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.docx   GOSSIP BASED RANDOM ROUTING IN AD HOC NETWORKS.docx (Size: 170.69 KB / Downloads: 57)

Presented By:
R.Saravanan 1
Naresh Sammeta 2
H.O.D Dept of CSE, P.S.N.A. College of Engg and Tech., Tamilnadu, India
Dept of CSE, P.S.N.A. College of Engg and Tech., Tamilnadu, India


Many ad hoc routing protocols are based on some variant
of flooding. Despite various optimizations of flooding,
manyrouting messages are propagated unnecessarily. We
propose a gossiping-based approach, where each node
forwards amessage with some probability, to reduce the
overhead of therouting protocols. Gossiping exhibits
bimodal behavior in sufficiently large networks: in some
executions, the gossip diesout quickly and hardly any
node gets the message; in theremaining executions, a
substantial fraction of the nodes getsthe message. The
fraction of executions in which most nodes getthe message
depends on the gossiping probability and the topology of
the network. In the networks we have considered,using
gossiping probability between 0.6 and 0.8 suffices to
ensure that almost every node gets the message in almost
everyexecution. For large networks, this simple gossiping
protocol uses up to 35% fewer messages than flooding,
with improved performance. Gossiping can also be
combined with various optimizations of flooding to yield
further benefits. Simulations show that adding gossiping
to AODV results in significant performance improvement,
even in networks as small as 150 nodes. Our results
suggest that the improvement should be even more
significant in larger networks.
Keywords-component: Ad hoc networks, gossiping,
percolation theory, phase transition, routing.


An ad hoc network is a multi-hop wireless network with
no fixed infrastructure. MIT Rooftop networks and sensor
networks are two examples of networks that might be
implemented using the ad hoc networking technology. Ad
hoc networks can be usefully deployed for
communication in applications such as disaster relief and
battlefield situations. In ad hoc networks, the power
supply of individual nodes is limited, wireless andwidth
is limited, and the channel condition can vary greatly.
Moreover, since nodes can be mobile, routes may
constantly change, requiring frequent route discovery
among communicating parties. Thus, to enable efficient
communication, robust routing protocols must be
developed. Many ad hoc routing protocols have been
proposed. Some, such as LAR , GPSR, and DREAM
assume that nodes are equipped with GPS hardware and
thus know their locations; others, such as DSR, AODV,
ZRP, and TORA, do not make this assumption.
Essentially all protocols that do not use GPS (and some
that do, such as LAR and DREAM) make use of flooding,
usually with some optimizations. Despite the
optimizations, in routing protocols that use flooding,
many routing messages are propagated unnecessarily. In
this paper, we show that gossiping”essentially, tossing a
coin to randomly decide whether or not to forward
message” can be used to significantly reduce the number
of routing messages sent It follows from results in
percolation theory[5], that gossiping exhibits a certain
type of bimodal behavior. Let the gossip probability be
?S(P) . Let be the fraction of executions where gossiping
with probability dies out, and let ?R (P) bethe fraction of
nodes getting the message when gossiping does not die
out. Then, in sufficiently large nice graphs (where
nice graphs include regular graphs and random graphs)
the gossip quickly dies out in 1- ?S(P) of the executions
and, in almost all of the fraction ?S(P) of the executions
where the gossip does not die out, a fraction ?R (P) of the
nodes get the message. Moreover, in many cases of
interest, ?R(P) is close to 1. Thus, in almost all executions
of the algorithm, either hardly any nodes receive the
message, or most of them do. Ideally, we could make the
fraction of executions where the gossip dies out relatively
low while also keeping the gossip probability low, to
reduce the message overhead. The goal of this paper is to
investigate the extent to which this can be done. Our
results show that, by using appropriate heuristics , we can
save up to 35% message overhead compared to flooding.
Furthermore, adding gossiping to a protocol such as
AODV not only reduces the number of messages sent, but
also results in improved network performance in terms of
end-to-end latency and throughput. We expect that the
various optimizations applied to flooding by other
protocols (for example, the cluster-based scheme) can
also be usefullycombined with gossiping to get further
performance improvements. We are certainly not the first
to use gossiping in networking applications. For example,
it has been applied in networked databases to spread
updates among nodes [5] and to multicasting
[3]. Gossiping proceeds by choosing some set of nodes at
random to which to gossip. Our problem is to find routes
to different nodes.We show by simulation that even n
networks with 150 nodes only, adding gossiping to
AODV can result in significant performance
improvements on all standard metrics. We expect that this
improvement will be even more significant in larger
networks. Section 7 concludes our paper.
talks about the probability that a node receives and
forwards the message in a given execution of the
algorithm. The intuition behind the equality of ?S 0(p) and
?F 0 (p) is easy to explain. A gossip initiated by a source n0
dies out if there is a set N of nodes that disconnects from
the rest of the graph; that is, there is a set of N nodes such
that, for infinitely many n nodes,
every path from n0 to n goes through a node in N . Thus,
?S 0(p) is the probability that there is no disconnecting set
N such that none of the nodes in N forwards the message.
(Note that N could consist of the singleton node n0 itself.)
Similarly, the probability
?F 0(p) that a random node receives and forwards the
message is precisely the probability that there is no set N1
such that N1 disconnects n from n0 and none of the nodes
in N1 forwards the message. Therefore, ?S
0(p) = ?F 0 (p) = ?0(p) . It follows from these results that, in
an execution where the message does not die out, the
probability that a random node receives the message is
?0(p)/p, since receiving the message is independent of
forwarding it. Thus, in terms of the notation used in the
introduction, ?S(p) = ?0(p) and ?R 0 (p) ?0(p)/p.


Since flooding is a basic element in many of the ad
hocrouting protocols, as mentioned in Section 1, we start
by comparing gossiping to flooding. Our basic gossiping
protocol is simple. A source sends the route request with
probability 1. When a node first receives a route request,
with probability it broadcasts the request to its neighbors
and with probability -p it discards the request; if the
node receives the same route request
again, it is discarded. Thus, a node broadcasts a given
route request at most once. This simple protocol is called
GOSSIP1( ).GOSSIP1 has a slight problem with initial
conditions. If the source has relatively few neighbors,
there is a fair chance that none of them will gossip and
that the gossip will die. To make sure this does not
happen, we gossip with probability 1 for the
first k- hops before continuing to gossip with probability
.We call this modified protocol GOSSIP1(p,k).
Theorem2.1: For all p? 0 , for all infinite regular graphs ,
and for almost all (i.e., a measure 1 subset) of the infinite
random graphs constructed as above, if GOSSIP1(p,0) is
used by every node to spread a message, then there is a
well-de fined probability ?S 0(p)<1 that the message
reaches infinitely many nodes. Moreover, in an execution
where the message reaches infinitely many nodes, the
probability ?F 0(p) that a node receives the message and
forwards it is equal to ?S0 (p). Note that the probability of
a message dying out (i.e., not spreading to infinitely many
nodes) is averaged over the executions of the algorithm.
That is, the theorem says that if we execute the algorithm
repeatedly, the probability that a message does not die out
in any given execution is ?S 0(p). On the other hand, ?F
We performed a large number of experiments to
investigate the behavior of gossiping. We summarize
some of the more interesting results here. We assumed an
ideal MAC layer for these experiments because we
wanted to decouple the effect of the MAC layer from the
effect of gossiping; using IEEE 802.11 MAC leads to
similar results. An ideal MAC layer is one that is not
subject to packet loss. When we consider more realistic
scenarios in Section 5, we use the IEEE 802.11 MAC
layer. In this section, we focus on regular graphs and the
random graphs discussed in the previous section. We
focus here on phase-transition phenomena in medium
sized networks of roughly 1000 nodes and larger
networks of 1 000 000 nodes. Of course, with larger
networks, the phase-transition phenomenon is even more
marked. Although networks of more than 1000 nodes are
not currently practical, given that hardware costs keep
decreasing, we believe that they may well exist in the near
future; for example, some researchers have envisioned
large networks involving smart dust. Our first set of
experiments involves medium-sized networks with 1000
nodes. We start by considering a 20-row by 50-column
grid (i.e., a regular graph of degree 4.For example, the
average erformance of GOSSIP1(0.65,4) is shown in Fig.
1©. As the graph shows, at distance 40, on average 58%
of the nodes got the message. However, in this case, the
graph is somewhat misleading. The averaging is hiding
the true behavior. As we would expect from Theorem II.1,
there is bimodal behavior. This is illustrated in Fig. 1(d).
If we consider nodes at distance 15“45 (so as to ignore
initial effects and boundary effects), in 14% of the
executions, fewer than 10% of the nodes get the message;
in 19% of the executions, fewer than 20% of the nodes get
the message; in 59% of the executions, more than 80% of
the nodes get the message; and in 41% of the executions,
more than 90% of the nodes get the message. If we lower
the gossip probability further, we get the same bimodal
behavior; all that changes is the fraction of rectangular
region; this results in a network with average degree 10.
In this network, it suffices to gossip with probability 0.65
to ensure that almost all nodes get the message in almost
all executions. All the graphs above show a marked drop
off in probability for nodes that are close to the boundary.
This is not just an effect of averaging; this drop off occurs
in almost all executions of the algorithm. The drop-off is
due to two related
boundary effects executions in which all nodes and no
nodes get the message. The drop off is fairly rapid. For
example, Fig. 1(e) and (f) describe the situation.


The results of the previous section suggest an obvious
way that gossiping can be applied in ad hoc routing.
Rather than flooding, we use GOSSIP1 (p,k) with p
sufficiently high to guarantee that almost all nodes will
receive the message in almost all executions. We can
practically guarantee that the destination node receives the
message, while saving a faction of 1-p messages. In cases
of interest, where the threshold probability is in the range
0.65“ 0.75, this means we can ensure that all nodes get the
message using 25%“35% fewer messages than flooding.
Notice that, if the network is congested and every node
has a congestion dropping probability , then to obtain the
same results, the broadcast probability needs to be
min(p/(1 - f),1) . If congestion is very localized, then we
can simply use because it is not likely to change the
outcome of a given run of gossiping. However, the
general interaction between gossiping and congestion is a
topic that deserves further study.

4.1 Two-Threshold Scheme

In many cases of interest, a gossip protocol is run in
conjunction with other protocols. If the other protocols
maintain fairly accurate information regarding a nodeâ„¢s
neighbors, we can make use of this information to further
improve the performance of GOSSIP1 by a simple
optimization. In a random network, unlike the grid, a node
may have very few neighbors. In this case,
the probability that none of the nodeâ„¢s neighbors will
propagate the gossip is high. In general, we may want the
gossip probability at a node to be a function of its degree,
where nodes with lower degree gossip with higher
probability. To show the effect of this improvement, we
consider a special case here: a protocol with four
parameters, p1, k, p2 , and n . As in GOSSIP1, p1 is the
typical gossip probability, but gossiping happens with
probability 1 for the first k hops. The new features are p2
and n ; the idea is that the neighbors of a node with fewer
than n neighbors gossip with probability p2 > p1 . That is,
if a node has fewer than neighbors, it instructs its
immediate neighbors to broadcast with probability p2
rather than p1. Call this modified protocol
GOSSIP2(p1,k,p1,n). probability, the probability that at
least one of them will gossip is high. This is not the case
if it has few neighbors. GOSSIP2 is not of interest in
regular networks. However, in random networks which
typically have some sparse regions, it can have a
significant impact.1000-node random network with
average degree 8, first considered in Fig. 2,
GOSSIP2(0.6,4,1,6) has better performance than
GOSSIP1(0.75,4), as shown in Fig. 5, while using 4% less
messages than GOSSIP1(0.75,4). Only when p ? 0.8does
GOSSIP1(p,4) begin to have the same performance as
GOSSIP2(0.6,4,1,6); however,
GOSSIP1(0.8,4) uses
13% more messages than GOSSIP2(0.6,4,1,6). There may
be other combinations of parameters for GOSSIP2 that
give even better performance; we have not checked
exhaustively. The key point is that using a higher
threshold for successors of nodes with low degree seems
to significantly improve performance.

4.2 Preventing Premature Gossip Death

As we have seen, the real problem with gossiping is that,
if we gossip with too low a probability, the message may
die out in a certain fraction of the executions. Measures
can be taken to prevent this (for example, having
successors of nodes with low degree gossip with a higher
probability), but, unfortunately, there is no way for a node
to know if a message is dying out.Nevertheless, a node
may get some clues. If a node with n neighbors receives a
message and does not broadcast it, but then does not
receive the message from at least m neighbors within a
reasonable timeout period, it broadcasts the message to all
its neighbors. The obvious question here is what m should
be. If m is chosen too large, then we may end up with too
many messages. Our experiments show that we actually
get the most significant performance improvement by
taking m=1 . Let GOSSIP3(p,k,m) be just like
GOSSIP1(p,k) , except for the following modification. A
node that originally did not broadcast a received message
(because its coin landed tails), but then did not get the
message from at least m other nodes within some imeout
period, broadcasts message immediately after the timeout
period. (The choice of timeout period can be taken quite
small. We discuss this issue in details in Section 6.) It
may seem that such rebroad casting can significantly
effect the latency of the message. However, as the
experiments discussed below show, if the parameters are
chosen correctly, latency is not a problem at all.
As Fig. 6 shows, the performance of GOSSIP3(0.65,4,1)
is even better than that of GOSSIP1(0.75,4). However,
GOSSIP3(0.65,4,1) sends only 67% of the messages sent
by flooding. By way of contrast, GOSSIP1(0.75,4) sends
75% of the messages sent by flooding. Thus, we get better
performance using GOSSIP3 while sending 8% fewer
messages. To examine the effect of GOSSIP3 on latency,
we recorded the number of timeout intervals a message
experienced, using a variable L , which was augmented
every time a message was forwarded after a timeout.
Among all the messages sent by GOSSIP3(0.65,4,1), only
2% have L >= 1 . Among these messages with L>= 1 ,
95% of them have L <= 2 . Thus, it seems that latency is
not significantly affected by this modification.

4.3. Retries

However, when using a gossiping protocol, there is
always a possibility that a route will not be found even if
it exists. Of course, there is a simple solution to this
problem: simply retry the protocol. Thus, for example, the
probability of finding a route within two attempts to a
node at distance 25 using GOSSIP1(0.65,4) in the random
network with average the out degree 8 is 0.95: the
probability of a node not receiving a message in any given
execution of the protocol is 0.23, and executions are
independent. With retries, the bimodal message
distribution works significantly to our advantage. As we
observed, with GOSSIP1(0.65,4), in 72% of the
executions, almost all nodes get the message. If we pick a
destination at random, in those executions where almost
all nodes get the message, the destination is likely to get
the message and a retry will not be necessary. On the
other hand, in tho se executions
where hardly any nodes got the message, a retry will
probably be necessary. However, such failing gossip
attempts do not involve too many transmissions, since
most nodes do not get the message in the first place. Of
course, retries increase latency, even if they do not
significantly increase the number of messages sent. This
is especially true in large networks, where Note that
parameters , Pr, and can be set adaptively as follows so as
to minimize the number of messages. Let Ns denote the
mean number of acknowledgments received if a route
reply is successfully received, and let Nf denote the
average number of acknowledgments received if a route
reply is not received. ¢ If both Nf < c and Ns <c , this
suggests that is set too high, so we decrement by 1.
¢ If both Nf >= c and Ns >=c this suggests that gossiping
does not die out after hops whether or not it is ltimately
successful, so we increment by 1.¢ If Nf >= c and Ns>= c,
this suggests that c and h are set appropriately. We can try
decrementing Pr slightly, say by 0.05, to see if we can still
obtain this behavior while reducing the number of
acknowledgments that need to be sent.

4.3. Zones

One of the best-known optimizations to flooding is the
zone routing protocol (ZRP) [12]. In ZRP, each node u
maintains a socalled zone, which consists of all the nodes
that are at most p hops away from , for some
appropriately chosen zone radius . A node that is exactly
hops away from is called a peripheral node of . A node
proactively tries to maintain complete routing tables for
all nodes in its zone. Initially, a node discovers who its
neighbors are and then broadcasts the identity of its
neighbors to its zone (by using flooding up to hop count ).
Then each time it discovers a change (i.e., that it has lost
or gained a neighbor), it broadcasts an update. This
procedure ensures that a node has an accurate picture of
its zone. If a source wants to send to a destination in its
zone, it simply routes the message directly there, since it
already knows the route. Otherwise, it sends a route
request query to the peripheral nodes in its zone. If the
destination is in a peripheral nodeâ„¢s zone, the peripheral
node replies with the route to the query originator.
Otherwise, it forwards the query to its peripheral nodes,
which in turn forward it to their peripheral nodes, and so
on. In the context of ZRP, there are two advantages of
maintaining a zone. First, if a node is in the zone, looding
is unnecessary; a message can be sent directly to the
intended recipient, saving much control traffic. This
brings about a significant improvement in overall
performance if a substantial fraction of nodes are in the
zone (which is likely to be true in a small network, but far
less likely in a large one). Second, if we want to send a
message outside the zone, we can multicast to the
boundary of the zone (or a subset of the nodes on the
boundary), which can be a significant saving over
flooding. However, there is a tradeoff in choosing the size
of the zone: a larger zone benefits more Comparing Fig.
7(b) to Fig. 7(a), we see that using a zone radius of 4 with
gossiping probability 0.65 in the random network with the
average degree 8 improves the performance by
only a few percent over most of the distances. However, it
does ameliorate the back-propagation effect. As shown in
Fig. 7©, increasing the zone radius to 8 does not
significantly improve the limiting performance, but it has
an even more beneficial effect on the back-propagation
problem. The situation is much different for
smaller networks. Here zones can have a significant
impact. For example, if we use gossip probability 0.65 in
a random network with 100 nodes and average degree 13,
the network is too small for the bimodal effect to show
up. However, the backpropagation problem is significant.


How much does gossiping really help in practice? That
depends, of course, on issues like the network topology,
mobility, and how frequently messages are generated. We
believe that in larger networks with high mobility many of
the optimizations discussed in the literature will be much
less effective. (We discuss this point in more detail below
in the context of AODV.) In this case, flooding will occur
more frequently, so gossiping will be particularly
advantageous. However, as our results show, gossiping
can provide significant advantages even in small
networks. To test the impact of gossiping, we considered
AODV, one of the most-studied ad hoc routing protocols
in the literature. We compared pure AODV to a variant of
AODV that uses gossiping instead of flooding whenever
AODV would use flooding. We do not have the resources
to simulate the protocols in very large networks.
However, our results do verify the intuition that, with high
mobility (when flooding will be needed more often in
pure AODV), gossiping can provide a significant

5.1. A Brief Overview of AODV

Using AODV, the first time a node requests a route to
node v , it uses an expanding-ring search to find the route.
That is, it first tries to find the route in a neighborhood of
small radius, by flooding. It then tries to progressively
find the route in neighborhoods of larger and larger
radius. If all these attempts fail, it resorts to flooding the
message through the whole network. The exact choice of
the neighborhood radii to try is a parameter of AODV.
Typically, not too many radii are considered before
resorting to flooding throughout the network. AODV also
maintains routing tables in the network nodes where it
stores the routes after they have been found. If AODV
running at node gets any packet with source and
destination, the route in the routing table will be tried
first. If any node w on the route from u to detects that the
link to the next hop is down, then w generates a route
error (RERR) message, which is propagated back to .
When u receives the RERR message, it deletes the route
to from its routing table.

5.2. GOSSIP3 in AODV

Our application traffic is CBR (constant bit rate). The
source destination pairs (connections) are chosen
randomly. The application packets are all 512 bytes. We
assumed a sending rate of 2 packets/s and 30 connections.
For mobility, we use the random-waypoint model [5] in a
rectangular field, as modified by Yoon [30]; to prevent
mobility from going asymptotically to
zero, the minimal speed is set to 1. In the simulations, 150
nodes are randomly placed in a grid of 3300 m× 600 m;
we chose this layout because in some sense it provides a
worst-case estimate of the performance of gossiping. For
this layout the gossip threshold
is approximately 0.65.With other more square layouts,
such as 1650×1200, it is possible to gossip with lower
probability (closer to 0.5).In the ns-2 implementation of
AODV, first a neighborhood radius of 5 hops is tried; if
no route is found, network-wide flooding is used. We
study the performance of the
following four metrics, of which the first three were also
studied in [8]. The packet delivery fraction is t he ratio of
the number of data packets successfully delivered to the
number of data packets generated by the CBR sources. ¢
The average end-to-end delay of data packets includes all
possible delays caused by buffering during routing
discovery, queuing at the interface queue, retransmission
at the MAC layer, propagation, and transfer time. ¢ The
normalized routing load represents the number of routing
packets transmitted per data packet delivered at the
destination. Each hop-wise packet transmission is counted
as one transmission.
¢ The route length ratio compares the shortest route length
found to the actual shortest route length. First, we
investigate the impact of mobility and network congestion
on gossiping


Despite the various optimizations, with flooding-based
routing many routing messages are propagated
unnecessarily. We show that gossiping can reduce control
traffic up to 35% when compared to flooding. Since the
routes found by gossiping may be up to 10%“15% longer
than those found by flooding (depending on the gossip
probability), how much gossiping can save in terms of
overall traffic depends on the gossip probability used,
node mobility, and the type of messages sent. With high
mobility, new routes will have to be found more
frequently, and the savings will be relatively larger. In
addition, if messages are mainly network-wide
broadcasts, rather than point-to-point, gossiping may
result in significant savings over flooding. (Note that with
gossiping, in general, a small fraction of the nodes will
not get the broadcast. However, in certain applications,
such as route discovery, for example, it may suffice that
almost everyone gets the message, or the contents of
broadcast k can be piggybacked with broadcast k+1 , so
that the probability of missing a message altogether
becomes very low.) Our protocol is simple and easy to
incorporate into existing routing protocols. When we add
gossiping to AODV,simulations show significant
performance improvements in all the performance
metrics, even in networks as small as 150 nodes.
Gossiping has a number of advantages over other
approaches considered in the literature. While there are
fundamental limits to the amount of non local traffic that
can be sent in large networks, due to problems of scaling,
gossiping should still be useful in large networks when
non local messages need to be sent. It is far less clear how
well other optimizations considered in the literature will
perform in large networks. it can be embedded into the
route request packet. Each intermediate node receiving
the packet will gossip with the probability carried in the
route request packet. Our preliminary experiments have
shown that this approach does produce good results,
although we have not had enough experience to determine
the best way of making these adjustments to the gossip
probability; we leave this for future work. . Moreover, as
our simulations with AODV have shown, gossiping can
provide significant advantages even in small networks.
Experience in other contexts has shown that gossiping is
also quite robust and able to tolerate faults; we expect that
this will be the case in ad hoc routing as well. All this
suggests that gossiping can be a very useful adjunct to the
arsenal of techniques in mobile computing. Of course,
work needs to be done in finding good techniques to learn
the appropriate gossip parameters.


The authors would like to thank Jon Kleinberg for
suggesting the relevance of percolation theory, Harry
Kesten for explaining the relevant results of percolation
theory, and Alan Demers for many useful comments.


R.Saravana received the B.E. degree in Electrical
and Electronics Engineering from Madurai
kamaraj University, India, in 1994, the M.E.
degree in Computer Science and Engineering
from Madurai Kamaraj University, India, in
2000, and currently pursing his Ph.D. in the area
of Mobile Computing from Anna University,
India. His research interests are in wireless
networking and mobile computing. He has 7
years of teaching experience and presently he is
HOD in CSE dept, P.S.N.A.C.E.T, Dindigul. He
has published 6 papers in conferences both in
National and International. He has been a
member of IEEE since 2000.


[1] S. Basagni, I. Chlamtac, V. R. Syrotiuk, and B. A.
Woodward, A distancerouting effect algorithm for
mobility (DREAM), in Proc. ACMMobiCom, 1998, pp.
[2] J. Broch, D. A. Maltz, D. B. Johnson, Y. C. Hu, and J.
Jetcheva, A performance comparison of multi-hop
wireless ad hoc network routing protocols, in Proc. ACM
MobiCom, Oct. 1998, pp. 85“97.
[3] S. R. Das, C. E. Perkins, and E. M. Royer, Performance
comparison of two on-demand routing protocols for ad hoc
networks, in Proc. IEEE INFOCOM, Mar. 2000, pp. 3“12.
[4] Z. Haas and M. Pearlman, The performance of query
Naresh Sammeta received the B.E. degree in
Computer Science and Engineering from Madras
University, India, in 2004, currently doing M.E
final year in Computer Science and Engineering
in P.S.N.A.C.E.T ,Dindigul from Anna
University, India, (2005-2007). His research
interests are in networking with a focus on
wireless networking and mobile computing.
control schemes for the zone routing protocol, in Proc.
ACM SIGCOMM, 1998, pp. 167“177.
[5] X.-Y. Li, K. Moaveninejad, and O. Frieder, Regional
gossip routing for wireless ad hoc networks, Mobile
Networks and Applications (MONET), vol. 10, no. 1“2, pp.
61“77, 2005.
[6] C. E. Perkins and E. M. Royer, Ad-hoc on-demand
distance vector routing, in Proc. 2nd IEEE Workshop on
Mobile Computing Systems and Applications, 1999, pp.
[7] The UCB/LBNL/VINT Network Simulator-ns (Version
2).VINT proj. [Online]. Available: http://isi.edu
B. Tuch, Development of WaveLAN, an ISM band
wireless LAN, AT&T Tech. J., vol. 72, no. 4, pp. 27“33,
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