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The Broadcast Storm Problem in a Mobile Ad Hoc Network *

Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, and Jang-Ping Sheu

Department of Computer Science and Information Engineering
National Central University
Chung-Li, 32054, Taiwan
Tel: 886-3-4227 151 ext. 45 12, Fax: 886-3-422268 1
Email: yctseng@csie.ncu.edu.tw
URL: http://www.csie.ncu.edu.tw/~yctseng/

an environment. Due to considerations such as radio power

limitation, channel utilization, and power-saving concerns, a
mobile host may not be able to communicate directly with
other hosts in a single-hop fashion. In this case, a multihop
scenario occurs, where the packets sent by the source host
are relayed by several intermediate hosts before reaching the
destination host.
Applications of MANETs occur in situations like battlefields or major disaster areas where networks need to be deployed immediately but base stations or fixed network infrastructures are not available. Unicast routing in MANET
has been studied in several articles [6,7, 14, 15,231. A working group called manet has been formed by the Internet
Engineering Task Force (IETF) to study the related issues
and stimulate research in MANET [21].
This paper studies the problem of sending a broadcast
message in a MANET. Broadcasting is a common operation


Broadcastingis a common operation in a network to resolve

many issues. In a mobile ad hoc network (MANET) in particular, due to host mobility, such operations are expected
to be executed more frequently (such as finding a route to
a particular host, paging a particular host, and sending an
alarm signal). Because radio signals are likely to overlap
with others in a geographical area, a straightforward broadcasting by flooding is usually very costly and will result in
serious redundancy, contention, and collision, to which we
refer as the broadcast storm problem. In this paper, we identify this problem by showing how serious it is through analyses and simulations. We propose several schemes to reduce
redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem. Simulation results are presented, which show different levels of improvement over the
basic flooding approach.
Keywords: broadcast, communication, mobile ad hoc network (MANET), mobile computing, wireless network.

in many applications, e.g., graph-related problems and distributed computing problems. It is also widely used to resolve many network layer problems. In a MANET in particular, due to host mobility, broadcastings are expected to be
performed more frequently (e.g., for paging a particular host,
sending an alarm signal, and finding a route to a particular
host [6, 14, 15,231). Broadcasting may also be used in LAN
emulation [2] or serve as a last resort to provide multicast
services in networks with rapid changing topologies.
In this paper, we assume that mobile hosts in the MANET
share a single common channel with carrier sense multiple
access (CSMA), but no collision detection (CD), capability.
Synchronization in such a network with mobility is unlikely,
and global network topology information is unavailable to
facilitate the scheduling of a broadcast. So one straightforward and obvious solution is broadcasting by flooding.
Unfortunately, in this paper we observe that serious redundancy, contention, and collision could exist if flooding is
done blindly. First, because the radio propagation is omnidirectional and a physical location may be covered by the
transmission ranges of several hosts, many rebroadcasts are
considered to be redundant. Second, heavy contention could
exist because rebroadcasting hosts are probably close to each

1 Introduction
The advancement in wireless communication and economical, portable computing devices have made mobile computing possible. One research issue that has attracted a lot of
attention recently is the design of mobile ad hoc network
(MANET). A MANET is one consisting of a set of mobile hosts that may communicate with one another and roam
around at their will. No base stations are supported in such
This work is supported by the National Science Council of the Republic of China
under Grants #NSC88-2213-E-008-013 and #NSC88-2213-E-008-014.
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other. Third, collisions are more likely to occur because the

RTSKTS dialogue is inapplicable and the timing of rebroadcasts is highly correlated.
Collectively, we refer to these problems associated with
flooding as the broadcast storm problem. Through analyses and simulations, we demonstrate how serious the storm
is. Two directions to alleviate this problem is to reduce the
possibility of redundant rebroadcasts and differentiate the
timing of rebroadcasts. Following these directions, we develop several schemes, called probabilistic, counter-bused,
distance-bused, locution-bused, and cluster-bused schemes,
to facilitate MANET broadcasting. Simulation results are
presented to study the effectiveness of these schemes.
To the best of our knowledge, the broadcast storm problem has not been addressed in depth for MANET before. It is
however worth of summarizing some results for broadcasting that are for other environments. Works in [3,4,9, 10, 11,
201 assume a packet-radio network environment. Most of
these results rely on time division multiple access (TDMA,
which requires timing synchronization) and certain levels of
topology information. Their goal is to find a slot assignment. Obtaining an optimal assignment has been shown to
be NP-hard [9]. The broadcast scheduling problem studied
in [8, 16,24, 251, although carries a similar name, is not intended to solve the problem addressed in this paper. Its goal
is to assign a contention-free time slot to each radio station.
The rest of this paper is organized as follows. Section 2
defines and analyzes the broadcast storm problem. Mechanisms to alleviate the storm are proposed in Sections 3. Simulation results are in Section 4 and conclusions are drawn in
Section 5.




The broadcast is unreliable. No acknowledgement

mechanism will be used.2 However, attempt should
be made to distribute a broadcast message to as many
hosts as possible without paying too much effort. The
motivations to make such an assumption are (i) a host
may miss a broadcast message because it is off-line,
it is temporarily isolated from the network, or it experiences repetitive collisions, (ii) acknowledgements
may cause serious medium contention (and thus another storm) surrounding the sender, and (iii) in many
applications (e.g., the route discovery in [6, 14, 15,
23]), a 100% reliable broadcast is unnecessary.

In addition, we assume that a host can detect duplicate

broadcast messages. This is essential to prevent endless flooding of a message. One way to do so is to associate with each
broadcast message a tuple (source ID, sequence number) as
that in [6,23].
Finally, we comment that we do not confine ourselves to
the broadcasting of the same message.3 In this paper we focus on the flooding behavior in MANET - the phenomenon
where the transmission of a packet will trigger other surrounding hosts to transmit the same (or modified) packet.
We shall show that if flooding is used blindly, many redundant messages will be sent and serious contention/collision
will be incurred. Our goal is to solve broadcast with efficiency in mind.




by Flooding

A straight-forward approach to perform broadcast is byfioding. A host, on receiving a broadcast message for the first
time, has the obligation to rebroadcast the message. Clearly,
this costs n transmissions in a network of it hosts. In a
CSMAICA network, drawbacks of flooding include:

in a MANET

A MANET consists of a set of mobile hosts that may communicate with one another from time to time. No base stations are supported. Each host is equipped with a CSMAICA
(carrier sense multiple access with collision avoidance) [ 191
transceiver. In such environment, a host may communicate
with another directly or indirectly. In the latter case, a multihop scenario occurs, where the packets originated from the
source host are relayed by several intermediate hosts before
reaching the destination.
The broadcastproblem refers to the sending of a message
to other hosts in the network. The problem considered here
has the following characteristics.

The broadcast is spontaneous. Any mobile host can

issue a broadcast operation at any time. For reasons
such as the host mobility and the lack of synchronization, preparing any kind of global topology knowledge
is prohibitive (in fact this is at least as hard as the
broadcast problem). Little or no local information may
be collected in advance.


Redundant rebroadcasts: When a mobile host decides to rebroadcast a broadcast message to its neighbors, all its neighbors already have the message.
Contention: After a mobile host broadcasts a message, if many of its neighbors decide to rebroadcast
the message, these transmissions (which are all from
nearby hosts) may severely contend with each other.
Collision: Because of the deficiency of backoff mechanism, the lack of RTSKTS dialogue, and the absence
of CD, collisions are more likely to occur and cause
more damage.
A more strict one is rdiuhlr
[I, 221, whose goal is to ensure all hosts receive the message. High-level
between hosts are exchanged.
are typically
at the application
layer and is out of the scope
of this paper. However,
the result in this paper may serve as an underlying
reliable broadcast.
?he MAC specification
in IEEE 802. I1 [ 191 does not request acknowledgement
on receipt of broadcast
For instance,
the routing protocols
in [6, 14, 15, 231 rely on broadcasting
packet called
to search for a route from a source to a particular
destination. When propagating
such a request. a host generally
appends its ID to the message
so that appropriate
routing information
can be collected.



Figure 2: Analysis on the extra area that can benefit from

a rebroadcast: A sends a broadcast packet and B decides to
rebroadcasts the packet.


Figure 1: Two optimal broadcasting schedules in MANETs.

Connectivity between hosts is represented by links. White
nodes are source hosts, and gray nodes are relay hosts.

Collectively, we refer to the above phenomena as the broadcast storm problem. The following discussion shows how
serious the storm is through analyses.


on Redundant


We first use two examples to demonstrate how much redundancy could be generated. In Fig. l(a), it only takes two
transmissions for the white node to broadcast a message,
whereas four transmissions will be carried out if no attempt
is made to reduce redundancy. Fig. l(b) shows an even serious scenario: only two transmissions are sufficient to complete a broadcast as opposed to seven transmissions caused
by flooding.
The following analysis shows that rebroadcasts are very
costly and should be used with caution. Consider the simple scenario in Fig. 2, where host A sends a broadcast message, and host B decides to rebroadcast the message. Let
SA and Sn denote the circle areas covered by As and Bs
transmissions, respectively. The additional area that can benefit from Bs rebroadcast is the shaded region, denoted as
&-A. Let r be the radii of SA and Sn, and d the distance between A and B. We can derive that (&-A I= ISn I- lS~nn I=
SC?- INTC(d), where INTC(d) is the intersection area of
the two circles centered at two points distanced by d,
INTC(d) = 4 J,;, mdx.
When d = r, the coverage area I&-AI is the largest, which
equals rc? - INTC(r) = ?( !j + a2 ) M 0.617~3. This shows
a surprising fact that a rebroadcast can provide only 0 N 6 1%
additional coverage over that already covered by the previous transmission.
Also, we would like to know the average value of rc? INTC(d). Supposing that B can randomly locate in any of
As transmission range, the average value can be obtained by
integrating the above value over the circle of radius x cen-



No. of transmissions heard (k)


Figure 3: Analysis on redundancy: the expected additional

coverage EAC(k) (divided by Xr2) after a host heard a broadcast message k times.
tered at A for x in [0, r]:


Thus, after the previous broadcast, a rebroadcast can cover

only additional 41% area in average.
Now consider the scenario of having received a broadcast
message twice: if host C decides to rebroadcast after it heard
As and Bs broadcasts. The area that can benefit from Cs
rebroadcast is SC-(AB). Through simulations (by randomly
generating A and B on Cs transmission range with grid estimation), we found that in average lS~-t~Un)l M 0.19719.
This shows an even dimmer prospect of hoping rebroadcasts
to propagate the message to new hosts.
In general, we would like to know the benefit of a host rebroadcasting a message after heard the message k times. The
result can be easily obtained from simulation by randomly
generating k hosts in a host Xs transmission range and calculating the area covered by X excluding those already covered by the other k hosts. Denote this value by EAC(k) (EAC
stands for expected additional coverage). Fig. 3 shows our
simulation result. As can be seen, when k > 4, the expected
additional coverage is below 0.05%.


6 ,
of hosts (n)

8 8
cola ent-Ike hosts (k)

Figure 4: Analysis on contention: the probabilities of having

k contention-free hosts among II receiving hosts.


on Contention

To address the contention problem, consider the situation

where host A transmits a broadcast message and there are n
hosts hearing this message. If all these hosts try to rebroadcast the message, contention may occur because two or more
hosts around A are likely to be close and thus contend with
each other on the wireless medium.
Lets analyze the simpler case of n = 2. Let hosts B and
C be the two receiving hosts. Let B randomly locate at As
transmission range. In order for C to contend with B, it must
locate in the area SA,-,B. So the probability of contention is
Let x be the distance between A and B. Integrating the above formula over the circle of radius x from 0
to r, the expected probability of contention is


on Collision

In a MANET, there is no base station or access point. Therefore, in this paper we exclude the use of the point coordinate
function (PCF) described in the IEEE 802.11 MAC specification [ 191, and study mainly the behavior under the distributed coordinate function (DCF).
The CSMAKA mechanism requires a host to start abackof procedure right after the host transmitted a message, or
when a host wants to transmit but the medium is busy and
the previous backoff has been done. To perform a backoff, a
counter is first set to an integer randomly picked from its current backoff window. If the channel clear assessment (CCA)
mechanism of the host detects no channel activity during the
past slot (a fixed period), the counter is decreased by one.
When the counter reaches zero, the backoff procedure is finished.
Now consider the scenario where several neighbor hosts
hear a broadcast from host X. There are several reasons for
collisions to occur. First, if the surrounding medium of X
has been quiet for enough long, all Xs neighbors may have
passed their backoff procedures. Thus, after hearing the
broadcast message (and having passed the DIFS period), they
may all start rebroadcasting at around the same time. This
is especially true if carriers can not be sensed immediately
due to such as RF delays and transmission latency. Second,
because the RTSKTS forewarning dialogue is not used in
a broadcast transmission, the damage of collision is more
serious. Third, once collision occurs, without collision detection (CD), a host will keep transmitting the packet even
if some of foregoing bits have been garbled. And the longer
the packet is, the more the waste.
The above problem is not addressed in the ordinary IEEE
802.11 MAC activities, possibly because the one-to-many
behavior is not considered therein. For all the above reasons,
we believe that the broadcast storm problem deserves serious
studies in a MANET environment.

Clearly, the contention is expected to be higher as n increases. We derived a simulation by randomly generating n
hosts in As transmission range. We observe the probability cf(n, k) that k hosts among these n hosts experience no
contention in their rebroadcasting (cf stands for contentionfree). The results are shown in Fig. 4. We can see that
the probability of all n hosts experiencing contention (i.e.,
cf (n,O)) increases quickly over 0.8 as n 2 6. So the more
crowded the area is, the more serious the contention is. On
the other hand, the probability of having one contention-free
host (i.e., cf (n, 1)) drops sharply as n increases. Further,
it is very unlikely to have more contention-free hosts (i.e.,
cf (n, k) with k 2 2). Note that having k = n - 1 contentionfree hosts implies having n such hosts, so cf (n,n - 1) = 0.








One approach to alleviate the broadcast storm problem is to

inhibit some hosts from rebroadcasting to reduce the redundancy, and thus contention and collision. In the following,
we present five schemes to do so. These schemes differ in
how a mobile host estimates redundancy and how it accumulates knowledge to assist its decision. Except the last
scheme, which relies on some local connectivity information, all schemes operate in a fully distributed manner.



An intuitive way to reduce rebroadcasts is to use probabilistic rebroadcasting. On receiving a broadcast message for
the first time, a host will rebroadcast it with probability P.
Clearly, when P = 1, this scheme is equivalent to flooding.

same message is heard. Then Hs rebroadcast will provide

additional coverage no more than nr2 - INTC(d,,,i,). In our
distance-based scheme, we will use dmin as the metric to
evaluate whether to rebroadcast or not. If dmin is smaller than
some distance threshold D, the rebroadcast transmission of
H is cancelled. The scheme is formally derived below. In
Section 4, we will test several possible values of D.

Note that to respond to the the contention and collision

problems addressed in Section 2.2.3, we should insert a small
random delay (a number of slots) before rebroadcasting the
message. So the timing of rebroadcasting can be differentiated.



When a host tries to rebroadcast a message, the rebroadcast

message may be blocked by busy medium, backoff procedure, and other queued messages. There is a chance for the
host to hear the same message again and again from other
rebroadcasting hosts before the host actually starts transmitting the message.
In Section 2.2.1 we have shown that EAC(k), the expected additional coverage after heard the message k times,
is expected to be lower when k increases. We can prevent a
host from rebroadcasting when the expected additional coverage of the hosts rebroadcast becomes too low. This is what
the counter-based scheme is based on. Specifically, a counter
c is used to keep track of the number of times the broadcast message is received. A counter threshold C is chosen.
Whenever c 2 C, the rebroadcast is inhibited. The scheme is
formally derived below.

Initialize counter c = 1 when a broadcast message msg

is heard for the first time. In S2, if msg is heard again,
interrupt the waiting and perform S4.

s2. Wait for a random number of slots. Then submit msg

for transmission and wait until the transmission actually starts.
s3. The message is on the air. The procedure exits.
s4. Increase c by one. If c < C, resume the interrupted
waiting in S2. Otherwise c = C, proceed to SS.
S5. Cancel the transmission of msg if it was submitted in
S2. The host is prohibited from rebroadcasting msg.
Then exits.



When a broadcast message msg is heard for the first

time, initialize dmi,, to the distance to the broadcasting
host. If d,,,i,, < D, proceed to S5. In S2, if msg is heard
again, interrupt the waiting and perform S4.


for transmission and wait until the transmission actually starts.
s4. Update dmin if the distance to the host from which tn.rg
is heard is smaller. If dmin < D, proceed to S5. Otherwise, resume the interrupted waiting in S2.
SS. Cancel the transmission of msg if it was submitted in
S2. The host is inhibited from rebroadcasting msg.
Then exits.
Below, we comment on how to obtain the distance information. One possibility is to estimate from the signal
strength on which a message is received. Specifically, let
Pt and P, be the power levels on which a message is sent and
received, respectively. According to [26], Pr = P,(a)cz,
where n, ci , and c2 are constants related to physical environment, the carriers wavelength, and antenna gains, respectively. Since P, and Pt can be measured, the distance d can
be estimated from this formula.
Having understood the relationship between the distance
and the power, we can even directly replace the role of distances by signal strengths by establishing a signal-strength
threshold. As a comment, we note that signal strength information was also used in [12] to facilitate routing in a


In the previous scheme, a counter is used to decide whether

to drop a rebroadcast or not. In this scheme, we will use the
relative distance between hosts to make the decision.
For instance, suppose host H heard a broadcast message
from S for the first time. If the distance, say d, between H
and S is very small, there is little additional coverage Hs
rebroadcast can provide. If d is larger, the additional coverage will be larger. In the extreme case, if d = 0, the additional coverage is 0 too. Earlier, we have analyzed the
relationship between the distance d and the additional coverage rcr2 - INTC(d). So this can be used as a metric by H
to determine whether to rebroadcast or not.
Now, suppose that before a rebroadcast message is actually sent, host H has heard the same message several times.
Let dmin be the distance to the nearest host from which the





Earlier we have used the number of times that a broadcast

message has been heard or the distances to sending hosts as
our rebroadcasting metrics. If we can acquire the locations
of those broadcasting hosts, it is even possible to estimate the
additional coverage more precisely. Such an approach may
be supported by positioning devices such as GPS (Global
Positioning System) receivers [17]. We note that location
information was also used to facilitate route discovery in a
MANET [5, 181.
Without loss of generality, let a hosts location be (0,O)
(here we use q-coordinate to facilitate our presentation; in
fact, devices such as GPS receivers can provide 3-D locations in longitude, latitude, and altitude). Suppose a host has
received the same broadcast message from k hosts located at

Figure 5: Scenarios of using convex polygons to determine whether to rebroadcast or not. (a) Host X is within the triangle
formed by three sending hosts. (b) X is outside of the polygon. (c) Analysis of maximum loss of additional coverage on using
the polygon test.
(xl>Yl),(-xz,Y2)r... , (y,yk). We can calculate the additional
area that can be covered if the host rebroadcasts the message.
Let AC((m,yd, (QY~), . . . , (y,yk)) denote the additional
coverage divided by x3. Then we can compare this value to
a predefined coverage threshold A (0 < A < 0.61) to determine whether the receiving host should rebroadcast or not.
The scheme is formally derived below.
Sl. When a broadcast message m.rg is heard for the first
time, initialize AC to the additional coverage provided
by the hosts rebroadcast. If AC < A, proceed to S5.
In S2, if m.rg is heard again, interrupt the waiting and
perform s4.
for transmission and wait until the transmission actually starts.
s4. Update AC. If AC < A, proceed to S5. Otherwise, resume the interrupted waiting in S2.
S5. Cancel the transmission of msg if it was submitted in
S2. The host is inhibited from rebroadcasting msg.
Then exits.
One obstacle in using the above scheme is the cost of
calculating AC, which is related to calculating many intersections among several circles. This problem is difficult already when there are four circles. One possibility is to use a
grid-filling approximation to estimate its value.
An alternative is using a convex polygon to determine
whether a rebroadcast should be carried out or not. For instance, suppose host X received a broadcast message three
times from hosts A, B, and C. In Fig. 5(a), it shows that if X
is in the convex polygon formed by A, B, and C, the additional coverage of Xs rebroadcast is small or even none. On
the contrary, as shown in Fig. 5(b), if X is not in the polygon,
it is likely that the rebroadcast will provide more additional
coverage (shaded area). As a result, we can allow the host to
rebroadcast only if it is not located within the convex polygon.
The following is to justify the reason of the above convex
approximation through geometry calculation. We show that


if the polygon test prevents a host (X) from rebroadcasting

(X is within the polygon), at most 22% of coverage will be
lost in the extreme case. Observe that the additional coverage will be the largest when X is located on some boundary
of the polygon. Let A and B be the two end points of that
boundary. We see that the additional coverage will be the
largest if A and B are each separated from X by the transmission range r as illustrated in Fig. 5(c). It is not hard to
find that the size of shaded area in Fig. 5(c) is

Now it is reasonable to say that if a host is in the convex

polygon formed by the locations of previously sending hosts,
the additional coverage that the host can provide is well below 22%.



The above schemes are based on statistical and geometric

modeling to estimate the additional coverage of a rebroadcast. In the following, we show how to develop an approach
based on graph modeling. Specifically, we will adopt the
clustering concept by [15] to derive our scheme. Note that
the clustering technique has been used to solve other problems in MANETs (e.g., traffic coordination [13], routing
[ 131, and fault-tolerance [ 11).
We first summarize the cluster formation algorithm proposed in [15]. It is assumed that a host periodically sends
packets to advertise its presence. Thus any host can determine its connectivity with other hosts on its own. Each host
has a unique ID. A cluster is a set of hosts formed as follows. A host with a local minimal ID will elect itself as a
cluster head. All surrounding hosts of a head are members
of the cluster identified by the heads ID. Within a cluster, a
member that can communicate with a host in another cluster
is a gateway. To take mobility into account, when two heads
meet, the one with a larger ID gives up its head role. Fig. 6
shows an example of a clustered MANET.


rate is one broadcast per second, and the broadcasting host

is randomly picked.
The performance metrics to be observed are:

Figure 6: An example of a clustered MANET. Clustering

provides a host the connectivity information of its surroundings.

Now back to our broadcast storm problem. We assume

that clusters have been formed in the MANET and will be
maintained regularly by the underlying cluster formation algorithm. In a cluster, the heads rebroadcast can cover all
other hosts in that cluster if its transmission experiences no
collision. Apparently, to propagate the broadcast message to
hosts in other clusters, gateway hosts should take the responsibility. But there is no need for a non-gateway member to
rebroadcast the message. Our cluster-based scheme is formally developed as follows.
Sl. When the broadcast message m.sgis heard, if the host
is a non-gateway member, the rebroadcast is inhibited
and the procedure exits. Otherwise, the host is either a
head or a gateway. Proceed to S2.

Use any of the probabilistic, counter-based, distancebased, and location-based schemes to determine whether
to rebroadcast or not.

Note that the above scheme is derived by incorporating

the schemes developed earlier into it. Step Sl is to eliminate non-gateway members from rebroadcasting. As a cluster may still have many gateway members, step S2 then further utilizes other knowledge (such as additional coverage)
to reduce the number of rebroadcasts.



We have developed a simulator using C++. Central to the

simulator is a discrete-event engine designed to simulate systems that can be modeled by processes communicating through
signals. A simplified version of MAC specification in IEEE
Std 801.11 is referenced to simulate CSMAKA behavior
among hosts.
The parameters that are fixed in our simulations are the
transmission radius (500 meters), the packet size (280 bytes),
the transmission rate (IM bits per second), and DSSS physical layer timing (PLCP overhead, the slot time, DIFS, backoff window size, as suggested in IEEE Std 801.11).
A geometric area called a map which contains one hundred mobile hosts is simulated. A map can be of size 1 x 1,
3 x 3,5 x 5,7 x 7, or 10 x 10 units, where a unit is of length
500 meters (equal to the transmission radius). The arrival


REachability (RE): the number of mobile hosts receiving the broadcast message divided by the total number of mobile hosts that are reachable, directly or indirectly, from the source host.
Saved ReBroadcast (SRB): (r - t)/r, where r is the
number of hosts receiving the broadcast message, and
t is the number of hosts actually transmitted the message.
Average latency: the interval from the time the broadcast was initiated to the time the last host finishing its

The simulation results of the probabilistic, counter-based,

distance-based, and location-based schemes are shown in
Fig. 7, Fig. 8, Fig. 9, and Fig. 10, respectively. Each point
in these figures represents our result obtained from a simulation run containing 10,000 broadcast requests. Fig. 7(a)
shows the observed RE and SRB associated with the probabilistic scheme. In a small map (which implies a dense host
distribution), a small probability P is sufficient to achieve
high reachability, while a larger P is needed if the host distribution is sparse. The amount of saving (SRB) decreases,
roughly proportionally to (1 - P), as P increases. Also, the
performance of broadcasting by flooding can be found at
the position where the probability P = 1. Fig. 7(b) shows
the broadcast latency at various P values. Interestingly, a
MANET with sparse hosts tend to complete broadcasting in
a faster speed than one with denser hosts.
Fig. 8 shows the performance of the counter-based scheme.
From Fig. 8(a), we can see that the reachability RE in fact
reaches about the same level as that of the flooding scheme
when the counter threshold C 2 3. However, various levels
of saving (SRB) can be obtained over the flooding scheme,
depending on the density of hosts in a map. For instance, the
scheme in higher density maps (e.g., 1 x I, 3 x 3, or 5 x 5)
can offer 27 - 67% saving at C = 3, while in lower density maps (e.g., 7 x 7, 9 x 9, or 11 x 11) 8 N 20% saving.
When C is larger (say 6) and the map is sparse (say 11 x 1l),
the amount of saving decreases sharply. This is because the
number of neighbors of a host tends to be small (2.4 neighbors per host in an 11 x 11 map), and thus it is less likely that
a host will receive the same broadcast message more than C
times. So a threshold C of 3 or 4 is an appropriate choice.
The performance of the distance-based scheme is shown
in Fig. 9. Note that the values of threshold D in the figure are chosen purposely in a way so as to make a reasonable comparison between the distance-based scheme and the
counter-based scheme. We mainly use the additional coverage as our measurement. For instance, when C = 2, we know
that EAC(2) x 0.187 from Fig. 3. So we should choose
a value of D to match the value of addition coverage, i.e.,




2 6o?h


%Y 0
























Figure 7: Performance of the probabilistic scheme. (a) Probability P vs. reachability RE (shown in lines) and saved rebroadcast
SRB(shown in bars). (b) Probability P vs. average latency.






6 0.07


E 0.06
& 0.05


E 0.04
















Figure 8: Performance of the counter-based scheme. (a) Counter threshold C vs. reachability RE (shown in lines) and saved
rebroadcast SRB(shown in bars). (b) Counter threshold C vs. average latency.





x 0.10































Figure 9: Performance of the distance-based scheme. (a) Distance threshold D vs. reachability RE (shown in lines) and saved
rebroadcast SRB (shown in bars). (b) Distance threshold D vs. average latency.








a 0.05


s%I 0.04



6 0.03








A 0.1871 0.0913

0.0469 0.0251










Figure 10: Performance of the location-based scheme. (a) Coverage threshold A vs. reachability RE (shown in lines) and saved
rebroadcast SRB (shown in bars). (b) Coverage threshold A vs. average latency.






6 0.035


B 0.030





0.1871 0.0913

0.0469 0.0251 0.0134

















0.1871 0.0913


0.0251 0.0134


Figure 11: The result of cluster-location-based scheme. (a) The coverage threshold A vs. SRB and RE. (b) The coverage
threshold A vs. average latency.
(d- -INTC(D))/(d)
R 0.187. This gives a D = 147. The
other D values along the x-axis in Fig. 9 are derived in a similar way.
By comparing Fig. 8(a) and Fig. 9(a), we see that although the distance-based scheme can provide a better reachability, not much of rebroadcasts are saved. In fact, the SRB
values are worse than those of the counter-based scheme.
This also incurs a higher broadcast latency than that of the
counter-based scheme, as shown in Fig. 8(b) and Fig. 9(b).
The reason that the distance-based scheme saves less among
of rebroadcasts than the counter-based scheme does is as follows. In the distance-based scheme, a host may have heard
a broadcast message so many times but still rebroadcast the
message because none of the transmission distances are below a given distance threshold, where the rebroadcast would
have been canceled if the counter-based scheme is used.
Fig. 10 illustrates the performance of the location-based
scheme at various threshold values of A. Note that the values
of A in the figure are chosen purposely for a similar reason
as that described in the distance-based scheme. Overall, this
scheme reveals the best performance in all aspects over all
other schemes because it utilizes the exact information to
calculate the additional coverage. It provides high levels of
reachability, while remaining a good amount of saving. Even
in sparse maps, it still provides a relatively higher level of
saving. Because of the saving, the broadcast latency is also
the best among all schemes.
Fig. 11 shows the performance of the cluster-based scheme
where the location-based scheme is incorporated in its step
S2. Compared to the original location-based scheme, the
cluster-based scheme apparently saves much more rebroadcasts and leads to shorter average broadcast latencies. Unfortunately, the reachability is unacceptable at sparser areas
(such as 7 x 7, 9 x 9, and 11 x 11 maps). This is probably
because when the number of hosts participating in rebroad-


Figure 12: An example of hidden terminal problem in the

cluster-based scheme. Hosts A, C, and D are gateways, B
and E are heads. Non-gateway members are not shown.
casting is reduced (mostly by step Sl of the cluster-based
scheme), the collisions caused by the hidden terminal problem will significantly reduce the chance of successful transmissions. For example, Fig. 12 illustrates a scenario where
a broadcast message was propagated through gateway A to
head B. After B rebroadcast the message, gateways C and D
will try to rebroadcast the message. Unfortunately, because
C and D can not hear each other, the two rebroadcasts from
C and D are very likely to collide at E. Such loss of a message may significantly reduce the reachability, especially in
a sparse map.
It is also worth mentioning that there is an anomaly that
the average latencies in the 1 x 1 map is pretty low (see
Fig. 11(b)). This is because in our simulations there are in
average only 1.2 cluster heads in the map, and thus when
there is only one head, the broadcast will be completed very
quickly because there is no gateway.
We have also studied how our schemes perform under
different load conditions. We varied the arrival rates at 1,
20,40,60, 80, and 100 broadcasts per second. Due to space
limit, only the result of the location-based scheme in a 5 x 5
map is shown in Fig. 13. From the figure, we observe that a

A 0.1871 0.0913


0.0251 0.0134









Figure 13: Performance of the location-based scheme in a 5 x 5 map under different load conditions. (a) Coverage threshold A
vs. RE (shown in lines) and SRB (shown in bars). (b) Coverage threshold A vs. average latency.
heavier load will result in a lower reachability. This is probably due to heavier collisions. At higher load, the reachability
even degrades as the coverage threshold A becomes smaller,
which contradicts with our intuition. This is because when
the threshold A is low, less rebroadcasts are saved, which
leads to more collisions. This also suggests that one consider
dynamically adjusting the threshold A so that the scheme can
adapt to different load conditions.


This paper has identified an important issue in a MANET,

the broadcast storm problem. We have demonstrated, through
analyses and simulations, how serious this problem could
be. Several schemes, namely probabilistic, counter-based,
distance-based, location-based, and cluster-based schemes,
have been proposed to alleviate this problem. Simulation
results based on different threshold values are presented to
verify and compare the effectiveness of these schemes. As
compared to the basic flooding approach, a simple counterbased scheme can eliminate many redundant rebroadcasts
when the host distribution is dense. If location information is available through devices such as GPS receivers, the
location-based scheme is the best choice because the scheme
can eliminate even more redundant rebroadcasts under all
kinds of host distributions without compromising the reachability. Future research could be on how to incorporate these
schemes with other MANET protocols, such as reliable broadcast, multicast, and routing protocols.

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