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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 1.2: Network of 50 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 2.1: Network of 25 Nodes
Figure 2.2: Network of 50 Nodes
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 6.1: Network of 25 Nodes
Figure 6.2: Network of 50 Nodes
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.
Figure 7.1: Network of 25 Nodes
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Figure 7.2: Network of 50 Nodes
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 8.2:vmax = 5m/s, 50 Nodes
Figure 8.3:vmax = 50m/s, 25 Nodes
Figure 8.4:vmax = 50m/s, 50 Nodes
Figure 8: Probability of Retransmission Vs AverageHop Count per Path
Figure 9.1:vmax = 5m/s, 25 Nodes
Figure 9.2:vmax = 5m/s, 50 Nodes
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Figure 9.3:vmax = 50m/s, 25 Nodes
Figure 9.4:vmax = 50m/s, 50 Nodes
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 10.1: Network of 25 Nodes
Figure 10.2: Network of 50 Nodes
Figure 10: Stability of DSR Routes
Figure 11.1: Network of 25 Nodes
Figure 11.2: Network of 50 Nodes
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 12.1:vmax = 5m/s, 25 Nodes
Figure 12.2:vmax = 5m/s, 50 Nodes
Figure 12.3:vmax = 50m/s, 25 Nodes
Figure 12.4:vmax = 50m/s, 50 Nodes
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.
Figure 13.1:vmax = 5m/s, 25 Nodes
Figure 13.2:vmax = 5m/s, 50 Nodes
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Figure 13.3:vmax = 50m/s, 25 Nodes
Figure 13.4:vmax = 50m/s, 50 Nodes
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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