multicast Ad hoc On-demand Distance Vector (MAODV) are Reactive multicast routing protocols. Reactive routing that means discovers the route when needed. Reactive routing protocols are well suited for a large-scale, narrow-band MANET with moderate or low mobility. Below is a brief description of the protocols.

A. Multicast Ad-hoc On-demand Distance Vector (MAODV)

Multicast operation of Ad-hoc On-demand Distance Vector (MAODV) [5] is a reactive tree-based multicast routing protocol. Using MAODV, all nodes in the network maintain local connectivity by broadcasting “Hello” messages with TTL set to one. Every node maintains three tables, a Routing Table (RT), a Multicast Routing Table (MRT) and a Request Table. Every multicast group has a sequence number to indicate the freshness of the multicast routing information. Thus, one and only one group leader is elected to broadcast periodical GROUP HELLO messages throughout the MANET to maintain the sequence number.

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The group leader is by default the first node joining the group, but could also be another node when the first node leaves the group.

B. On-Demand Multicast Routing Protocol (ODMRP) On-Demand Multicast Routing Protocol (ODMRP) [4] is

a reactive mesh based multicast routing protocol. ODMRP is not only a multicast routing protocol, but also provides unicast routing capability. The source establishes and maintains group membership and multicast mesh on demand if it needs to send data packets to the multicast group. A set of nodes, which is called forwarding group, participate in forwarding data packets among group members. ODMRP uses a forwarding group concept for multicast packet transmission, in which each multicast group G is associated with a forwarding group (FG). Nodes in FG are in charge of forwarding multicast packets of group G. In a multicast group of ODMRP, the source manages the group membership, establishes and updates the multicast routes on demand.

C. Adaptive Demand-Driven Multicast Routing (ADMR) The Adaptive Demand Driven Multicast Routing

(ADMR) [6] is a source-based, on-demand multicast protocol. It was designed under the assumption that nodes in the network may move at any time, and that any packet may be lost due to factors such as packet collision, wireless interference, or signal attenuation due to distance. ADMR depends on periodic data flooding, and nodes’ up streams and down streams information during its operation. In creating the multicast tree, a source that wants to begin sending multicast traffic periodically floods the first multicast data packet to the entire network. A valid response comes from a node that wishes to join the multicast group. This protocol invokes a Keep-Alive method; during the delivery of multicast data an inter-packet time interval is included in the data packet header to notify downstream nodes of how often they should expect data packet.

III. SIMULATION DESCRIPTION In this section, we describe the simulation setup, scenarios, and performance metrics used in the evaluation of ADMR, MAODV and ODMRP.

1) Simulation Environment For simulations we have used QualNet 5.0 [13], which is

extended to include the Uniform, Manhattan, Exhibition, and Battlefield mobility models [9, 10, 11, 12] along with multicast protocols ADMR and MAODV. The ODMRP implementation provided in the QualNet 5.0 distribution was also used. The implementation scenario is depicted in the Fig. 1. Each simulation was run with 50 nodes, randomly placed over a square field whose length and width is 1000 meters. The multicast traffic was generated through three multicast groups, each consisting of 7 receivers. Each multicast source uses a Constant Bit Rate (CBR) flow, transmitting a 64 byte packet every 250 milliseconds. IEEE

802.11 MAC protocol, with free-space radio signal propagation, with a 2Mbps channel were used. Each simulation was run for 600 seconds and averaging was done by running 20 simulations for each data point.

Figure 1: Implementation Design

2) Mobility Models In order to study the performance of MANET protocols in

a simulation environment, it becomes essential that we employ mobility models that imitate close to how devices move in reality. Out of the several mobility models, in this work, we consider five mobility models that are designed to capture a wide range of mobility patterns for ad-hoc applications. These models are briefly described in the following subsections.

A. Uniform Each node starts at a random position and moves in a

random direction with a constant velocity. The speed of each node is chosen randomly between a minimum and maximum value. Whenever a node reaches a boundary of the simulated field, it bounces off and continues moving in a new direction. The Uniform model is based on work by Lee et al. [9] and is included in our study because each node’s movement is independent but with high temporal dependency.

B. Random Waypoint Mobility Model The Random Waypoint Mobility Model used by Johnson

[8] and Lee (Lee et al., 1999) [4] includes pause times between changes in direction and/or speed. A Mobile Node (MN) begins by staying in one location for a certain period of time (i.e., a pause time). Once this time expires, the MN chooses a random destination as well as a speed that is uniformly distributed between [0, MAXSPEED]. It then travels towards the newly chosen destination at the selected speed. Upon arrival, the MN takes another break before starting the process again.

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C. Manhattan Mobility Model In some mobile applications, the movement of mobile

nodes follows the mobility pattern similar to the road maps. The Manhattan mobility model [10] uses a grid road topology. In this mobility model, the mobile nodes move in horizontal or vertical direction on an urban map. Although this model provides flexibility for the nodes to change the direction, it imposes geographic restrictions on node mobility.

D. Exhibition Each node chooses a destination from among a fixed set

of exhibition centers and then moves toward that center with a fixed speed. Once a node is within a certain distance of the center it pauses for a given time and then chooses a new center. This model represents independent movement but with high node density.

E. Battlefield Each node follows a group leader by choosing a

destination close to where the leader is currently located and then moving to that destination. The group leader uses the Random Waypoint Model with a pause time of 10 seconds. As with the Exhibition model, each node maintains a minimum distance from the group leader. Each node adjusts its intended destination after every meter of movement, based on where its group leader is now located. The speed of all nodes is random between a minimum and maximum value.

3) Performance Metrics Protocol performance will be evaluated using the

following metrics, which are computed over the whole duration of the simulation:

• Throughput: The ratio of the number of packets received to the number of packets sent.

• Packet delay: The difference between the time when the packet is sent by the source and when it is received by the receiver.

• Transmission Overhead: It is the ratio of the number of data messages transmitted (originated or forwarded) to the number of data messages received.

• Control Overhead: The ratio of the number of control messages originated or forwarded over the combined total of data and control messages originated or forwarded.

IV. PERFORMANCE EVALUATION

A. MAODV MAODV attains very high throughput at the expense of

high transmission overhead (Fig. 2 and Fig. 3). In fact as we have seen during the simulations this values delimited when the speed of the mobile node increase. It is obvious that when the mobile node moves with greater speed there are

more chances for the link breakage and result in less packet delivery ratio.

From Fig. 2 Battlefield model results in a relatively low throughput for MAODV, which is 83% as compared to other mobility models whose throughput is more than 94%. The reason is that all packets are flooded. MAODV also establishes a lower bound on delay for each of the mobility models (Fig. 4). Delay is 60% higher for other mobility models than Battlefield and Exhibition because group members have a higher likelihood of being near the source (i.e. if the source is the group leader, following the same leader or visiting the same center). Delay is higher for Uniform and Manhattan models because nodes are likely to be both well connected and spread out in the entire field.

B. ODMRP For ODMRP [4], throughput (Fig. 6) depends on the

model and the number of link changes roughly predicts the ordering from worst-to-best. Throughput for Exhibition model is 23.5% higher than from Battlefield model, despite a similar number of link changes, because of its much lower reach ability. Throughput for the Uniform model is the only exception to this ordering which is 11% lower than Exhibition model and this can be explained by its lower node density. ODMRP is significantly better than MAODV because of its ability to achieve good throughput with much lower transmission overhead (Fig. 7). For ODMRP, approximately 2.15 packets are forwarded for every packet received. ODMRP could have very high throughput by increasing the join query rate, but then this becomes flooding at very high rates, with a corresponding increase in transmission overhead. For both transmission overhead and delay (Fig. 8), the ordering among models is the same as for MAODV. Delay for group-based mobility is 45% lower than the Uniform mobility model whose delay is the highest. The reason for this is it correlates well with node density, as Group based mobility results in group members having a higher likelihood of being near the source, which can be expected to reduce delay and transmission overhead.

Figure 2: Throughput for MAODV

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Figure 3: Transmission Overhead for MAODV

Figure 4: Packet delay for MAODV

Figure 5: Control Overhead for MAODV

Figure 6: Throughput for ODMRP

Figure 7: Transmission Overhead for ODMRP

Figure 8: Packet delay for ODMRP

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Figure 9: Traffic control for ODMRP

Figure 10: Throughput for ADMR

Figure 11: Transmission Overhead for ADMR

The increased efficiency of ODMRP results in added control overhead, which was absent in case of flooding (Fig. 8). The high values of overheads are due to the combination of low traffic rate (4 packets per second) and periodic flooding (once per 3 seconds). With higher traffic rates, the percentage of overhead becomes much lower. The ordering of models in this graph is again similar to that of node density, with ODMRP.

C. ADMR The most sophisticated of all the three protocols studied,

ADMR is able to maintain high throughput for nearly all of the mobility models (80 - 90%) even as speed increases (Fig. 10). This is due to two mechanisms in ADMR.

First, forwarding nodes are able to initiate local repair of the multicast tree when they determine that packet loss is occurring. Second, receivers experiencing high packet loss can ask ADMR to switch to flooding.

For some models, both MAODV and ODMRP are able to achieve higher throughput than ADMR at low speeds. This could indicate that local repair can be used more efficiently to recover from loss at low speed. The consequence of performing adaptive flooding is that this increases transmission overhead for ADMR when speed is increased (Fig. 11). As with ODMRP and MAODV, the relative performance of the mobility models correlates to node density for both transmission overhead and delay (Fig. 12). ADMR have slightly higher delay of 4% than ODMRP, this is due to the increased number of control messages in ADMR, which may lead to collisions and retransmissions at the MAC layer. Control overhead for ADMR may decrease as the speed increases, depending on the mobility model (Fig. 13).

Figure 12: Packet Delay for ADMR

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Figure 13: Traffic Control for ADMR

This is because ADMR switches to flooding, which decreases the amount of control traffic due to local repair and member adaptation to loss. This trend is not as evident with the group based mobility models because flooding in areas of high node density can lead to more collisions and hence more control traffic (when nodes try to recover from the resulting loss).

V. CONCLUSIONS In this work, performance of multicast routing protocols

namely MAODV, ODMRP and ADMR in different mobility models, which are named as Uniform, Manhattan, Exhibition, Random way point and Battlefield was studied. Regardless of the mobility model, ODMRP performance degrades as speed increases, whereas ADMR is able to maintain throughput greater than 80%. ADMR is able to maintain high throughput because (a) forwarding nodes are able to initiate local repair of the multicast tree and (b) receivers experiencing high packet loss can ask ADMR to switch to flooding. For both ODMRP and ADMR, the transmission overhead, control overhead, and delay vary according to the mobility model. Group-based mobility models, which lead to higher node density, result in a greater chance that multicast group members will be located near the source. This leads to a savings in transmission overhead and delay. High density also decreases control overhead for ODMRP, since messages JOIN REPLY travel fewer hops. However, for ADMR the control of overhead increases with density. This happens because ADMR switches to flooding more frequently whenever there is congestion of packets. The results in this work indicate that characterizing link variations and density fluctuations for any user movement is crucial towards understanding routing performance.

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