Multicasting in Tactical Networks: Forwarding Versus Network Coding
Ola Ashour, Marc St-Hilaire and Thomas Kunz
Systems and Computer Engineering, Carleton University, Ottawa, Canada
Keywords:
Multicast, Network Coding, Packet Forwarding, Lower Bounds, Tactical Networks.
Abstract:
Multicasting refers to the transmission of packets to a group of one or more destinations. It can be very useful
in military applications, such as command and control, in which a commander needs to send instructions to a
group of tanks, users, or planes. Broadcast is a unique case of multicast, where all the nodes in the network
are intended receivers. The broadcast case has been studied quite extensively in the literature, showing that the
use of Network Coding (NC) requires fewer data transmissions than Packet Forwarding (PF). That motivates
us to explore whether the same result holds true when only a subset of nodes are receivers. In this paper,
we developed two linear optimization models that determine lower bounds on the number of required data
packet transmissions when sending data in a Mobile Ad-hoc Network (MANET) from a single source to
multiple receivers. The first model determines the minimum number of required packet transmissions under
the assumption that PF is used. The second model assumes that data is distributed using NC. We derive lower
bounds for different scenarios while varying the network size, network density, and the number of receivers in
the multicast group and compare them with each other. Results indicate that the lower bounds for both PF and
NC are almost the same for smaller network sizes (30 nodes or less), small multicast group sizes (5 or lower),
or dense networks. However, for larger network sizes, sparser networks, and larger multicast group sizes NC
is more advantageous than PF.
1 INTRODUCTION
Tactical networks are MANETs that are temporarily
formed using radios with relatively long range and
very limited bandwidth. These networks are built
from mobile nodes such as sensors, soldiers and ve-
hicles that communicate through tactical radio links.
Most of the communications over tactical networks
are group-oriented, requiring the transmission of the
same data to several destinations. One example would
be transmitting instructions from military headquar-
ters to a group of military units that are moving to-
gether such as a squad, platoons, or battalions (Refaei
and Bush, 2014; Egbogah et al., 2008). In such an en-
vironment, using unicast transmission is not efficient,
so multicasting can be a good solution.
Multicasting refers to the transmission of packets
to a group of nodes identified by a single multicast
group address. Several multicast routing protocols
have been proposed to address the problem of data
distribution in MANETs which require transmitting
data from single or multiple sources to multiple desti-
nations.(Royer and Perkins, 1999; Lee et al., 2002;
Lucile Canourgues and Beylot, 2006). These mul-
ticast routing protocols assume that packets are for-
warded on a per-packet basis as would be the case in
the Internet. Ahlswede et al. (Ahlswede et al., 2000)
introduced a new idea to enhance the multicast traffic
throughput called Network Coding (NC). NC enables
the nodes to combine or encode a certain number of
incoming packets together instead of simply forward-
ing them. One of the popular coding schemes is called
Random Linear Network Coding (RLNC). RLNC al-
lows a node to linearly combine a number of pack-
ets into one or more coded packets. A coding vector
that contains the coding coefficient of the constituent
packets is then appended to each coded packet. The
receiving node must receive a certain number of lin-
early independent coded packets (at least equal to the
number of original packets) to be able to decode the
received packets.
In this paper, we are interested in comparing the
lower bounds obtained from applying PF and NC
to a multicast scenario. Our key metric is the re-
quired number of data packet transmissions at the
MAC layer. Unlike most of the previous work, which
explored the use of NC for increasing the multicast
throughput, we are interested in exploring ways to
distribute a given amount of data most efficiently to
42
Ashour, O., St-Hilaire, M. and Kunz, T.
Multicasting in Tactical Networks: Forwarding Versus Network Coding.
DOI: 10.5220/0006907900420051
In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (ICETE 2018) - Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS, pages 42-51
ISBN: 978-989-758-319-3
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
a group of receivers. The fewer packet transmissions
a given approach requires, the fewer radio resources
and (finite) battery energy are required. To that end,
we develop two linear optimization models that, given
a network scenario, determine lower bounds on the
required number of data packet transmissions when
sending data in a MANET from a single source to
multiple destinations. Both linear programs are opti-
mistic in that they assume that packets are never lost,
in addition to being forwarded over the optimal path.
The outline of this paper is as follows. Section 2
reviews related work. Section 3, describes the PF and
NC linear programs and compare the results obtained
from both models. Finally, conclusions and future
work are provided in Section 4.
2 RELATED WORK
There exist several work that addressed the problem
of data distribution in MANETS. Some methods ap-
plied multicast routing protocols which is based on PF
to enhance bandwidth utilization. Gopinath and Na-
garajan (Gopinath and Nagarajan, 2015) proposed the
residual energy-based reliable multicast routing pro-
tocol (RERMR), which increases packet delivery ra-
tio and network life time. This is achieved by inte-
grating a stability model with a multicast backbone
to improve node stability and link quality. The pro-
tocol estimates the reliability of each path, as well as
the residual energy of its nodes. The path with higher
reliability and residual energy is then chosen for for-
warding data packets. The algorithm monitors the er-
ror rate on each path and if the error rate for a certain
path increases, another path will be selected. Sim-
ulation results show that the proposed algorithm has
better performance in terms of higher delivery ratio,
network stability, and lower delay. However, the algo-
rithm assumes a priori knowledge of nodes direction
of motion.
The problem of finding the minimal set of for-
warding nodes for broadcasting in the PF case
is called the Minimum Connected Dominating Set
(MCDS) problem and is known to be NP-hard (
ˇ
Cagalj
et al., 2002). The best exact solution to find a Mini-
mum Connected Dominating Set of an arbitrary graph
of n nodes is described in (Fomin et al., 2008) and
solves the problem in O(1.9407
n
), a slight improve-
ment of the trivial (2
n
) algorithm. If the complete
topology is known, centralized heuristics such as the
one in (Butenko et al., 2004) can be applied and pro-
vide in general a good approximation to the MCDS
size. The work done in (Kunz et al., 2010) imple-
mented this heuristic to derive the lower bound for
broadcasting data from one source to all nodes in the
network based on PF.
Other methods suggest the use of NC to reduce
duplicate packet transmission and enhance the trans-
mission efficiency of the network.The work done by
Lun et al. (Lun et al., 2008), propose the use of Ran-
dom Linear Network Coding (RLNC) to provide reli-
able communication in lossy multi-hop wireless net-
works. RLNC enables the node to linearly combine a
number of incoming packets to produce a single en-
coded packet that conveys useful information about
the whole data set. The encoded packets are then
transmitted over the wireless channel in which pack-
ets can get lost. However, if the receiving node re-
ceives a certain number of linearly independent coded
packets, it can reconstruct the original message. This
ensures a reliable delivery of data.
Determining the lower bounds in the case of net-
work coding was formulated as a linear optimization
problem in (Kunz et al., 2010; Kunz et al., 2012).
The program minimizes the total number of packet
transmissions by all nodes, subject to the constraints
that each receiver has to receive M coded data packets
from each source. The underlying assumption is that,
if a source generates M coded data packets, a receiver,
upon receipt of all M coded data packets, will be able
to decode them and regenerate the original data pack-
ets. A RLNC broadcast protocol called ARLNCCF
was proposed in (Kunz et al., 2012). The protocol
supports the use of cross source coding by allowing
packets from different sources to be coded together.
Results showed that the use of cross source coding
can decrease the number of packet transmitted in the
network by 8%-20%. Moreover,the PDR was im-
proved and packet latency was decreased. However,
the protocol complexity increased due to the need for
managing packets from different sources.
Broadcast is a unique case of multicast, where
all the nodes in the network are intended destina-
tions. (Fragouli et al., 2008) studied in-depth the case
for all-node broadcasting (all sources generate a data
packet that then has to be shared with all other nodes
in a wireless multi-hop network). The paper shows
that for certain regular topologies (ring, square grid),
NC can distribute the data at a competitive advan-
tage over PF, which they refer to as coding gain. In
the case of a ring topology, as the number of nodes
grows, NC requires only half the number of packet
transmissions compared to PF. In the grid topology,
PF requires about 33% more packet transmissions.
More generally, the theoretical analysis in the paper
shows that network coding improves performance by
a constant factor in fixed networks. In networks where
the topology dynamically changes, for example due to
Multicasting in Tactical Networks: Forwarding Versus Network Coding
43
mobility, and where operations are restricted to sim-
ple distributed algorithms, network coding can offer
improvements of a factor of log n, where n is the num-
ber of nodes in the network.
3 OPTIMIZATION MODELS
The previous section showed that NC can have signif-
icant performance benefits over PF in various broad-
casting scenarios, in particular as the network size
increases. On the other hand, in the case of a sin-
gle source and a single destination (unicasting), there
is no difference between PF and NC: the minimum
number of packet transmissions at the MAC layer
is achieved by forwarding all data or coded packets
over the shortest hop path connecting source and des-
tination. In this section, we want to examine cases
between these two extremes. Is NC beneficial in
scenarios where a single source multicasts data to a
(relatively small) number of receivers? To answer
this question, we developed two linear optimization
models using IBM ILOG CPLEX Optimization Stu-
dio. The programs determine lower bounds on the
required number of data packet transmissions when
sending data in a MANET from a single source to
multiple destinations.
3.1 Problem Formulation
Consider a static wireless ad-hoc network with N
nodes, that are randomly positioned in a square area,
based on a uniform distribution. To compare the
lower bounds in a meaningful way, we generated 25
network scenarios with the setdest utility in NS2,
which places a certain number of nodes within a given
area, using a uniform random distribution. We vary
both the number of nodes in the network as well as the
network density. We created networks with 10 to 100
nodes in steps of 10. The network size scales with the
number of nodes to keep the nodal density approx-
imately constant. To explore the impact of network
density, three sets of network scenarios were created,
which we refer to as Sparse Density, Medium Den-
sity, and High Density. In Sparse Density, the net-
work area ranges from 470 m x 470 m for a 10 node
network to an area of size 1500 m x 1500 m for a 100
node network. With an assumed transmission range
of 250 m, nodes have, on average, slightly below 2
neighbors. All networks are connected. In Medium
Density networks, the area size ranges from 346 m
x 346 m to 1095 m x 1095 m, with nodes having, on
average, slightly more than 4 neighbors. In High Den-
sity networks, the area size ranges from 255 m x 255
m to 806 m x 806 m, nodes have, on average, 8 to 9
neighbors.
In all scenarios, we assume that we have a single
source which is node zero that needs to transmit data
to a number of destinations (d). The number of re-
ceivers in the multicast group can vary from 2 to 5
and 9 which is the maximum number of destinations
that can exist in a 10 nodes network (the smallest net-
work size we consider). The linear programs assume
that the nodes with the highest ID are multicast re-
ceivers. As an example, if the user specifies that d
nodes (out of N) are to be receivers, the model uses
nodes N-d to N-1 as the receiving nodes. With ran-
dom node placements, these receivers could be close
or far away from the source node (always node 0).
The number of packet transmissions will be very sen-
sitive to the relative location of source and destina-
tions, particularly for smaller multicast group sizes
and sparser networks. To allow for coding opportu-
nities, the source node has to send more than a sin-
gle data packet, so the two linear programs assume
that the source sends 1000 data packets. We will use
these scenarios to evaluate/compare PF and NC lower
bounds.
Table 1 summarizes all experimental parameters
we varied in this paper.
Table 1: Experimental Parameters.
Parameter Possible Values
Number of Nodes 10, 20, 30, 40, 50, 60, 70,
80, 90, 100
Network Density Sparse, Medium, High
Multicast Group Size 2, 5, 9
3.2 Packet Forwarding Model
The lower bound for PF is derived using a linear pro-
gram which is illustrated as follow. Let s represent
the source node, P represent the number of packets
transmitted by the source node, N(i) is the set of one
hop neighbours of node i, X
i
is the number of pack-
ets transmitted by node i, F
i, j
(d) is the data flow from
source node s over link (i,j) to destination d. The ob-
jective of the optimization model is to minimize the
total number of packets transmitted by all the nodes
in the network.
min
X
i
Subject to:
Constraint 1: the flow over any link (i,j) to desti-
nation d should be greater than or equal to zero.
F
i, j
(d) >= 0 (i, j N) (1)
DCNET 2018 - International Conference on Data Communication Networking
44
Constraint 2: flow balance constraints which indicates
that, if i is the source node, it has to transmit P pack-
ets. If i is the destination node, it has to receive P
packets. For any other intermediate node, the sum
of outgoing flows minus the sum of incoming flows
should be zero.
jN(i)
F
i, j
(d)
lN(i)
F
l,i
(d) =
P f or i = s
P f or i = d
0 otherwise
(2)
Constraint 3: the flow over existing link (i,j)
should not exceed the number of packets transmitted
by the head of the link.
X
i
F
i, j
(d) (i, j N) (3)
Constraint 4: the flow over any link is either all
the packets or none.
F
i, j
(d) = P || F
i, j
(d) = 0 (i, j N) (4)
Figures 1 to 3 plot the lower bounds when using
PF to implement multicasting for various multicast
group sizes. The X axis plots the number of nodes in
the network, the Y axis shows the number of packet
transmissions to send a single packet to all destina-
tions in the multicast group, averaged over all 25 sce-
narios. The results presented also include the 95%
confidence interval.
The results illustrate that we need more packet
transmissions per data packet for larger networks, as-
suming the network density remains constant. We
also require more packet transmissions as the multi-
cast group size increases. Finally, as network density
increases, fewer packet transmissions are necessary to
reach all multicast group members.
3.3 Network Coding Model
Determining the lower bounds in the case of NC is de-
rived using the linear program we introduced in (Kunz
et al., 2010), but here we are interested in multicast
scenarios where only a subset of the nodes are inten-
dent destinations. The main difference between the
PF and NC linear programs is in Constraint 4 which
indicates that the flow over any link is either all the
packets or none. This constraint can not be used in
the case of NC as it will force the node to forward
all the packets it received and the idea of NC is to al-
low the node to combine multiple incoming packets
into one or more outgoing coded packets. Removing
that constraint will raise another problem which will
be illustrated using the following example. Consider
a ring network consisting of six nodes, and assume
that the source node (node 1) wants to transmit two
packets (a and b) to destination nodes 3, 4, and 5. If
we use PF, the minimum number of required packet
transmission to deliver the two packets to all the three
destinations is 8 packets as shown in Figure 4. Node
1 will transmit the two packets to its one hop neigh-
bours node 2 and 6, then node 2 will transmit the two
packets to node 3. Meanwhile node 6 will transmit
the two packets to node 5. Finally either node 3 or
node 5 will transmit the two packets to node 4. How-
ever, if we remove Constraint 4, the result from the
optimization program indicate that we need only 6
packet transmissions to deliver the two packets to all
the destinations. Figure 5 illustrates how packets are
transmitted in the case of NC. The source node will
transmit packet a to node 2 and packet b to node 6,
node 2 will transmit packet a to node 3, while node
6 will transmit packet b to node 5. After that node 3
will transmit packet a to node 4. Meanwhile node 5
will transmit packet b to node 4. In this case, node
4 received the two packets a and b, but each of node
3 and 5 received only one packet, so node 4 will en-
code packet a and packet b and transmit one encoded
packet (a b) which will be received by nodes 3 and
5. Nodes 3 and 5 will decode the received encoded
packet to obtain the missing packet (either a or b). The
problem that arises here is that although the source
node transmitted two packets when it sent packets a
and b, and each transmission occupies the transmis-
sion media, the minimum number of packet transmis-
sion should be 7 not 6. However, if we account for
physical packet transmissons the way we do in the PF
case, these two transmissons would only be counted
as one packet transmission sent by the source node.
In order to fix this problem we introduce the notion of
dummy nodes. In the above scenario, the two pack-
ets transmitted by node 1 will first be sent to node 1’s
dummy node, and count as two physical packet trans-
missions, before the dummy node forwards the two
packets to different intended receivers.
We assume that any real node i has to transmit the
packets to its dummy node
¯
i, which in turn will for-
ward the packets to the direct neighbours of i. Assume
that we are again transmitting P packets, which now
may be coded (i.e., combinations of native data pack-
ets). The objective of the program is to minimize the
total number of packets transmitted by all nodes in the
network.
min
X
i
Subject to:
Const. 1: the flow from node i to its dummy node
¯
i
should be less than or equal to the number of physical
packets transmitted by that node.
X
i
>= F
i,
¯
i
(d) i, j N (5)
Multicasting in Tactical Networks: Forwarding Versus Network Coding
45
Figure 1: Lower Bounds for PF in Networks of Sparse Density.
Figure 2: Lower Bounds for PF in Networks of Medium Density.
Figure 3: Lower Bounds for PF in Networks of High Density.
Const. 2: flow balance constraints which indicate that
the source node has to generate P packets and the
destination node should receive P coded data pack-
ets. Otherwise, the node should forward all the data
DCNET 2018 - International Conference on Data Communication Networking
46
Figure 4: Packet Transmission using PF.
Figure 5: Packet Transmission using NC.
packets it received to its dummy node.
F
i,
¯
i
(d)
jN(i)
F
¯
j,i
(d) =
P f or i = s
P f or i = d
0 otherwise
(6)
Const. 3: the flow balance constraints on the
dummy node is zero.
jN(i)
F
¯
i, j
(d) F
i,
¯
i
(d) = 0 (7)
Const. 4: the number of packets transmitted by
any node i should be greater than or equal to zero.
X
i
>= 0 i N (8)
Multicasting in Tactical Networks: Forwarding Versus Network Coding
47
Figure 6: Lower Bounds for NC in Networks of Sparse Density.
Figure 7: Lower Bounds for NC in Networks of Medium Density.
Figure 8: Lower Bounds for NC in Networks of High Density.
Figures 4 to 6 plot the lower bounds when us-
ing NC to implement multicasting for various multi-
cast group sizes. The 95% confidence intervals are
quite tight, giving us assurance that the results av-
eraged over 25 scenarios are meaningful enough to
draw some conclusions/observations from our data.
DCNET 2018 - International Conference on Data Communication Networking
48
Figure 9: Coding Gain in Networks of Sparse Density.
Figure 10: Coding Gain in Networks of Medium Density.
Figure 11: Coding Gain in Networks of High Density.
Multicasting in Tactical Networks: Forwarding Versus Network Coding
49
Similar to the PF results, NC results show that
as the multicast group size increases, more packet
transmissions are required to reach all group mem-
bers. For a given network density, as the number of
nodes increases, again more packet transmissions are
required, as destinations are (potentially) further away
from the multicast source. Finally, as network density
increases, the network diameter shrinks, resulting in
fewer packet transmissions to reach a given number
of multicast receivers.
3.4 Comparing NC and PF Results
Based on the results obtained from PF and NC in the
previous sections, the question that arises is whether
NC has a clear advantage over PF. To answer this
question, we calculated the coding gain and plotted
it in Figures 7, 8, and 9 as a function of network size,
network density, and multicast group size.
NC has an advantage over PF if it requires fewer
packet transmissions, so we divide the number of
packet transmissions under PF by the number of
packet transmissions under NC. A value of 1 indi-
cates that both approaches require the same number
of packet transmissions to deliver a data packet to
all multicast receivers. A value of 1.2, for exam-
ple, would indicate that PF requires 20% more packet
transmissions than NC under the same scenario. Con-
sider for example that we want to calculate the cod-
ing gain in a sparse network of size 90 nodes and 9
destinations. The number of required packet trans-
missions in case of PF is 11.68 which can be obtained
from Figure 1. The corresponding number of required
packet transmissions in case of NC is 10.79 which is
obtained from Figure 6. Dividing the number of re-
quired packet transmissions in case of PF by that of
NC we get 1.08 which is the value plotted in Figure 9.
This value indicates that PF requires transmitting 8%
more packets than NC.
Figures 7, 8, and 9 illustrate that NC is never
worse than PF, no matter the network density, num-
ber of nodes, or multicast group size. Using NC will
often result in a coding gain, and the gain is more pro-
nounced for larger networks, sparser networks, and
larger multicast group sizes. For multicasting to 2
destinations, NC has at best a marginal improvement
over PF (often less than 1%, with a maximum gain of
3%). For multicasting to 9 destinations, NC starts to
show a non-trivial gain for networks as small as 30
nodes. However, given the width of the confidence
intervals for the individual data points, we should not
over-analyze these differences. It turns out that, for
all network sizes, multicast group sizes, and network
densities, the differences in the lower bounds are NOT
statistically significant.
4 CONCLUSIONS
Based on our models and the results collected, it
would seem that NC is potentially advantageous, un-
less we limit ourselves to small networks (30 nodes
or less) and small multicast group sizes (5 or lower).
However, any coding scheme also introduces over-
heads: packets have to be encoded at the source, de-
coded at the receiver, and potentially recoded at in-
termediate nodes. Depending on the coding scheme
used, network coding may also increase end-to-end
latency, as a number of coded packets may have to
be received at a node before the original data pack-
ets can be reconstructed. Finally, in a network coding
scheme, losing a single coded packet may result in a
receiver being unable to recover a number of original
data packets. In deciding whether to apply PF or NC,
these factors also should be considered.
In the future work, we plan to build on these mod-
els to model/include the impact of lossy links.
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