38

CHAPTER 3

OPTIMIZED ON-DEMAND MULTICAST ROUTING

PROTOCOL FOR MOBILE AD-HOC NETWORK (MANET)

3.1 INTRODUCTION

Mobile Ad-hoc Network (MANET) has attracted significant

attention in research over the last two decades. In spite of the challenges in

the routing protocol design, the scope of MANET is wide due to recent

technologies like Internet of Things (IoT). Applications like Underwater

Sensor Networks, Vehicular Ad-hoc Networks, Online gaming, Classroom

communication, Battlefield communications, etc., lead to a substantial

research focus on the routing protocol design for MANET. This chapter

explores On-Demand Multicast Routing Protocol (ODMRP) which is a

routing protocol for MANET and the scope for optimization is also analyzed.

One such optimization based on the control message is proposed and its

results are evaluated.

3.2 MULTICAST ROUTING IN MANET

Multicast communication refers to the single source data to be

transmitted to multiple receiving nodes. This is aided by forming a multicast

group and destination nodes are referred by a multicast id. A lot of research in

recent years has been concentrated on providing routing functionality in

multicast applications (Royer and Perkins 1999). In MANET, the success of

data transmission depends on the characteristics of a group of hosts in a

39

network. The majority applications of MANET includes rescue sites, class

rooms, conventions, emergency search, battlefields, etc., which demand

participants share information dynamically and thus multicast operations.

Therefore, multicast protocols play a major role in MANET. Multicast

protocols developed for static networks, such as Distance Vector Multicast

Routing Protocol (DVMRP) (Deering and Cheriton 1990), Multicast Open

Shortest Path First (MOSPF) (Moy 1994), Core Based Trees (CBT) (Ballardie

et al 1993) and Protocol Independent Multicast (PIM) (Deering et al 1999),

do not function very well in ad-hoc network environments because of their

continuous dynamic behaviour. One of the major drawbacks of the

above-mentioned multicast protocols is that they possess an inherently

volatile tree structure. This volatile tree structure obliges these types of

networks to continuously update their link status in response to topology

changes. Additionally, typical multicast trees usually require a link state or

distance vector global routing substructure that can result in significant packet

loss. Furthermore, continuous topology changes caused by the frequent

exchange of routing vectors or link state tables can also result in excessive

channel and processing overhead, which can significantly increase network

congestion. As a result, constraints related to bandwidth resources, power

consumption and host mobility make multicast protocol design particularly

challenging.

In response to these difficulties, several multicast routing protocols

have been proposed for use in wireless ad-hoc networks, including Ad-hoc

Multicast Routing Protocol (AMRoute) (Xie et al 2002), On-Demand

Multicast Routing Protocol (ODMRP) (Ho Bae et al 2000), Ad-hoc Multicast

Routing Protocol Utilizing Increasing Id-numberS (AMRIS) (Wu et al 1999),

Core Assisted Mesh Protocol (CAMP) (Garcia and Madruga 1999), Multicast

Ad-hoc On-Demand Distance Vector (MAODV) (Perkins 2008) and Adaptive

Demand-Driven Multicast Routing protocol (ADMR) (Jetcheva and Jhonson

40

2001). However, the critical disadvantage of these topological multicast

routing algorithms is that their data delivery strategies do not guarantee

efficient transmission in highly mobile environments such as vehicular ad-hoc

networks (VANET´s).

3.2.1 Multicast Route Establishment

Multicast protocols are meant for group-oriented computing. The

nodes are connected as multicast group members and this group is formed

dynamically. Any host can either join or leave the group. The host group can

have any number of members. For forwarding the data, the host need not be a

member of the group. The route is established through two phases, the route

discovery and data forward phases. Route discovery is done by sharing of

messages. Generally these messages are request and reply messages. The

request is broadcast by the source. The intermediate and the receiving nodes

will be the recipients of this request message. The response of the request is

given only by the multicast receivers. After sending the request, the source

node waits for the reply for some predetermined time. If there is no response,

it rebroadcasts the request by incrementing the broadcast id. The intermediate

nodes maintain a multicast table. They forward the request by broadcasting.

The reply is originated by the receiver node and thus the forward path is

established.

3.2.2 Example Protocols

Researchers have proposed a variety of multicast protocols namely,

AMRIS (Wu et al 1998), Ad-hoc multicast routing AMRoute (Bommaiah

et al 1998), the Core Assisted Mesh Protocol CAMP (Garcia and Madruga ,

1999), Multicast Ad-hoc On-demand Vector, (Royer and Perkins 1999),

On-demand Multicast Routing Protocol ODMRP (Gerla et al 2000) and

Differential Destination Multicast (DDM) (Ji and Corson 2001). The

41

challenges in building multicast protocols for MANET are the dynamic

environment, unpredictable network topology, limited bandwidth and limited

power. Taxonomy of the multicast protocols and the design features were

explored by Junhai et al (2008). The study concludes that all protocols have

their own advantages and disadvantages.

3.3 ON-DEMAND MULTICAST ROUTING PROTOCOL

(ODMRP)

On-Demand Multicast Routing Protocol (Gerla et al 1998) is a

protocol for routing multicast and unicast traffic through Ad-hoc wireless

mesh networks. ODMRP creates routes on demand, rather than proactively

creating routes. This suffers from a route acquisition delay, although it helps

reduce network traffic in general. To help reduce the problem of this delay,

some implementations send the first data packet along with the route

discovery packet. Due to the fact that some links may be asymmetric, the path

from one node to another is not necessarily the same as the reverse path of

these nodes.

3.3.1 Principles of Forwarding Group

ODMRP has a key concept called Forwarding Group Concept as

shown in Figure 3.1. In ODMRP, group membership and multicast routes are

established and updated by the source on demand. Similar to on-demand

unicast routing protocols, ODMRP has both a request phase and a reply

phase. When a multicast source sends packets, it uses a flooding strategy to

transmit a member advertising packet to all the members of the group. This

packet, called JOIN_DATA, which also carries the payload, is periodically

broadcast to the entire network to refresh the membership information and

42

update the routes. When a node receives a non-duplicate JOIN _ DATA, it

stores the upstream node ID into the routing table and rebroadcasts the packet.

When the JOIN_DATA packet reaches a multicast receiver, the receiver

creates and broadcasts a JOIN_TABLE to its neighbours. When a node

receives a JOIN_TABLE, it verifies that the next node id of one of the entries

matches its own id. If it does, the node realizes that it is located at an

intermediate point between the source and receiver and recognizes that it must

forward the packet. It then sets the Forwarding Group Flag (FG_FLAG) and

broadcasts its own JOIN_TABLE based on matched entries. The

JOIN_TABLE is thus propagated by each forwarding group member until it

reaches the multicast source via the shortest path. This process constructs

(or updates) the routes from sources to receivers and builds a mesh of nodes.

Figure 3.1 Group formation in ODMRP

43

3.3.2 Features of ODMRP

Another major feature of ODMRP is maintaining the Forward

Group on an On-Demand approach. A sender periodically floods control

messages (Join Request) only if it has data to send. Figure 3.2 depicts the

on-demand approach. All intermediate nodes set up a route to the sender

(Backward Learning). Receivers update their Member Tables when they

receive Join Requests from senders. While valid entries exist in Member

Table, Join Tables are broadcast to all neighbors periodically. Neighbors

which match the route set and refresh the FG_FLAG of FG nodes. Also they

create and forward Join Tables to their neighbors. All the Join Table

exchanges are confined within the “bubble". No explicit messages are

required to join/leave multicast group (or FG)

Figure 3.2 Join reply flow

44

Figure 3.3 Join table updating

An example of Join Table Forwarding is shown in Figure 3.3. An

intermediate node builds its own Join Table and forwards it only when any of

the received tables' Next Node field matches its own id.

3.4 PERFORMANCE ANALYSIS OF ON-DEMAND

MULTICAST ROUTING PROTOCOL

As highlighted in the internet draft (Yi et al 2002), the different

data rate in a network results in different transmission range. Also the path

life time is dependent on the transmission range (Wang 2005). This thrust on

transmission range (Nuevo 2003), reveals an effort of addressing this issue

in multicast protocols. This section is intended to analyze the transmission

range effects of ODMRP with more realistic approach. The analysis is

simulated in Network Simulator GloMoSim (Zeng et al 1998) and the

results are evaluated. Global Mobile Information System Simulator

(GloMoSim) is a scalable simulation environment for large wireless and

wire line communication networks. GloMoSim uses a parallel discrete event

simulation capability provided by Parsec. It simulates networks with upto

thousand nodes linked by a heterogeneous communications capability that

45

includes multicast, asymmetric communications using direct satellite

broadcasts, multihop wireless communications using ad hoc networking, and

traditional Internet Protocols.

3.4.1 Network Parameters

Analysis of impact of individual parameters of a network on the

routing performance is as essential as the focus on the design of routing

protocols. Over recent time, various MANET routing protocols have evolved.

Not all of them are capable of delivering adequate performance due to the

unique MANET characteristics. Thus, it is necessary to observe the behavior

of interaction of various network parameters and the routing protocols in

MANET. Also the depth of impact of the individual parameters needs to be

explored in order to conclude about the performance of the routing protocol.

3.4.1.1 Node mobility

The wireless topology is dynamic and unpredictable due to the

mobility variations of nodes in MANET. In the future, MANETs are expected

to be deployed in myriads of scenarios having complex node mobility and

connectivity dynamics (Bai et al 2003). The node mobility characteristics are

application specific. The average connected paths at every instant are

dependent on the mobility of the nodes which consequently affects the

performance of the routing algorithms. Thus the mobility analysis of a

protocol is essential. The effects of different mobility models on the

performance of mobile IP multicast protocols are evaluated for two mobility

metrics such as number of link changes and multicast agent density

(Xu et al 2009). In this chapter, the behavior of ODMRP with different

mobility values is analyzed.

46

3.4.1.2 Network size

Network size of a network refers to the number of network nodes.

Combining it as a ratio with geographical area covered by the network, it

refers to network density parameter. Network size as well as network density

are critical parameters for coordinating the network functionalities with

distributed control mechanism. The node density has a greater impact on the

routing performance of a routing protocol. A densely populated network has a

number of possible connections between any two nodes as high. Thus, a

sparsely connected network reveals poor performance. Beyond some limit of

network density, the case is reverse and it degrades the protocol performance.

3.4.2 Simulation Results

An analysis is carried out by enabling the ODMRP protocol in

GloMoSim. Initial configuration includes network size, mobility model,

source destination assignments, number of packets to be transmitted and the

multicast members. Simulation is performed for a number of 40 nodes under a

wide range of mobility (10-100m/sec). Packets of size 512B along with

802.11 MAC protocol is communicated. Different numbers of data packets at

the source (10, 15 and 20) are tested as different iterations. A front end

interactive interface is made using programming language C to extract the stat

file from simulator. Multiple iterations are carried out with different speed

values. Simulation supports a wide range of mobility. As the real time speed

of mobile devices is limited one typical speed of real time is highlighted.

The data observed for a single speed of 10m/sec is tabulated below in Table

3.1 as a sample.

47

Table 3.1 Simulated ODMRP metrics for 1000 m X 1000 m network

ItNo

Speedm/sec

NodesSeed

ValuePackets Collision

Throughput

Bits/sec

1

10S-15

R-18,29,325

S-20

R-3,3,39,7,1 129,129,129

10S-3

R-6,11,136

S-15

R-3,15,151,5,7 1753,8777,8777

10S-29

R-15,18,327

S-10

R-10,2,24,8,1 9102,87,87

Table 3.1 enumerates a sample iteration with three multicast

transmissions. Sources in all the three cases transmit number of packets of

data as 20,15 and 10 respectively. The throughput of each case has been

obtained with few collisions.

3.4.2.1 Result summary

The protocol is tested for the PDR, throughput and the number

of collisions. The inferences indicates that a smooth response on PDR and

throughput is feasible for the mobile device when it most within the speed

range of 10m/s to 40m/s. Table 3.2 summarizes average of the results. This

preliminary work on ODMRP provides an idea to fix the network parameters

while implementing our proposed algorithms.

Table 3.2 Summary of simulated ODMRP metrics

Terrain DimensionAverage PDR

%Average No. of

Collisions

AverageThroughput

(bits/sec)

1000 m X 1000 m 46 4 3427

48

3.5 OPTIMIZED ON DEMAND ROUTING PROTOCOL

(O-ODMRP)

With limited power, bandwidth and frequently changing topology

as well, the multicast protocols of MANET are unable to outperform. To

overcome these limitations, various optimization algorithms are proposed

towards each of these protocols. The optimization can be introduced in

several stages of data transmission like route discovery, route reply, data

transmissions, error handling etc., Performance metrics of ODMRP like PDR

can be preserved with improved and normalized packet overhead (Oh et al

2005) by dynamic refresh rate adaption. A work of improving the resilient

property of ODMRP under node failure was suggested by Pathirana and

Kwon (2007) which provides higher PDR with minimal overheads. PDR

improvement by using multicast paths technique in ODMRP was proved by

Begdillo et al (2007) using OPNET simulator. Suitability of ODMRP for

Underwater Acoustic Networks (UAN) was also proved (Bauer et al 2010).

3.5.1 Join Query Analysis

ODMRP works with the concept of Forwarding Group (FG)

concept and Join-Query (JQ) messages. The nodes receiving JQ will react

either by setting the FG flag and relaying the JQ or by generating Join-Reply

(JR). In original ODMRP when a neighbor node receives the JQ, it will check

for merely the duplication. The nodes that are not registering for membership

will also overhear this JQ. When registered members are facing any failure

problems, route failure may occur. By making use of the neighbors who have

not registered i.e., with Non-Forwarding Group (NFG) flag set status, we can

overcome this issue of failure. The promiscuous mode of the nodes enables

them to obtain control messages of the neighbors. This property can be

exploited to implement optimization based on control messages. From the

49

analysis of the JQ message, three optimization procedures are proposed in this

chapter. They are

Static threshold algorithm for non-forwarding to forwarding

mode change.

Dynamic threshold algorithm for non-forwarding to forwarding

mode change.

Power threshold algorithm to change forwarding to non-

forwarding.

3.5.2 Static Threshold Algorithm (Non-Forwarding to Forwarding)

This algorithm is executed only at the nodes which are not in the

multicast group. As per the algorithm these nodes will set a counter for a

particular sequence numbered JQ. Whenever they overhear a join query they

will just register, update the counter and discard the requests. When this

updated counter value exceeds the threshold which is initially fixed by the

user, the node will assume itself as a member of the multicast group and it

will start broadcasting the JQ. The threshold is called static because it is fixed

by the user and never changed later. In this research, the threshold is obtained

by an empirical analysis.

Figure 3.4 describes the above static algorithm of non-forwarding to

forwarding mode conversion.

50

Start

Initialize Counter C; (for counting JQs)

Initialize Threshold ; (maximum no. of JQs)

If not member

{

if JQ duplicate

Increment C;

If C>

Then set as member;

}

End;

Figure 3.4 Static threshold algorithm

3.5.3 Dynamic Threshold Algorithm (Non-Forwarding to

Forwarding)

Due to static algorithm, the probability of path resilience will

increase. Simultaneously there is a probability for the reduction of individual

node’s life time. This may also lead to the overall reduction of life time of the

network. Alternatively the dynamic algorithm proposed in this work considers

an adaptive service of contributing the non-grouping requests. Otherwise,

selfishness is integrated with the decision on the service of committing

non-forwarding to forwarding mode. This adaptive procedure will offer a

tradeoff between the PDR and lifetime of the node.

The methodology used is that the user uploads a small table of

thresholds with corresponding power limits in the cache memory of the nodes.

Whenever the node has join queries updated, it immediately checks the

51

thresholds. The thresholds are obtained from the cache with respect to the

current power range of the node. Figure 3.5 demonstrates the algorithm for

the above dynamic procedure.

Start

Initialize Counter C; (for counting JQs)

Initialize Thresholds 1 and 2;(maximum no. of JQs)

Fix power values;

If P1

If not member

{

if JQ duplicate

Increment C;

If C> 1

Then set as member;

}

If P2

If not member

{

if JQ duplicate

Increment C;

If C> 2

Then set as member;

}

End;

Figure 3.5 Dynamic threshold algorithm

52

3.5.4 Power Threshold Algorithm (Forwarding to Non-Forwarding)

The previous sections 3.5.2 and 3.5.3 considered the repeated JQs

overheard at the non-group nodes as an inference and there are no nodes in

the multicast group to support their request. Thus, the nodes not belonging to

the multicast group, volunteer in helping the request from those JQs. There is

another inference possible from the repeated occurrence of duplicate JQs at

the nodes of multicast group. The JQ to a node may be directly sent by the

source and forwarded by other neighbors too. Thus, this JQ occurrence will

reveal the strength of neighbors around a node. If a node has a dense neighbor

set, it can compromise its JQs hoping that the neighbor will support. This will

make the node to protect its energy under critical conditions. This principle

can be executed by the node whenever the repeated JQs are exceeding a limit.

The power threshold algorithm uses this methodology at the multicast group

nodes and makes them switch from forwarding to non-forwarding mode. For

applications with critical power handling requirements, the algorithm listed in

Figure 3.6 can be used.

Start

Initialize Counter C; (for counting JQs)

Initialize Threshold ; (maximum value of power)

If member

{ if JQ duplicate

Increment C;

If C>

Then set as non member; }

End;

Figure 3.6 Power threshold algorithm

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3.6 PERFORMANCE ANALYSIS OF O-ODMRP

To evaluate all the three proposed algorithms, the ODMRP is

initially simulated and its performance is analyzed under different set of

inputs. This provides a set of empirical data which are required for our

algorithms. By using these data the proposed algorithms are developed as a

new protocol called O-ODMRP. Their performance is analyzed and compared

with the ODMRP.

3.6.1 Network Scenario

As per the summary of results in 3.4.2.1, the number of nodes are

made more than 20 and the mobility range is chosen as 10-40m/s. The

simulation is carried out in GloMoSim network simulator. The node’s

position is considered to be random. The MAC layer protocol is IEEE 802.11

and the number of packets to be sent is 40 for two multicast member group

and 30 for one member group where there will be a single source sending

packet to a multicast member group. Each group consists of three

destinations. The receivers are set with bounded SNR. Nodes follows random

way point model. Random way point model is defined as the mobility pattern

in which the nodes position changes with normally distributed velocity and

direction. Also there exists pause time between the random walks. The

empirical study provides a set of input configuration parameters as listed in

Table 3.3.

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Table 3.3 Inputs in configuration file

Parameters ValuesTerrain dimension 1000*1000No of nodes 40Node input RandomMobility 10 – 40 m/secMobility type Random Way pointSimulation time 200sec

The algorithm is evaluated for the metrics of PDR and throughput

for different network conditions. The results summary obtained from the

empirical study pertaining to our algorithms are

The static threshold for JQ at non-group nodes is 80.

The average mobility range supported is 10-40m/s.

The average number nodes in the dimension of 1000X1000

supported is 10-100 nodes

The mapping of power and JQ limits for dynamic algorithm is

as listed in Table 3.4.

Table 3.4 Threshold table

Power in mw JQ Counter0-50 4050-100 50100-150 60150-200 70200-250 80>250 90

55

3.6.2 Simulation Results – Static Threshold Algorithm

During this simulation, a static threshold value of 80 is stored as an

added constant in the ODMRP routing protocol file of GloMoSim. A counter

variable is added in that routing file with initialization, updating and

finalization features. The simulation is repeated for five seed values and the

average of the results are recorded. Figures 3.7-3.9 illustrates the

observations. The comparison confirms PDR increase of 56% and throughput

increase of 125% in an average with O-ODMRP. Simultaneously an increase

in power consumption due to O-ODMRP for about 80% can be observed.

The power consume is a localized computation pertaining to the network

routing functions. Assumptions are made on the values of the transmission of

messages like Joint Query and thus the average power consumed is

calculated.

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

10 15 20 25 30 35 40

Mobility (m/s)

ODMRPO-ODMRP

Figure 3.7 Mobility vs PDR with static algorithm

56

2000

3000

4000

5000

6000

7000

8000

10 15 20 25 30 35 40

Mobility (m/s)

ODMRPO-ODMRP

Figure 3.8 Mobility vs throughput with static algorithm

0

100

200

300

400

10 15 20 25 30 35 40

Mobility (m/s)

ODMRPO-ODMRP

Figure 3.9 Mobility vs power consumed with static algorithm

57

3.6.3 Simulation Results – Dynamic Threshold Algorithm

Here a set of power values and corresponding thresholds as given in

Table 3.8 are stored in the cache memory of routing protocol. Similar to static

procedure the counter value is initialized. The updating module makes the

counter value change adaptively. The protocol is added with a function to

retrieve the current power status of the node. These changes in the protocol

are bundled as O-ODMRP dynamic and the results obtained in the simulator

are compared with the O-ODMRP static. Figures 3.10-3.12 explains the

behavior of dynamic O-ODMRP. The observation summarizes that, by this

dynamic algorithm there is about 3% reduction of both PDR and throughput

than static algorithm. Also the power consumption in dynamic algorithm is

reduced by about 29% from that of static algorithm.

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

10 15 20 25 30 35 40Mobility (m/s)

Dynamic algorithmStatic algorithm

Figure 3.10 Mobility vs PDR with static and dynamic algorithms

58

7200

7400

7600

7800

8000

10 15 20 25 30 35 40Mobility (m/s)

Dynamic…Static…

Figure 3.11 Mobility vs throughput with static and dynamic algorithms

200

250

300

350

10 15 20 25 30 35 40Mobility (m/s)

Dynamicalgorithm

Figure 3.12 Mobility vs power consumed with static and dynamicalgorithms

The entire simulation studies are observed under various mobility

values. The lower ranges are suitable for vehicular networks and thus may

confine with moving vehicles of defense. Whereas the higher range is

imagined for military services while in flight.

59

3.6.4 Simulation Results – Power Threshold Algorithm

This algorithm includes a threshold similar to that, which occurs in

static procedure; but the threshold is added with the multicast nodes

functionalities in the routing protocol. Like the other two procedures, a

counter is initialized, updated and finalized. The protocol includes a function

which changes the status of the node from forwarding mode to non-

forwarding mode if the threshold exceeds. The average power consumption is

analyzed and plotted against that of ODMRP, as illustrated by Figures 3.13-

3.15. The interesting observation is that the PDR and throughput remains

same at reduced power consumption of about 2%. Thus, energy conservation

applications can make use of the power threshold algorithm for multicast

routing.

0.70.720.740.760.78

0.80.820.840.860.88

0.9

10 15 20 25 30 35 40Mobility (m/s)

Without PowerSaving algorithmWith PowerSaving algorithm

Figure 3.13 Mobility vs PDR with and without power threshold

algorithm

60

5000

5500

6000

6500

7000

10 15 20 25 30 35 40Mobility (m/s)

Without PowerSaving algorithmWith PowerSaving algorithm

Figure 3.14 Mobility vs throughput with and without power threshold

algorithm

90

95

100

105

110

10 15 20 25 30 35 40

Mobility (m/s)

Without PowerSaving algorithmWith Power Savingalgorithm

Figure 3.15 Mobility vs power consumed with and without power

threshold algorithms

61

3.7 SUMMARY

A through experiment of ODMRP protocol has been carried out

and the scope of performance improvements has been analyzed. The adoptive

approaches of changing forwarding mode to non-forwarding mode and

non-forwarding mode to forwarding mode have been verified. The result

benefits out of forwarding to non-forwarding is mainly pertaining to the self

power of the node. Non-forwarding to forwarding provides benefits like

increase in PDR and throughput with reduction in power as well. The graphs

obtained confirm the real time speed suitability of the proposed algorithm.

The inferences helped in designing base protocol for our minimal

network coding algorithms. The various choices of enhancements are deeply

observed and an O-ODMRP which uses static threshold algorithm is

finalized. Thus, the minimal network coding algorithms proposed in the later

chapters use the base protocol as static threshold based O-ODMRP.