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Performance Studies of MANET Routing Protocols in the Presence of

Different Broadcast Route Discovery Strategies

Dr. Natarajan Meghanathan

Department of Computer Science

Jackson State UniversityJackson, MS 39217

Email: [email protected]

ABSTRACT

Simulation studies for the Mobile Ad hoc NETwork (MANET) routing protocols have so

far employed flooding as the default mechanism of route discovery. During flooding,each node broadcasts the packet exactly once, causing the broadcast storm problem [1].

Several efficient broadcasting strategies [1][2] that reduce the number of retransmitted

route query packets and the number of retransmitting nodes have been proposed in theliterature. These include the probability-based, area-based and neighbor-knowledge

based methods to reduce the retransmission overhead. Our contribution in this paper is anns-2 simulation based analysis on the impact of employing these broadcasting strategies

for route discovery on the hop count and stability of routes. We use the minimum-hop

based Dynamic Source Routing (DSR) protocol [3] and the stability-based Flow-Oriented Routing Protocol (FORP) [4] as the routing protocols for our analysis. We

compare the hop count and stability of DSR and FORP routes determined under

conditions that guarantee at least 92-95% success in route discoveries and simultaneouslyminimize the number of retransmissions and retransmitting nodes.

Keywords: Broadcasting, Routing, Stability, Hop count, Mobile Ad Hoc

Networks

1 INTRODUCTIONA Mobile Ad hoc NETwork (MANET) is a

dynamic distributed system of autonomously moving

wireless nodes (such as laptops, personal digitalassistants, etc) and lacks a fixed infrastructure. The

network has limited bandwidth as the wireless

medium is shared and is prone to transmissioninterference. Nodes are battery-powered and

operated with a limited transmission range. As a

result, routes in MANETs are often multi-hop in

nature and have to be discovered by the nodesthemselves. There is no centralized administration

like in cellular networks. Several unicast and

multicast MANET routing protocols have beenproposed in the literature. The route discovery could

be either proactive or reactive. In the proactive

approach, nodes determine and maintain routes for

every possible source-destination pair, irrespective oftheir requirement. Reactive or on-demand MANETrouting protocols determine a route only when

required. It has been observed [5][6] that with adynamically changing network topology where route

accuracy and routing overhead are crucial, on-

demand routing protocols are to be preferred over the

proactive protocols. We will focus only on on-

demand routing for the rest of this paper.Currently, all the on-demand MANET routing

protocols employ a simple form of broadcasting

called flooding to discover the routes. Whenever a

source node has data to send to a destination node,

but does not have the route to the same, it will

initiate a broadcast route-query process. In the case

of flooding, the source node broadcasts a Route-Request-Query (RREQ) packet to its neighbors. Each

node in the network will broadcast this RREQ packet

exactly once when they see it the first time. Thedestination node receives the RREQ packets along

several paths, chooses the best route according to the

route selection principles of the particular routing

protocol and notifies the source node about theselected route using a Route-Reply (RREP) packet.

Flooding is a very expensive process with respect

to the bandwidth and energy usage. With resource-constrained environments like those of MANETs,

employing flooding for on-demand route discovery

will be very costly. Flooding also introduces lot of

redundancy in the packet retransmission process. In[1], it has been observed that with flooding, when anode receives a packet for the first time, at least 39%

of the nodes neighborhood would have also receivedthe message simultaneously and on average only

41% of additional area could be covered with a

rebroadcast. In general, when a node rebroadcasts a

message after hearing it k times, the expected

additional coverage decreases exponentially withincreasing values of k [1]. These observations

motivated researchers to introduce several efficient


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broadcasting strategies that will minimize thenumber of redundant retransmissions and at the same

time maximize the chances of the broadcasted

message reaching all the nodes in the network.The techniques for efficient broadcasting can be

grouped into three families [1][2]: probability-based

methods, area-based methods and the neighbor

knowledge-based methods. In probability-basedmethods, each node is assigned a probability for

retransmission. In area-based methods, a common

transmission range is assumed and a node willrebroadcast if only sufficient new area can be

covered with the retransmission. In neighbor-

knowledge based methods, each node stores

neighborhood state information and uses it to decidewhether to retransmit or not. One or more

broadcasting techniques have been proposed under

each of the above three families. The objective of allthese broadcasting techniques is to minimize the

number of retransmitted messages and the number of

nodes retransmitting the message. More information

on the different broadcasting techniques can be

found in Section 3.The performance of the different efficient

broadcasting techniques under different conditions of

topology changes and offered broadcast traffic hasbeen studied in [2]. As the number of retransmitting

nodes and the retransmitted messages get reduced

when using these broadcasting techniques for RREQ

propagation, the quality of the routes chosen may bedifferent compared to those routes discovered using

simple flooding. This formed the motivation for us toimplement these broadcasting techniques and use

them for route discovery in on-demand MANET

routing protocols.

On-demand MANET routing protocols can be

classified into two broad categories [7]: minimum-weight based routing protocols and stability-oriented

routing protocols. The Dynamic Source Routing

(DSR) protocol [3] is a well-known minimum-weight based protocol that selects routes with the

minimum hop count. The Flow-oriented Routing

Protocol (FORP) [4] was observed to discover the

most stable routes within the class of stable pathrouting protocols [8]. The stability of routes selected

by a routing protocol is quantified in terms of the

number of route transitions incurred by the protocolfor a source-destination (s-d) session. More

information on DSR and FORP is provided in

Section 2.

In this paper, we implement the probability- based method, the distance-based technique (area-based method), the Multi-Point Relaying (MPR) and

the Minimum Connected Dominating Set (MCDS)

based techniques (neighbor-knowledge basedmethod) as the route discovery strategies for DSR

and FORP and study the impact of these

broadcasting techniques on the quality of routeschosen by the two routing protocols. We specifically

study the impact on two principal routing metrics,viz., the stability and hop count. We compare the

stability and hop count of DSR and FORP routes

chosen with these broadcasting techniques with thosediscovered using flooding. Flooding helps to

discover the minimum hop routes for DSR and the

most stable routes for FORP. But, these efficient

broadcasting techniques may not yield the minimumhop routes for DSR or the most stable routes for

FORP.

The rest of the paper is organized as follows: InSection 2, we briefly discuss the DSR and FORP

protocols. Section 3 discusses the different

broadcasting techniques that have been published in

the literature. Section 4 describes the simulationenvironment, illustrates the results and interprets

them. Section 5 concludes the paper. Note that we

use the words route and path, message andpacket, rebroadcast and retransmit

interchangeably in this paper.

2 REVIEW OF MANET ROUTINGPROTOCOLS

In this section, we briefly review the minimum-

hop based Dynamic Source Routing (DSR) protocol[3] and the stability-based Flow-Oriented Routing

Protocol (FORP) [4] the two protocols we use for

our simulation analysis.

2.1 Dynamic Source Routing (DSR) ProtocolThe unique feature of DSR [3] is source routing:

data packets carry the route from the source to the

destination in the packet header. As a result,

intermediate nodes do not need to store up-to-date

routing information. This avoids the need for beaconcontrol neighbor detection packets that are used in

the stability-oriented routing protocols. Route

discovery is by means of the broadcast query-replycycle. A source nodes wishing to send a data packet

to a destination d, broadcasts a Route-Request

(RREQ) packet throughout the network. The RREQ

packet reaching a node contains the list ofintermediate nodes through which it has propagated

from the source node. After receiving the first RREQ

packet, the destination node waits for a short timeperiod for any more RREQ packets and then chooses

a path with the minimum hop count and sends a

Route-Reply Packet (RREP) along the selected path.

If any RREQ is received along a path whose hopcount is lower than the one on which the RREP wassent, another RREP would be sent on the latest

minimum hop path discovered.

2.2 Flow-Oriented Routing ProtocolFORP [4] utilizes the mobility and location

information of nodes to approximately predict the


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Link Expiration Time (LET) for each wireless link.FORP selects the route with the maximum Route

Expiration Time (RET), which is the minimum of the

LET values of the constituent links of the route. Eachnode periodically sends a beacon control message to

its neighbors and the message includes the current

position of the nodes, velocity, the direction of

movement and the transmission ranges. Each node isassumed to be able to predict the LET values of each

of its links with the neighboring nodes based on the

information collected using beacon packets. FORPassumes the availability of location identifying

techniques like GPS (Global Positioning System) [9]

and also assumes that the clocks across all nodes are

synchronized.Given the motion parameters of two neighboring

nodes, the duration of time the two nodes will remain

neighbors can be predicted as follows: Let two nodesi andjbe within the transmission range of each other.

Let (xi, yi) and (xj, yj) be the co-ordinates of the

mobile hosts i and j respectively. Let vi, vj be the

velocities and i, j, where (0 i, j< 2) indicate

the direction of motion of nodes i andj respectively.The amount of time the two nodes i and j will stay

connected,Di-j, can be predicted as follows:

Dab cd a c r ad bc

a ci j =

+ + +

+

( ) ( ) ( )2 2 2 2

2 2

where,a = vi cosi vj cosj; b = xi xj; c = vi sini vjsinj; d = yi yj

Route discovery is accomplished using the broadcast query-reply cycle with RREQ packets

propagating from the source node s to the destination

node don several paths. The information recorded inthis case by a node i receiving a RREQ messagefrom a nodej is the predicted LET of the linki-j. The

destination d will receive several RREQ messages

with the predicted LETs in the paths traversed beinglisted. Thes-dpath that has the maximum predicted

RET is then selected. If more than one path has the

same maximum predicted RET, the tie is broken by

selecting the minimum hop path of such paths.

3 REVIEW OF BRAODCASTINGSTRATEGIES

In general, the broadcasting strategies can be

grouped into four families: Simple flooding,Probability-based methods, Area-based methods and

Neighbor knowledge based methods.

3.1 Simple FloodingA source node initiates flooding by broadcasting

a packet to all its neighbors. The neighbor nodes in

turn rebroadcast the packet exactly once and the

process continues until each node in the network hasretransmitted the packet. As a result, all nodes

reachable from the source receive the packet.

Flooding causes the broadcast storm problem [1]which is characterized by redundant rebroadcasts,

channel contention and collision of messages.

3.2

Probability-based Methods

3.2.1 Probabilistic SchemeWhen a node receives a broadcast message for

the first time, the node rebroadcasts the message with

a probability P. If the message received is already

seen, then the node drops the message irrespective of

whether or not the node retransmitted the messagewhen received for the first time. For sparse networks,

the value ofPhas to be high enough to facilitate a

higher packet delivery ratio. WhenP= 1, the schemeresorts to simple flooding.

3.2.2 Counter-based SchemeA broadcast message received for the first time is

not immediately retransmitted to the neighborhood.The message is queued up for a time called the

Random Assessment Delay (RAD) during which the

node may receive the same message (redundantbroadcasts) from some of its other neighbors. After

the RAD timer expires, if the number of times the

same message is received exceeds a counter

threshold, the message is not retransmitted and issimply dropped.

3.3 Area-based Methods3.3.1 Distance-based Scheme

When a node receives a previously unseen

broadcast message, the node computes the distancebetween itself and the sender. If the sender is closer

than a threshold distance, the message is dropped and

all future receptions of the same message are alsodropped. Otherwise, the received message is cached

and the node initiates a RAD timer. Redundant

broadcast messages received before the expiry of the

RAD timer are also cached. When the RAD timerexpires, the node computes the distance between

itself and the neighbor nodes that previously

broadcast the particular message. If any suchneighbor node is closer than a threshold distance

value, the message is dropped. Otherwise, the

message is retransmitted.

3.3.2 Location-based SchemeWhenever a node originates or rebroadcasts a

message, the node puts its location information in the

message header. The receiver node calculates theadditional coverage area that would be obtainable if

it were to rebroadcast. If the additional coverage is

less than a threshold value, all future receptions ofthe same message will be dropped. Otherwise, the


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RAD timer is started. Redundant broadcast messagesreceived before the expiry of the RAD timer are also

cached. After the RAD timer expires, the node

considers all the cached messages and recalculatesthe additional obtainable coverage area if it were to

rebroadcast the particular message. If the additional

obtainable coverage area is less than a threshold

value, the cached messages are dropped. Otherwisethe message is rebroadcast.

3.4 Neighbor Knowledge based Methods3.4.1 Multi-point Relaying

Under this scheme, each node is assumed to have

a list of its 1-hop and 2-hop neighbors, obtained via periodic Hello beacons. The Hello messages

include the identifier of the sending node, the list of

the nodes known neighbors and the Multi-PointRelays (MPRs). After receiving Hello messages

from all its neighbors, a node has the 2-hop topology

information centered at itself. Using this list of 1-hop

and 2-hop neighbors, a node selects the MPRs the

1-hop neighbors that most efficiently reach all nodeswithin its 2-hop neighborhood. Each node selects the

set of MPRs using a greedy approach of iteratively

including the 1-hop neighbors that would cover thelargest number of uncovered 2-hop neighbors.

3.4.2 Minimum Connected Dominating SetA Connected Dominating Set (CDS) is a set of

nodes in the network such that all nodes in the

network are either in the CDS or directly attached toa node in the CDS. A Minimum Connected

Dominating Set (MCDS) is the smallest CDS, in

terms of the number of nodes in the CDS, for the

entire network. The size of the MCDS is the

minimum number of retransmissions required in abroadcasting process so that all nodes in the network

receive the broadcast message. Determining the

MCDS for a given network graph is an NP-complete problem and hence several heuristics have been

proposed to approximate the MCDS for a given

network graph.

4 SIMULATIONSWe use ns-2 (version 2.28) [10] as the simulator

for our study. The network dimensions are 1500m x

300m. The transmission range of each node is 250m.

These values are very commonly used in MANET

simulations. We vary the density of the network byconducting simulations with 25 nodes (low density)and 50 nodes (high density). The simulation time is

1000 seconds. While we implemented the FORP

protocol, we used the implementation of DSR thatcomes with ns-2.

4.1 Broadcasting Strategies

The route discovery mechanism in each of DSRand FORP is implemented with the following

broadcasting strategies: Simple flooding,

Probabilistic broadcasting, Distance-basedbroadcasting, MPR and MCDS-based broadcasting.

We choose the probabilistic scheme over counter-

based scheme as the range of counter values to

experiment with changes dynamically depending onnetwork density and node mobility. We choose the

distance-based scheme over the location-based

scheme because of the higher overhead in computingthe additional coverage area when a node receives

multiple broadcast messages from its neighbors. The

probability of retransmission was varied from 0.1 to

1. The threshold distance for triggering a broadcast isvaried from 20m to 200m, in increments of 20m. We

do not let any intermediate node to reply for the

RREQ packets and disable local route repairs as thismay affect our goal on studying the effect of the

different broadcasting strategies on the routing

metrics. We do not expect much congestion in our

network scenarios. Hence, the value of the RAD

timers used for the distance-based scheme is 0.01seconds, as suggested in [2].

4.2 Beacon MessagingEach node periodically broadcasts a Hello

beacon message in its neighborhood. The Hello

message contains the following information: thelocation of the node, its velocity and direction of

moving, the 1-hop neighbor list of the node, and theset of MPRs for the node. The Hello message is

used by FORP and the MPR and MCDS based

broadcasting strategies. In the case of FORP, the

clocks across all nodes are assumed to be

synchronized and each node also keeps track of thepreviously advertised location of its neighbor nodes.

This helps to keep track of the direction in which the

neighbor node is moving.

4.3 Simulation ModelsThe physical, data link and MAC layer models

are based on the multi-hop wireless network

extension [5] provided by the CMUs Monarch

research group. The MAC layer uses the DistributedCoordinated Function (DCF) of the IEEE Standard

802.11 [11] for wireless LANs. The radio model uses

the standard channel bandwidth of 2Mbps. The

signal propagation model used is the two-ray groundreflection model [5]. The interface queue stores boththe routing and data packets sent by the routing layer

until the MAC layer is able to transmit them. We use

a FIFO-based interface queue of length 100.The node mobility model used is the Random

Waypoint model [12]. Each node starts moving from

an arbitrary location to a randomly selecteddestination location at a speed uniformly distributed


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in the range [vmin, , vmax]. Once the destination isreached, the node may stop there for a certain time

called the pause time and then continue to move by

choosing a different target location and a differentvelocity. In this paper, we set vmin = 0. The vmax

values are 5, 10 (low mobility), 20, 30 (moderate

mobility), 40 and 50m/s (high mobility). The pause

time is 0 seconds.

4.4 Performance MetricsWe study the following performance metrics for

DSR and FORP:

(i) Percentage of successful route discovery ratio

(expressed as percentage) of the number ofsuccessful route discovery attempts to the total

number of route discovery attempts.

(ii) Number of retransmitted messages the numberof messages received at all the nodes in the network

per successful route discovery attempt, averaged

over alls-dsessions for the entire simulation time.

(iii)Number of retransmitting nodes the number of

nodes retransmitting the RREQ packet in thenetwork per successful route discovery attempt,

averaged over all s-d sessions for the entire

simulation time.(iv) Number of route transitions average of the

number of route discoveries required for all s-d

sessions.

(v)Hop count per route average of the number ofhops in routes, time-averaged over alls-dsessions.

4.5 Percentage of Successful Route DiscoveryWe refer to a successful route discovery as the

scenario when at least one RREQ packet broadcast

by the source reaches the destination. The flooding,MPR and MCDS approaches guarantee successful

route discovery if the underlying network is

connected. With the probability and distance-based broadcasting techniques, there is always a chance

that the RREQ packet does not reach its intended

destination, even though the underlying network may

be connected. The network density plays a huge rolein determining the minimum value for the probability

of retransmission and the maximum threshold

distance value for retransmission that wouldmaximize the number of successful route discoveries.

Larger the network density, the lower the minimum

probability of retransmission and larger the

maximum threshold distance for retransmission thatwould maximize the chances of a successful routediscovery. In this paper, we set ourselves a target of

92-95% successful route discoveries for each s-d

session.For a given probability of retransmission, the

percentage of successful route discoveries increases

as the network density increases. For a givennetwork density, the percentage of success in route

discoveries increases as the probability of aretransmission increases. At high network density,

one can obtain 100% success in route discoveries

when the probability of a retransmission is beyond0.7. With 25 nodes in the network, the maximum

achievable percentage of successful route discovery

is only 95%. Such a limitation arises due to the poor

connectivity of the network at low density. For eachnetwork density, we define a Threshold Probability,

ProbThresh, as the probability of retransmission that

results in 92-95% success in route discoveries and atthe same time the number of retransmitted messages

and the number of retransmitting nodes is the

minimum. For fixed probability of retransmission

values belowProbThresh, the percentage of success inroute discoveries decreases with increase in node

mobility. This is due to the increase in the number of

route discovery attempts as the node mobilityincreases. The value ofProbThresh was observed to be

0.7 with a network of 25 nodes and 0.4 with a

network of 50 nodes. The percentage of successful

route discoveries for DSR under the probabilistic

schemes is shown in Figures 1.1 and 1.2. Similarresults are obtained for FORP too.

Figure 1.1: Network of 25 Nodes


Figure 1: Percentage of Successful RouteDiscoveries with Probabilistic Scheme

Similarly, to obtain 92-95% success in route

discovery attempts, we choose DistTresh = 100m asthe maximum threshold distance value for

retransmission in a network of 25 nodes and DistTresh

= 180m as the maximum threshold value in anetwork of 50 nodes. The percentage of successful


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route discoveries for DSR under the distance-basedscheme is shown is shown in Figures 2.1 and 2.2.



Figure 2: Percentage of Successful Route

Discoveries with Distance-based Scheme

Figure 3 shows the percentage of successful route

discoveries using the selected threshold values forthe probability and distance-based schemes and the

other broadcasting techniques, including flooding.

Figure 3: Percentage of Successful Route

Discoveries with Different Broadcasting Techniques

4.6 Reduction in Retransmission OverheadSince we did not let any intermediate node to

reply for RREQ messages, the number of

retransmitting nodes (Figure 4) and the number of

retransmitted messages (Figure 5), depend only on

the network density, node mobility and the

broadcasting strategy used. With simple flooding

(Figure 4), each node retransmits the RREQ messageexactly once. Hence, as the network density

increases, the number of retransmitting nodesincreases. The destination node gets the RREQ

message through several paths and thus can choose

the best path depending on the route selection principles of the protocol employed. The route is

learnt with the least possible route-acquisition delay,

but with the maximum message retransmission

overhead.On the other hand, the number of retransmitting

nodes in the case of MCDS based route discovery is

the minimum since the RREQ message isretransmitted only by nodes that are part of the

approximate MCDS. But, the MCDS approach tends

to increase the route-acquisition delay, as prior to

route discovery, the MCDS itself needs to bedetermined. We run a distributed version of the

Kous heuristic [13] in the network to approximate

the MCDS. Each node then learns the set of itsMCDS neighbors and the presence/absence of the

node in the MCDS.

Figure 4: Average Number of Retransmitting Nodes

per Route Discovery

Figure 5: Average Number of Retransmitted

Messages per Route Discovery

For the MPR, the probabilistic and distance-based

schemes, the number of retransmitting nodes and thenumber of retransmitted messages is in between the

two extremes set by simple flooding and MCDS. TheMPR approach is not scalable as it does not take into

account the path taken by the RREQ message. The

set of MPR nodes is selected statically using agreedy approach of choosing neighbor nodes that

covered the maximum number of 2-hop neighbors.

When a node receives a RREQ message, the node

does not remove from its MPR set the neighbor


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nodes that might also have received the RREQmessage. The number of 1-hop and 2-hop neighbors

of a node is doubled as the network density is

doubled. As a result, the number of nodesconstituting the MPR set (the number of

retransmitting nodes) also doubles, when the number

of nodes in the network is increased from 25 to 50

nodes.When simulated under the probabilistic and

distance-based schemes using the threshold values

mentioned in Section 4.5, we observe that thenumber of retransmitting nodes (Figure 4) required

in a network of 50 nodes is only 20% more than the

number of retransmitting nodes required in a network

of 25 nodes. Similarly, we observe that in a low-density 25-node network operated atDistTresh = 100m,

the number of retransmitting nodes required to

guarantee a 92-95% success in route discovery isonly 40% more (and does not double) than that

required in a high-density 50-node network operated

atDistTresh = 180m.

From Figure 5, we also observe that the number

of retransmitted messages with flooding and MPRquadruples as we double the network density. This

illustrates that flooding and MPR are not scalable

broadcasting techniques. For the MCDS scheme, thenumber of retransmitted messages just doubles as the

network density doubles. With the probabilistic

scheme operated under the appropriate threshold

values, the number of retransmitted messages in a50-node network is 2.4 times to that incurred in a 25-

node network. With the distance-based schemeoperated under the appropriate threshold values, the

number of retransmitted messages in a 50-node

network is only 1.3 times to that incurred in a 25-

node network. These two observations illustrate that

the probabilistic and distance-based schemes, whenoperated at the appropriate threshold values for

retransmissions, are scalable. This is one of the

significant contribution and finding of our research.

4.7 Average Hop Count per PathFrom Figures 6 and 7, one can observe that the

average hop count per path for both DSR and FORP

is not very much influenced by node mobility and is

only affected by the broadcasting strategy used.When simple flooding is used as the route discovery

strategy, the destination node learns about several

routes from the source of the RREQ message to itself.

From this set, the destination node can then choosethe best route according to the route selection

principles of the routing protocol. When we employ

the different broadcasting strategies, we are reducing

the number of retransmitting nodes as well as thenumber of retransmitted messages. Hence, the

destination node learns only relatively fewer routes

compared to the situation when flooding is used.



Figure 6: Average Hop Count per Path for DSR

In the case of DSR (Figures 6.1 and 6.2), the hop

count of the routes chosen using MCDS and floodingis the minimum. Routing through the nodes that form

the minimum connected dominating set results in themessage traversing the minimum number of

intermediate hops from the source node to the

destination node. Figures 6.1 and 6.2 illustrate thatflooding also discovers a similar route with the

minimum number of hops from the source node to

the destination node. With MPR, probability-based

and distance-based schemes, the hop count of DSRroutes increases by at most 15% compared to that

discovered using MCDS and flooding.The hop count of FORP routes (Figures 7.1 and

7.2) is normally more than that of DSR routes.

Among a set of routes learnt, FORP selects the routethat has the largest predicted route expiration time.

For such routes, at the time of their selection, the

average physical distance of the constituent nodes ofa hop is only 55-65% of the transmission range of

the nodes. This results in the relatively larger hop

count for FORP routes.



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Figure 7: Average Hop Count per Path for FORP

The protocols learn the maximum and minimumnumber of source-destination (s-d) routes using

flooding and MCDS respectively. Thus, the average

hop count of FORP routes is 10-15% and 2-3% morethan that of DSR routes when the routes are learnt

respectively using flooding and MCDS.

In a probability-based scheme (Figures 8.1through 8.4), the number of retransmitting nodes

decreases as the probability for retransmission isreduced. At high network density, the dense

coverage of nodes within a neighborhood offsets for

the lower threshold probability of retransmission. Atlow network densities, one has to adopt reasonably

high values for the threshold probability of

retransmission in order to guarantee a highpercentage of success in route discoveries.

At high node mobility, the hop count of the

routes decreases drastically as the probability for

retransmission falls below 0.4 (for low densitynetworks) and 0.2 (for high density networks). This

could be attributed to the loss of connectivity between the source and the destination for low

values of the probability of retransmission. Thenetwork is partitioned into two or more segments.

There exists a path from the source to the destination

only if the two end nodes of the path are within thesame segment, thus accounting for the reduction in

the hop count when the network is partitioned. As

MPR incurs more message retransmissions, if we cantolerate a 15% sub-optimality in the hop count, the

distance-based or probabilistic schemes, at the

appropriate threshold values, may be preferred as the

route discovering strategies for DSR.

Figure 8.1:vmax = 5m/s, 25 Nodes




Figure 8: Probability of Retransmission Vs AverageHop Count per Path




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Figure 9: Threshold Distance of Retransmission VsAverage Hop Count per Path

In a distance-based scheme (Figures 9.1 through9.4), a node rebroadcasts the RREQ message only if

no neighbor node within the area covered by the

threshold distance of retransmission has yet

broadcasted the message. In the case of DSR, even

though we may use several threshold distance values

for retransmission, the protocol chooses only theroute that has the minimum hop count. Hence, the

hop count of DSR routes is not much sensitivetowards the threshold distance for retransmission,

except for high values of the distance. FORP isslightly more sensitive to the threshold distance of

retransmission. Routes with physical hop distance55-65% of the transmission range are more likely to

be found when the threshold distance for

retransmission of the RREQ messages is also only55-65% of the transmission range of the nodes.

4.8 Average Number of Route TransitionsIn the case of DSR (Figure 10), routes discovered

through flooding and MCDS have the minimum

number of hops. But such routes are very unstable as

observed in Figures 10.1 and 10.2. At the time of

route discovery, the average physical distancebetween the constituent nodes of a hop is close to 70-

80% of the transmission range of the nodes. Suchhops are highly vulnerable to fail as the constituent

nodes of the hop are more susceptible to move away

quickly. The chance of link failure in the near futureincreases with increase in node mobility.

Broadcasting strategies like MPR also do not offer

any improvement in the stability of the routes chosen.

The DSR protocol always targets for the minimum

hop route among the set of routes discovered usingthese broadcasting strategies. At the threshold values

for the probability of retransmission and the

threshold distance for retransmission, as indicated inFigures 10.1 and 10.2, DSR incurs 20% less

transitions compared to routes discovered using

flooding.



Figure 10: Stability of DSR Routes



Figure 11: Stability of FORP Routes

In the case of FORP (Figures 11.1 and 11.2), the


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routes are most stable when discovered usingflooding. This is because the targeted destination

node of the RREQ message receives the message

across several routes and selects the route with thehighest predicted route expiration time. When routes

are discovered using flooding, the number of route

transitions incurred by DSR is 70% and 125% more

than that incurred by FORP routes at low and highnetwork densities respectively.

When route discovery is done using MCDS, the

RREQ messages are propagated only by the nodes inthe MCDS and hence, the routes learnt are very

likely to be of minimum hop paths. Such routes are

least stable. When routes are discovered using

MCDS, the number of route transitions incurred byDSR is only 3-7% more than that incurred by FORP.

Thus, FORP routes selected using MCDS based

scheme are the most unstable of routes selected usingthe broadcasting strategies.

DSR routes are less stable in networks of high

density compared to networks of low density. This is

due to the edge effect problem [14]. In high

density networks, the average physical distance of ahop in a minimum-hop path during its discovery is

close to 80% of the transmission range of the node.

While in low-density networks, the average physicaldistance of a hop is only 70% of the transmission

range of the nodes. In high-density networks, when

we aim for minimum-hop, we can select the farthest

neighbor that is on the path towards the destination.But, this results in routes that are highly unstable.

When operated at the threshold distance forretransmission as shown in Figures 11.1 and 11.2,

the number of route transitions incurred for both the

protocols when using threshold distance of 180m is

at most 1.5 times to that incurred when using

threshold distance of 100m. More detailed results areshown in Figures 12.1 to 12.4.





Figure 12: Threshold Distance of Retransmission VsAverage Number of Route Transitions

Compared to the distance-based scheme, FORP

routes discovered using MPR and probability-based

scheme are relatively more stable. The number oftransitions incurred by these routes is only 20-35%

more than that incurred by routes discovered usingflooding. For low density networks and in networks

with high node mobility, the network connectivity is

very limited when operated with low values for the probability of retransmission (Figures 13.1 through

13.4). Under such conditions, the number ofsuccessful route discoveries and the number of route

transitions are low.




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Figure 13: Probability of Retransmission VsAverage Number of Route Transitions

5 CONCLUSIONSThe high-level contribution of this paper is a

simulation-based analysis on the impact of the

broadcast route discovery techniques on the stability

and hop count of routes discovered for the minimum-

hop based DSR and the stability-based FORPprotocols. We also showed that the probability-based

and distance-based schemes, when operated at theappropriate threshold values for retransmission, are

more scalable compared to the flooding and MPRschemes. Future work will also involve studying the

impact of the broadcasting strategies on the linkefficiency and stability of trees and meshes

determined for the multicast routing protocols.

For networks of low density and high density, weidentify the threshold values for the probability of

retransmission and the distance for retransmission,

using which we can obtain 92-95% success in route

discoveries with the minimum number of

retransmissions, and below these threshold values,the percentage of success in route discoveries

decreases with increase in node mobility. When

operated at the threshold probability values forretransmission, the number of retransmitting nodes in

a network of 50 nodes is only 20% more than the

number of retransmitting nodes in a network of 25

nodes. Also, when operated at the appropriatethreshold distances for retransmission, the number of

retransmitting nodes decreases as the network

density increases. The probabilistic and distance- based schemes require less overhead to implement

compared to the MPR and MCDS based schemes.

Determining the MCDS in a highly mobile network

itself will be a significant overhead.When we employ the different broadcasting

strategies, we are reducing the number of

retransmitting nodes as well as the number ofretransmitted messages. The routing protocols learn

only relatively fewer routes compared to the situation

when flooding is used. With flooding, each node in

the network retransmits the RREQ packet exactlyonce, thus resulting in the maximum number of

retransmissions. Letting the RREQs propagate

through the nodes that form the minimum connecteddominating set results in the packet traversing the

minimum number of intermediate hops with

minimum number of retransmissions from the source

to the destination. So, we learn the maximum andminimum number of routes using flooding and

MCDS respectively. The number of routes learnt

using the other broadcasting strategies is in betweenthese two extremes.

In the case of DSR, the hop count of routes

chosen using the MCDS and flooding based route

discovery approaches is the minimum. Nevertheless,

since DSR opts always for the minimum hop routes,the hop count of DSR routes discovered using MPR,

probability-based and distance-based schemes is at

most 15% more than that discovered using floodingand MCDS. This illustrates that routes having

minimum hop or close to being minimum hop are

very much discovered using the different

broadcasting strategies. If we can tolerate a 15% sub-optimality in the hop count, the distance-based or

probabilistic scheme at the appropriate thresholdvalues (which yield the minimum number of

retransmissions) may be preferred as the route

discovery strategies for DSR.

FORP targets stable routes and the hop count of

such routes are usually more than that of minimumhop routes. At the time of route selection, the

average physical distance of the constituent nodes of

a hop in stable routes is only 55-65% of thetransmission range of the nodes. Thus, FORP is more

sensitive to the different broadcasting strategies. The

average hop count of FORP routes is 10-15% more

than that of DSR routes when routes are learnt usingflooding, MPR, distance-based and probabilistic

approaches. While using MCDS, the hop count of

FORP routes is only 2-3% more than that of DSRroutes.

The stability of DSR routes does not change much

with the broadcasting strategy used. This is because

the protocol always targets for minimum-hop routesand manages to discover routes with minimum hopcount or routes close to minimum hop count

irrespective of the broadcasting strategy used. With

respect to FORP, the most stable routes arediscovered using flooding. FORP routes discovered

using the MCDS approach are the least stable as they

are more or less similar to DSR routes. FORP routesdiscovered using MPR and probability-based


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schemes (operated at the threshold probability forretransmission) incur only 20-35% more transitions

compared to those routes discovered using flooding.

With regards to distance-based schemes, FORProutes are more stable when discovered using

moderate values for the distance of retransmission.

This is because, at the time of route discovery itself

the physical distance between the constituent nodesof a hop is at least the threshold distance of

retransmission. In general, route discoveries with

less retransmission overhead yield less stable routesand vice-versa. We thus observe a tradeoff between

the number of retransmissions per stable route

discovery and the number of stable route discoveries

needed for a source-destination session.

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