Broadcasting has many important uses and several mobile ad hoc networks (MANETs) protocols assume the availability of an underlying broadcast service. Applications, which make use of broadcasting, include LAN emulation, paging a particular node. However, broadcasting induces what is known as the "broadcast storm problem" which causes severe degradation in network performance, due to excessive redundant retransmission, collision, and contention. Although probabilistic flooding has been one of the earliest suggested approaches to broadcasting. There has not been so far any attempt to analyse its performance behaviour in MANETs. This paper investigates using extensive ns-2 simulations the effects of a number of important parameters in a MANET, including node speed, pause time and, traffic load, on the performance of probabilistic flooding. The results reveal that while these parameters have a critical impact on the reachability achieved by probabilistic flooding, they have relatively a lower effect on the number of saved rebroadcast packets

Ould-Khaoua, M.

Papanastasiou, S.

Yassein, M.B.

English

Enlighten: Publications

   
  
  
  
  
Yassein, M.B. and Ould-Khaoua, M. and Papanastasiou, S. (2005) 
Performance evaluation of flooding in MANETs in the presence of multi-
broadcast traffic. In, 11th International Conference on Parallel and 
Distributed Systems, 2005., 20-22 July 2005 Vol 2, pages pp. 505-509, 
Fukuoka, Japan.
 
http://eprints.gla.ac.uk/3407/  
  
  
  
 
1Performance Evaluation of Flooding in MANETs in the Presence of Multi-
Broadcast Traffic  
M. Bani Yassein, M. Ould-Khaoua and S. Papanastasiou  
Department of Computing Science  
University of Glasgow 
Glasgow G12 8RZ 
UK
{muneer, mohamed, stelios}@dcs.gla.ac.uk 
Abstract 
Broadcasting has many important uses and several 
Mobile Ad hoc Networks (MANETs) protocols assume the 
availability of an underlying broadcast service. 
Applications, which make use of broadcasting, include 
LAN emulation, paging a particular node,  However, 
broadcasting induces what is known as the “broadcast 
storm problem” which causes severe degradation in 
network performance, due to excessive redundant 
retransmission, collision, and contention.  Although 
probabilistic flooding has been one of the earliest 
suggested approaches to broadcasting.  There has not 
been so far any attempt to analyse its performance 
behaviour in MANETs. This paper investigates using 
extensive ns-2 simulations the effects of a number of 
important parameters in a MANET, including node 
speed, pause time and, traffic load, on the performance of 
probabilistic flooding. The results reveal that while these 
parameters have a critical impact on the reachability 
achieved by probabilistic flooding, they have relatively a 
lower effects on the number of saved rebroadcast packets. 
1. Introduction 
Mobile Ad hoc Networks (MANETs) consist of a set of 
wireless mobile nodes, which communicate with one 
another without relying on any pre-existing infrastructure 
in the network. The distributed, wireless, and self-
configuring nature of MANETs make them suitable for a 
wide variety of applications [1]. These include critical 
military operations, rescue and law enforcement missions 
as well as and disaster recovery scenarios [1]. Other 
applications of MANETs are in data acquisition in hostile 
territories, virtual classrooms, and temporary local area 
networks. Broadcasting is a fundamental operation in 
MANETs whereby a source node transmits a message 
that is to be disseminated to all the nodes in the network. 
In the one-to-all model, transmission by each node can 
reach all nodes that are within its transmission radius,  
while in the one-to-one model, each transmission is 
directed toward only one neighbour using narrow beam  
directional antennas or separate frequencies for each node 
[5]. A number of MANET routing protocols such as 
Dynamic Source Routing (DSR), Ad Hoc on Demand 
Distance Vector (AODV) ,  Zone Routing Protocol (ZRP) 
and Location Aided Routing (LAR) )  use broadcasting or 
one of its derivatives to establish routes[5]. Broadcasting 
also serves as the last resort for other group 
communication operations such as multicast.
One of the earliest broadcast mechanisms proposed 
in the literature is simple or “blind” flooding [6] where 
each node receives and then re-transmits the message to 
all its neighbours. The only ‘optimisation’ applied to this 
technique is that nodes remember broadcast messages 
received and do not act if they receive repeated copies of 
the same message [3]. However, a straightforward 
flooding broadcast is usually costly and results in serious 
redundancy and collisions in the network; such a scenario 
has often been referred to as the broadcast storm problem
[3, 7] and has generated many challenging research 
issues. A number of researchers have identified this 
problem by showing how serious it is through analyses 
and simulations [3, 7].  
A probabilistic approach to flooding has been suggested 
in [3, 6, 8, 9] as a means of reducing redundant 
rebroadcasts and alleviating the broadcast storm problem. 
In the probabilistic scheme, when receiving a broadcast 
message for the first time, a node rebroadcasts the 
message with a pre-determined probability p, every node 
has the same probability to rebroadcast the message. 
When the probability is 100%, this scheme reduces to 
simple flooding. The studies of [3, 7, 8] have shown that 
probabilistic broadcasts incur significantly lower 
overhead compared to blind flooding while maintaining a 
high degree of propagation for the broadcast messages. 
However, these studies have not taken into consideration 
the impact of important factors in a MANET including 
node mobility, and injected traffic load to assess the 
performance of probabilistic flooding. In an effort to gain 
a deeper understanding and gain a clearer insight 
environment , this paper investigate the effects of 
Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 
0-7695-2281-5/05 $20.00 © 2005 IEEE 
2mobility on the operation and effectiveness of 
probabilistic flooding, two important flooding metrics, 
namely reachability and saved rebroadcasts. In particular, 
using the popular random waypoint model we analyse 
through extensive simulations the impact of varying the 
node pause time and speed on the performance of 
probabilistic flooding [4]. The effects of varying traffic 
load, i.e. the number of broadcast request injected into the 
network per second.  The results reveal that while node 
speed, pause time, and traffic load have a critical impact 
on the reachability achieved by probabilistic flooding, 
they have relatively a lower impact on the saved 
rebroadcast packets. 
The rest of the paper is organised as follows. 
Section 2 gives an overview of the previous on 
broadcasting in MANETs. Section 3 present the 
performance results and analyse the behaviour of 
broadcasting flooding. Finally, Section 4 concludes by a 
recount of the obtained results and suggestions for future 
work.
2. Related Work
One of the earliest broadcast mechanisms is flooding, 
where every node in the network retransmits a message to 
its neighbours upon receiving it for the first time. 
Although flooding is very simple and easy to implement, 
it can be very costly and may lead to a serious problem, 
often known as the broadcast storm problem [3, 7] that is 
characterised by high redundant packet retransmissions, 
network bandwidth contention and collision. Ni et al [3, 
7] have studied the flooding protocol analytically and 
experimentally. Their obtained results have indicated that 
rebroadcast could provide at most 61% additional 
coverage and only 41% additional coverage in average 
over that already covered by the previous. Therefore, 
rebroadcasts are very costly and should be used with 
caution. The authors in [5] have also classified 
broadcasting schemes into five categories to reduce 
redundancy, contention, and collision. These categories 
are probabilistic, counter-based, distance-based, location-
based and cluster-based. A brief description for each of 
these categories is provided in the sequel. 
In the probabilistic scheme, a mobile node 
rebroadcasts packets according to a certain probability. In 
the counter-based scheme, a node determines whether to 
rebroadcast a packet or not by counting how many 
identical packets, it has received during a random delay. 
The counter-based scheme assumes that the expected 
additional coverage is so small that rebroadcast would be 
ineffective when the number of recipient broadcasting 
packets exceed a certain threshold value.  
The distance-based scheme uses the relative 
distance between a mobile node and previous sender to 
make a decision as to whether to rebroadcast a packet or 
not. In the location-based scheme, the additional coverage 
concept [3] is used to decide whether to rebroadcast a 
packet. Additional coverage is acquired by the locations 
of broadcasting nodes using the geographical information 
of a MANET [5].  
The cluster-based scheme divides the MANET into 
a number of clusters or sub-sets of mobile nodes. Each 
cluster has one cluster head and several gateways. Cluster 
head is a representative of the cluster whose rebroadcast 
can cover all hosts in that cluster. Only gateways can 
communicate with other clusters and have responsibilities 
to propagate the broadcast message to other clusters. 
         The simple flooding scheme [3, 8] is a 
straightforward broadcasting approach that is easy to 
implement with guaranteed message dissemination. In 
this scheme, a source broadcasts packets to every 
neighbour who in turn rebroadcasts received packets to its 
neighbours and so on. This process continues until all 
reachable nodes have received and rebroadcast the packet 
once. Of course, this approach has its obvious 
shortcoming redundancy and message contention. 
The probabilistic scheme [8, 9] is one of the 
alternative approaches that aim at reducing redundancy 
through rebroadcast timing control in an attempt to 
alleviate the broadcast storm problem. In this scheme, 
when receiving a broadcast message for the first time, a 
node rebroadcasts the message with a pre-determined 
probability p so that every node has the same probability 
to rebroadcast the message, regardless of its number of 
neighbours. In dense networks, multiple nodes share 
similar transmission range. Therefore, these probabilities 
control the frequency of rebroadcasts and thus might save 
network resources without affecting delivery ratios. It 
should be noticed that in sparse networks there is much 
less shared coverage; thus some nodes will not receive all 
the broadcast packets unless the probability parameter is 
high. 
          Previous studies [3, 8] have only considered the 
performance of probabilistic flooding as a function of the 
network density. This study investigates the effects of the 
node speed, pause time, traffic load, on the performance 
behaviour of the probabilistic approach to flooding in 
MANETs considering a wide range of mobility scenarios 
using the popular random waypoint model [4]. 
3. Performance Evaluation 
We have used the ns-2 packet level simulator (v.2.27) [2] 
to conduct extensive experiments to evaluate the 
performance of probabilistic flooding. The network 
considered for the performance analysis of the 
rebroadcast probability vs. traffic load varies from 1 
broadcast per second  up to  4 broadcast per second   with 
50 nodes on 600u600 m2, with each node engaging in 
communication transmitting within 250 meter radius and 
having bandwidth of 2Mbps. The random waypoint 
Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 
0-7695-2281-5/05 $20.00 © 2005 IEEE 
3model is used to simulate 25 mobility patterns with 
retransmission probabilities ranging from 0.1 to 1.0 
percent with 0.1 percent increment per trial. In short, the 
random waypoint model considers nodes that follow a 
motion-pause recurring mobility state. Each node at the 
beginning of the simulation remains stationary for pause 
time seconds, then chooses a random destination and 
starts moving towards it with speed selected from a 
uniform distribution (0, max_speed]. After the node 
reaches that destination, it again stands still for a pause 
time interval (pause_time) and picks up a new destination 
and speed [4]. This cycle repeats until the simulation 
terminates. The maximum speeds (max_speed) of 1, 5, 
10, 20 meter/second and pause times of 0 seconds are 
considered for the purposes of this study. The simulation 
parameters are summarised in Table 1.  
Table 1: Summary of the parameters used in the 
simulation experiments. 
Parameter Value
Transmitter range 250m 
Bandwidth 2Mbps 
Simulation time 
900 seconds 
Pause time 0,20,40 seconds 
Packet size 512 bytes 
Number of nodes 50 
Maximum speed 1,5,10 and 20 m/s 
Interface queue length 50 packets 
The performance of broadcast protocols can be 
measured by a variety of metrics [3, 5, 7]. A commonly 
used metric is the number of message re-transmissions 
with respect to the number of nodes in the network [8]. In 
this work, we use saved rebroadcast, which is a 
complementary measure as defined below. The next 
important metric is reachability, which is defined in 
terms of the ratio of nodes that received the broadcast 
message out of all the nodes in the network. The formal 
definitions of these two metrics are given as follows [3]. 
Saved ReBroadcasts (SRB): Let r be the number of 
nodes that received the broadcast message and let and t be 
the number of nodes that actually transmitted the 
message. The saved rebroadcast is then defined by (r – 
t)/r [3].
Reachability (RE): Reachability is defined by the 
percentage of nodes that received the broadcast message 
to the total number of nodes in the network. For useful 
information, the total number of nodes should include 
those nodes that are part of a connected component in the 
network [3]. 
         Figures 1-6 depict reachability percentages shown 
for increasing the rebroadcast probability. The figures 
show reachability with four different mean node speed 
and four different node traffic loads. Figure 1 suggests 
that achieved reachability using probabilistic flooding for 
continuous mobility (0 pause time) increases with 
medium speed. Furthermore, the trend in the following 
four figures suggest that the reachability increases as the 
node load increases.  
The reachability is getting better with higher load 
traffic and faster nodes the rational is as follows. As the 
load of the nodes increases, the number of nodes covering 
a particular area also increases. As the probability of the 
transmission is fixed for every node  this implies that 
these are more candidates for transmission in each 
“coverage “ area. Hence, there is greater chance that a 
transmission will occur, thus reachability increases. In 
addition to that, for given transmission range, as load 
increases the connectivity of the network increases then a 
small probability p is sufficient to achieve high 
reachability. but larger p is needed if the node distribution 
is sparse , the amount of reachability (RE) increases , 
proportionally to p , as p increases in addition as node 
speed increases the connectivity increases then the 
probability of partitioning decreases thus reachability 
increase.
The remaining simulation results give indication on 
the effect of speed and traffic load of the save 
rebroadcast. Figures 7 through 8, demonstrate this effect 
using 16 combinations of node traffic load and speed. As 
can be observed from the figures, the saved rebroadcast 
increases with higher nodes speeds and traffic load. The 
amount of saving (SRB) increases as the traffic load of 
the nodes increases, the number of nodes covering a 
particular area also increases. As the probability of the 
transmission is fixed for every node  this implies that 
these are more candidates for transmission in each 
“coverage “ area. Hence, there is greater chance that a 
transmission will occur, thus (SRB) increases at the level 
each probability. In addition to that, (SRB) decreases as p
increases in addition as node speed increases the 
connectivity increases then the probability of partitioning 
decreases thus (SRB) increases. 
0
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
RE
RE at 1m/s
RE at 5m/s
RE at 10m/s
RE at 20m/s
Figure 1 : The impact of load on reachability at one  
broadcast/ second for different node speed
Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 
0-7695-2281-5/05 $20.00 © 2005 IEEE 
40
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
R
E
RE at 1
RE at 5
RE at 10
RE at 20
Figure 2: The impact of traffic load on reachability  
at two broadcasts/ second for different node speed. 
0
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
R
E
RE at 1m/s
RE at 5m/s
RE at 10m/s
RE at 20m/s
Figure 3:  The impact of traffic load on reachability 
 at three broadcasts/second for different node speeds.
Reachability at four broadcasts
0
20
40
60
80
100
120
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
RE
RE at 1
RE at 5
RE at 10
RE at 20
Figure 4: The impact of traffic load in reachability  
at four broadcasts/second for different node speeds.
0.92
0.93
0.94
0.95
0.96
0.97
0 0.2 0.4 0.6 0.8 1
Probability
SR
B
SRB at 1
SRB at 5
SRB at 10
SRB at 20
Figure 5: The impact of  traffic  load on saved rebroadcast  
at one broadcast/ second for different node speed
0.92
0.925
0.93
0.935
0.94
0.945
0.95
0.955
0.96
0 0.2 0.4 0.6 0.8 1
Probability
SR
B
SRB at 1
SRB at 5
SRB at 10
SRB at 20
Figure 6: The impact of traffic load on saved rebroadcast   
at 2 broadcasts/second for different node speeds. 
0.925
0.93
0.935
0.94
0.945
0.95
0.955
0.96
0.965
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
SR
B
SRB at 1
SRB at 5
SRB at 10
SRB at 20
Figure 7: The impact of  traffic load on saved rebroadcast at  
three broadcast/ second for different node speed.
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
SR
B
SRB at 1m/s
SRB at 5m/s
SRB at 10m/s
SRB at 20m/s
Figure 8:  The impact of traffic load on saved  
rebroadcast at four  broadcasts/second for different node speeds. 
4. Conclusions
This paper has studied the effects of traffic load on 
reachability and saved rebroadcast ratios of Mobile Ad 
hoc Networks. In this study, we have used the random 
waypoint model applied to the probabilistic flooding 
approach. Through simulation, we have shown that there 
is a substantial effect of traffic load and mobility on the 
reachability and saved rebroadcast ratios. This prompts 
the need of a dynamically probability adjustment strategy 
for the probabilistic flooding approach. Another potential 
area of possible improvement includes investigating the 
effect of nodes’ transmission ranges on the rebroadcast 
probability. 
Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 
0-7695-2281-5/05 $20.00 © 2005 IEEE 
5References 
 [1]  I. Stojmenovic, Editor, Handbook of wireless 
networks and mobile computing, Wiley, New 
York, 2002. 
 [2]  The Network Simulator ns-
2,http://www.isi.edu/nsnam/ns/ns-man.html 
[3]  S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, 
The broadcast storm problem in a mobile ad hoc 
network, Wireless Networks, vol. 8, no. 2, pp.153- 
           167, 2002 
[4]   T. Camp, J. Boleng, V. Davies, A Survey of Mobility 
Models for Ad Hoc Network Research, Wireless 
Communication & Mobile Computing, vol. 2, no. 5, pp. 
483-502, 2002
[5]  B. Williams, T. Camp, Comparison of 
broadcasting techniques for mobile ad hoc 
networks. Proc. ACM Symposium on Mobile Ad 
Hoc Networking & Computing (MOBIHOC 2002),
pp. 194–205, 2002. 
[6]  W. Lou, J. Wu, On Reducing Broadcast redundancy in 
mobile ad hoc networks, IEEE Transactions Mobile 
Computing, vol. 1, no. 2, pp. 111-123, 2002
  [7]  Y.-C. Tseng, S.-Y. Ni, E.-Y. Shih, Adaptive 
approaches to relieving broadcast storm in a 
wireless multihop mobile ad hoc network, IEEE 
Transactions Computers, vol. 52, no 5, 2003. 
  [8]  Y. Sasson, D. Cavin, A. Schiper, Probabilistic 
broadcast for flooding in wireless mobile ad hoc 
networks, WCNC 2003 - IEEE Wireless 
Communications and Networking Conference, pp. 
1124-1130, 2003. 
[9]   J. Cartigny,  D. Simplot, Border node retransmission 
based probabilistic broadcast protocols in ad-hoc 
networks, Telecommunication Systems, vol. 22, 
no 1– 4, pp. 189–204, 2003.  
Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 
0-7695-2281-5/05 $20.00 © 2005 IEEE 


Performance evaluation of flooding in MANETs in the presence of multi-broadcast traffic

Enlighten

           Yassein, M.B. and Ould-Khaoua, M. and Papanastasiou, S. (2005) Performance evaluation of flooding in MANETs in the presence of multi-broadcast traffic. In, 11th International Conference on Parallel and Distributed Systems, 2005., 20-22 July 2005 Vol 2, pages pp. 505-509, Fukuoka, Japan. http://eprints.gla.ac.uk/3407/         1Performance Evaluation of Flooding in MANETs in the Presence of Multi-Broadcast Traffic  M. Bani Yassein, M. Ould-Khaoua and S. Papanastasiou  Department of Computing Science  University of Glasgow Glasgow G12 8RZ UK{muneer, mohamed, stelios}@dcs.gla.ac.uk Abstract Broadcasting has many important uses and several Mobile Ad hoc Networks (MANETs) protocols assume the availability of an underlying broadcast service. Applications, which make use of broadcasting, include LAN emulation, paging a particular node,  However, broadcasting induces what is known as the “broadcast storm problem” which causes severe degradation in network performance, due to excessive redundant retransmission, collision, and contention.  Although probabilistic flooding has been one of the earliest suggested approaches to broadcasting.  There has not been so far any attempt to analyse its performance behaviour in MANETs. This paper investigates using extensive ns-2 simulations the effects of a number of important parameters in a MANET, including node speed, pause time and, traffic load, on the performance of probabilistic flooding. The results reveal that while these parameters have a critical impact on the reachability achieved by probabilistic flooding, they have relatively a lower effects on the number of saved rebroadcast packets. 1. Introduction Mobile Ad hoc Networks (MANETs) consist of a set of wireless mobile nodes, which communicate with one another without relying on any pre-existing infrastructure in the network. The distributed, wireless, and self-configuring nature of MANETs make them suitable for a wide variety of applications [1]. These include critical military operations, rescue and law enforcement missions as well as and disaster recovery scenarios [1]. Other applications of MANETs are in data acquisition in hostile territories, virtual classrooms, and temporary local area networks. Broadcasting is a fundamental operation in MANETs whereby a source node transmits a message that is to be disseminated to all the nodes in the network. In the one-to-all model, transmission by each node can reach all nodes that are within its transmission radius,  while in the one-to-one model, each transmission is directed toward only one neighbour using narrow beam  directional antennas or separate frequencies for each node [5]. A number of MANET routing protocols such as Dynamic Source Routing (DSR), Ad Hoc on Demand Distance Vector (AODV) ,  Zone Routing Protocol (ZRP) and Location Aided Routing (LAR) )  use broadcasting or one of its derivatives to establish routes[5]. Broadcasting also serves as the last resort for other group communication operations such as multicast.One of the earliest broadcast mechanisms proposed in the literature is simple or “blind” flooding [6] where each node receives and then re-transmits the message to all its neighbours. The only ‘optimisation’ applied to this technique is that nodes remember broadcast messages received and do not act if they receive repeated copies of the same message [3]. However, a straightforward flooding broadcast is usually costly and results in serious redundancy and collisions in the network; such a scenario has often been referred to as the broadcast storm problem[3, 7] and has generated many challenging research issues. A number of researchers have identified this problem by showing how serious it is through analyses and simulations [3, 7].  A probabilistic approach to flooding has been suggested in [3, 6, 8, 9] as a means of reducing redundant rebroadcasts and alleviating the broadcast storm problem. In the probabilistic scheme, when receiving a broadcast message for the first time, a node rebroadcasts the message with a pre-determined probability p, every node has the same probability to rebroadcast the message. When the probability is 100%, this scheme reduces to simple flooding. The studies of [3, 7, 8] have shown that probabilistic broadcasts incur significantly lower overhead compared to blind flooding while maintaining a high degree of propagation for the broadcast messages. However, these studies have not taken into consideration the impact of important factors in a MANET including node mobility, and injected traffic load to assess the performance of probabilistic flooding. In an effort to gain a deeper understanding and gain a clearer insight environment , this paper investigate the effects of Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 0-7695-2281-5/05 $20.00 © 2005 IEEE 2mobility on the operation and effectiveness of probabilistic flooding, two important flooding metrics, namely reachability and saved rebroadcasts. In particular, using the popular random waypoint model we analyse through extensive simulations the impact of varying the node pause time and speed on the performance of probabilistic flooding [4]. The effects of varying traffic load, i.e. the number of broadcast request injected into the network per second.  The results reveal that while node speed, pause time, and traffic load have a critical impact on the reachability achieved by probabilistic flooding, they have relatively a lower impact on the saved rebroadcast packets. The rest of the paper is organised as follows. Section 2 gives an overview of the previous on broadcasting in MANETs. Section 3 present the performance results and analyse the behaviour of broadcasting flooding. Finally, Section 4 concludes by a recount of the obtained results and suggestions for future work.2. Related WorkOne of the earliest broadcast mechanisms is flooding, where every node in the network retransmits a message to its neighbours upon receiving it for the first time. Although flooding is very simple and easy to implement, it can be very costly and may lead to a serious problem, often known as the broadcast storm problem [3, 7] that is characterised by high redundant packet retransmissions, network bandwidth contention and collision. Ni et al [3, 7] have studied the flooding protocol analytically and experimentally. Their obtained results have indicated that rebroadcast could provide at most 61% additional coverage and only 41% additional coverage in average over that already covered by the previous. Therefore, rebroadcasts are very costly and should be used with caution. The authors in [5] have also classified broadcasting schemes into five categories to reduce redundancy, contention, and collision. These categories are probabilistic, counter-based, distance-based, location-based and cluster-based. A brief description for each of these categories is provided in the sequel. In the probabilistic scheme, a mobile node rebroadcasts packets according to a certain probability. In the counter-based scheme, a node determines whether to rebroadcast a packet or not by counting how many identical packets, it has received during a random delay. The counter-based scheme assumes that the expected additional coverage is so small that rebroadcast would be ineffective when the number of recipient broadcasting packets exceed a certain threshold value.  The distance-based scheme uses the relative distance between a mobile node and previous sender to make a decision as to whether to rebroadcast a packet or not. In the location-based scheme, the additional coverage concept [3] is used to decide whether to rebroadcast a packet. Additional coverage is acquired by the locations of broadcasting nodes using the geographical information of a MANET [5].  The cluster-based scheme divides the MANET into a number of clusters or sub-sets of mobile nodes. Each cluster has one cluster head and several gateways. Cluster head is a representative of the cluster whose rebroadcast can cover all hosts in that cluster. Only gateways can communicate with other clusters and have responsibilities to propagate the broadcast message to other clusters.          The simple flooding scheme [3, 8] is a straightforward broadcasting approach that is easy to implement with guaranteed message dissemination. In this scheme, a source broadcasts packets to every neighbour who in turn rebroadcasts received packets to its neighbours and so on. This process continues until all reachable nodes have received and rebroadcast the packet once. Of course, this approach has its obvious shortcoming redundancy and message contention. The probabilistic scheme [8, 9] is one of the alternative approaches that aim at reducing redundancy through rebroadcast timing control in an attempt to alleviate the broadcast storm problem. In this scheme, when receiving a broadcast message for the first time, a node rebroadcasts the message with a pre-determined probability p so that every node has the same probability to rebroadcast the message, regardless of its number of neighbours. In dense networks, multiple nodes share similar transmission range. Therefore, these probabilities control the frequency of rebroadcasts and thus might save network resources without affecting delivery ratios. It should be noticed that in sparse networks there is much less shared coverage; thus some nodes will not receive all the broadcast packets unless the probability parameter is high.           Previous studies [3, 8] have only considered the performance of probabilistic flooding as a function of the network density. This study investigates the effects of the node speed, pause time, traffic load, on the performance behaviour of the probabilistic approach to flooding in MANETs considering a wide range of mobility scenarios using the popular random waypoint model [4]. 3. Performance Evaluation We have used the ns-2 packet level simulator (v.2.27) [2] to conduct extensive experiments to evaluate the performance of probabilistic flooding. The network considered for the performance analysis of the rebroadcast probability vs. traffic load varies from 1 broadcast per second  up to  4 broadcast per second   with 50 nodes on 600?600 m2, with each node engaging in communication transmitting within 250 meter radius and having bandwidth of 2Mbps. The random waypoint Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 0-7695-2281-5/05 $20.00 © 2005 IEEE 3model is used to simulate 25 mobility patterns with retransmission probabilities ranging from 0.1 to 1.0 percent with 0.1 percent increment per trial. In short, the random waypoint model considers nodes that follow a motion-pause recurring mobility state. Each node at the beginning of the simulation remains stationary for pause time seconds, then chooses a random destination and starts moving towards it with speed selected from a uniform distribution (0, max_speed]. After the node reaches that destination, it again stands still for a pause time interval (pause_time) and picks up a new destination and speed [4]. This cycle repeats until the simulation terminates. The maximum speeds (max_speed) of 1, 5, 10, 20 meter/second and pause times of 0 seconds are considered for the purposes of this study. The simulation parameters are summarised in Table 1.  Table 1: Summary of the parameters used in the simulation experiments. Parameter ValueTransmitter range 250m Bandwidth 2Mbps Simulation time 900 seconds Pause time 0,20,40 seconds Packet size 512 bytes Number of nodes 50 Maximum speed 1,5,10 and 20 m/s Interface queue length 50 packets The performance of broadcast protocols can be measured by a variety of metrics [3, 5, 7]. A commonly used metric is the number of message re-transmissions with respect to the number of nodes in the network [8]. In this work, we use saved rebroadcast, which is a complementary measure as defined below. The next important metric is reachability, which is defined in terms of the ratio of nodes that received the broadcast message out of all the nodes in the network. The formal definitions of these two metrics are given as follows [3]. Saved ReBroadcasts (SRB): Let r be the number of nodes that received the broadcast message and let and t be the number of nodes that actually transmitted the message. The saved rebroadcast is then defined by (r – t)/r [3].Reachability (RE): Reachability is defined by the percentage of nodes that received the broadcast message to the total number of nodes in the network. For useful information, the total number of nodes should include those nodes that are part of a connected component in the network [3].          Figures 1-6 depict reachability percentages shown for increasing the rebroadcast probability. The figures show reachability with four different mean node speed and four different node traffic loads. Figure 1 suggests that achieved reachability using probabilistic flooding for continuous mobility (0 pause time) increases with medium speed. Furthermore, the trend in the following four figures suggest that the reachability increases as the node load increases.  The reachability is getting better with higher load traffic and faster nodes the rational is as follows. As the load of the nodes increases, the number of nodes covering a particular area also increases. As the probability of the transmission is fixed for every node  this implies that these are more candidates for transmission in each “coverage “ area. Hence, there is greater chance that a transmission will occur, thus reachability increases. In addition to that, for given transmission range, as load increases the connectivity of the network increases then a small probability p is sufficient to achieve high reachability. but larger p is needed if the node distribution is sparse , the amount of reachability (RE) increases , proportionally to p , as p increases in addition as node speed increases the connectivity increases then the probability of partitioning decreases thus reachability increase.The remaining simulation results give indication on the effect of speed and traffic load of the save rebroadcast. Figures 7 through 8, demonstrate this effect using 16 combinations of node traffic load and speed. As can be observed from the figures, the saved rebroadcast increases with higher nodes speeds and traffic load. The amount of saving (SRB) increases as the traffic load of the nodes increases, the number of nodes covering a particular area also increases. As the probability of the transmission is fixed for every node  this implies that these are more candidates for transmission in each “coverage “ area. Hence, there is greater chance that a transmission will occur, thus (SRB) increases at the level each probability. In addition to that, (SRB) decreases as pincreases in addition as node speed increases the connectivity increases then the probability of partitioning decreases thus (SRB) increases. 0204060801000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilityRERE at 1m/sRE at 5m/sRE at 10m/sRE at 20m/sFigure 1 : The impact of load on reachability at one  broadcast/ second for different node speedProceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 0-7695-2281-5/05 $20.00 © 2005 IEEE 40204060801000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilityRERE at 1RE at 5RE at 10RE at 20Figure 2: The impact of traffic load on reachability  at two broadcasts/ second for different node speed. 0204060801000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilityRERE at 1m/sRE at 5m/sRE at 10m/sRE at 20m/sFigure 3:  The impact of traffic load on reachability  at three broadcasts/second for different node speeds.Reachability at four broadcasts0204060801001200 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilityRERE at 1RE at 5RE at 10RE at 20Figure 4: The impact of traffic load in reachability  at four broadcasts/second for different node speeds.0.920.930.940.950.960.970 0.2 0.4 0.6 0.8 1ProbabilitySRBSRB at 1SRB at 5SRB at 10SRB at 20Figure 5: The impact of  traffic  load on saved rebroadcast  at one broadcast/ second for different node speed0.920.9250.930.9350.940.9450.950.9550.960 0.2 0.4 0.6 0.8 1ProbabilitySRBSRB at 1SRB at 5SRB at 10SRB at 20Figure 6: The impact of traffic load on saved rebroadcast   at 2 broadcasts/second for different node speeds. 0.9250.930.9350.940.9450.950.9550.960.9650 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilitySRBSRB at 1SRB at 5SRB at 10SRB at 20Figure 7: The impact of  traffic load on saved rebroadcast at  three broadcast/ second for different node speed.0.90.910.920.930.940.950.960.970 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ProbabilitySRBSRB at 1m/sSRB at 5m/sSRB at 10m/sSRB at 20m/sFigure 8:  The impact of traffic load on saved  rebroadcast at four  broadcasts/second for different node speeds. 4. ConclusionsThis paper has studied the effects of traffic load on reachability and saved rebroadcast ratios of Mobile Ad hoc Networks. In this study, we have used the random waypoint model applied to the probabilistic flooding approach. Through simulation, we have shown that there is a substantial effect of traffic load and mobility on the reachability and saved rebroadcast ratios. This prompts the need of a dynamically probability adjustment strategy for the probabilistic flooding approach. Another potential area of possible improvement includes investigating the effect of nodes’ transmission ranges on the rebroadcast probability. Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 0-7695-2281-5/05 $20.00 © 2005 IEEE 5References  [1]  I. Stojmenovic, Editor, Handbook of wireless networks and mobile computing, Wiley, New York, 2002.  [2]  The Network Simulator ns-2,http://www.isi.edu/nsnam/ns/ns-man.html [3]  S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P. Sheu, The broadcast storm problem in a mobile ad hoc network, Wireless Networks, vol. 8, no. 2, pp.153-            167, 2002 [4]   T. Camp, J. Boleng, V. Davies, A Survey of Mobility Models for Ad Hoc Network Research, Wireless Communication & Mobile Computing, vol. 2, no. 5, pp. 483-502, 2002[5]  B. Williams, T. Camp, Comparison of broadcasting techniques for mobile ad hoc networks. Proc. ACM Symposium on Mobile Ad Hoc Networking & Computing (MOBIHOC 2002),pp. 194–205, 2002. [6]  W. Lou, J. Wu, On Reducing Broadcast redundancy in mobile ad hoc networks, IEEE Transactions Mobile Computing, vol. 1, no. 2, pp. 111-123, 2002  [7]  Y.-C. Tseng, S.-Y. Ni, E.-Y. Shih, Adaptive approaches to relieving broadcast storm in a wireless multihop mobile ad hoc network, IEEE Transactions Computers, vol. 52, no 5, 2003.   [8]  Y. Sasson, D. Cavin, A. Schiper, Probabilistic broadcast for flooding in wireless mobile ad hoc networks, WCNC 2003 - IEEE Wireless Communications and Networking Conference, pp. 1124-1130, 2003. [9]   J. Cartigny,  D. Simplot, Border node retransmission based probabilistic broadcast protocols in ad-hoc networks, Telecommunication Systems, vol. 22, no 1– 4, pp. 189–204, 2003.  Proceedings of the 2005 11th International Conference on Parallel and Distributed Systems (ICPADS'05) 0-7695-2281-5/05 $20.00 © 2005 IEEE 

Performance evaluation of flooding in MANETs in the presence of multi-broadcast traffic

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