Abstract. Multipath routing can improve system performance of capacity-limited wireless networks through load balancing. However, even with a single source and destination, intra-flow and inter-flow interference can void any performance improvement. In this paper, we show that establishing non-interfering paths can, in theory, leverage this issue. In practice however, finding non-interfering paths can be quite complex. In fact, we demonstrate that the problem of finding two non-interfering paths for a single source-destination pair is NP-complete. Therefore, an interesting problem is to determine if, given a network topology, non-interfering multipath routing is appropriate. To address this issue, we provide an analytic approximation of the probability of finding two non-interfering paths. The correctness of the analysis is verified by simulations

David R Cheriton

R Boutaba

S Waharte

CiteSeerX

On the Probability of Finding Non-interfering
Paths in Wireless Multihop Networks
S. Waharte and R. Boutaba
David R. Cheriton School of Computer Science
University of Waterloo
Waterloo, Ontario, Canada N2L 3G1
{spwaharte,rboutaba}@uwaterloo.ca
Abstract. Multipath routing can improve system performance of
capacity-limited wireless networks through load balancing. However, even
with a single source and destination, intra-flow and inter-flow interference
can void any performance improvement. In this paper, we show that estab-
lishing non-interfering paths can, in theory, leverage this issue. In practice
however, finding non-interfering paths can be quite complex. In fact, we
demonstrate that the problem of finding two non-interfering paths for a
single source-destination pair is NP-complete. Therefore, an interesting
problem is to determine if, given a network topology, non-interfering mul-
tipath routing is appropriate. To address this issue, we provide an analytic
approximation of the probability of finding two non-interfering paths. The
correctness of the analysis is verified by simulations.
1 Introduction
The landscape of network communications has evolved over the last years with
the emergence and deployment of protocols enabling more flexible and reliable
means to communicate wirelessly over single or multiple hops. Deploying wire-
less backbone networks (also referred to as Wireless Mesh Networks) is becoming
increasingly popular in view of the reduced initial investment cost and flexibility
of deployment. Wireless sensor networks have also received great attention from
the research community, which can be explained by their application potential
in various areas such as military applications, environment monitoring, etc. [1].
However dealing with interference in a shared transmission environment still re-
mains a challenging task. Environmental noise can be partly responsible for the
quality degradation of data transmissions. In addition to that, the necessity to
share the transmission channel among wireless nodes within the same vicinity
significantly contributes to decreasing the nominal capacity available to wire-
less nodes. It becomes therefore crucial to develop appropriate mechanisms to
alleviate the effect of these limitations on the system performance.
Multipath routing has been put forward as a solution to leverage the capacity
limitations in wireless networks. Spreading traffic flows over multiple paths has
been demonstrated to achieve a better load balancing than single path routing
potentially leading to an increase in the nominal achievable throughput [3] [5]. But
A. Das et al. (Eds.): NETWORKING 2008, LNCS 4982, pp. 914–921, 2008.
c© IFIP International Federation for Information Processing 2008
On the Probability of Finding Non-interfering Paths 915
the resulting performance gain greatly depends on the choice of the routing paths.
If the paths are at a distance such that the traffic on one path interferes with the
traffic on the other path, the throughput improvement overall becomes negligible
[7]. It is also important to factor in the choice of the routing approach the cost of the
paths establishment. As the control overhead increases with the number of paths
[6], it can potentially void the benefit of multipath routing. Therefore, estimating
the probability of finding non-interfering paths given a network topology can help
deciding whether for a particular network, multipath routing is an appropriate
approach and if it can improve the system performance at all.
To support our analysis, we first show that routing over non-interfering paths
can lead to a better network utilization. We then study the complexity of finding
two non-interfering paths and show that the problem is actually NP-complete.
Therefore, in order the reduce the cost of establishing multiple paths, we provide
an analytic estimate of the probability of finding two non-interfering paths as a
function of the network density. This result can subsequently be used to evaluate
the suitability of implementing a non-interfering multipath routing algorithm
for a given network topology and to adjust accordingly the routing strategy. We
validate the correctness of our analysis through simulations.
The remainder of this paper is organized as follows. We describe our analysis
of the complexity of 2-path routing in Section 2. The analytical derivation of the
probability of finding two non-interfering paths is presented in Section 3. Section
4 summarizes our contributions and concludes this paper.
2 Complexity Analysis of Disjoint Path Routing with
Interference Constraints
The problem we are studying consists in finding multiple non-interfering paths
between a source-destination pair. To analyze the complexity of the problem,
we restrict our analysis to the case in which only two paths are set up between
a source and destination node. We refer to this problem as 2-path routing. We
prove the NP-completeness of this problem, i.e. that is, the polynomial time
solution for this problem is unlikely to exist.
Definition 1. Given a directed graph G(V, E) and two nodes (s, t) ∈ V , 2-path
routing consists in finding two paths P1 and P2 between s and t such that: 1/ all
the nodes in P1 and all the nodes in P2 form a connected graph; and 2/ there
exists no edge between a node in P1 and a node in P2.
Theorem 1. 2-path routing is NP-complete.
Proof. Omitted due to space restrictions.
3 Probability of Finding Two Non-interfering Paths
3.1 General Methodology
Given a source node and a destination node, two paths minimizing the inter-
ference shoud consist of a set of nodes such that the nodes on one path do not
916 S. Waharte and R. Boutaba
interfere with the nodes on the other path, except for the first-hop nodes and
last-hop nodes. We assume that nodes locations are known. In order to avoid the
choices of multiple paths with significantly different lengths (and consequently
to avoid the burden of handling traffic flows with different end-to-end delays),
the approach we considered is to define a guard band between the source and
the destination and to set up paths outside this band but as close as possible to
it (within a distance ε of the guard band). Therefore, the paths are guaranteed
not to interfere. ε is a tunable parameter that can be adjusted depending on the
network density. More details on the tuning of this parameter are given at the
end of this section. We therefore need to compute:
1. The probability P1 to find two non-interfering nodes at the first hop.
2. The probability P2 that two paths exist after the first hop and before the
last hop, with the constraint that the nodes along each path do not interfere
with each other.
3.2 Computation of P1
The first condition to satisfy is to find two non-interfering nodes in the trans-
mission area of the source. We assume that for each path the next-hop nodes
are located in the half-plane oriented towards the destination (i.e. no backward
transmission). For the first hop, this constraint added to the physical transmis-
sion range limitation restricts the feasible geographic location of the first-hop
nodes to half a disk. We need therefore to compute:
1. the probability P11(k) to have k nodes in half a disk;
2. the probability P12(k) that at least two of these k nodes are separated by a
distance greater than R (transmission range).
P1 can be determined by: P1 =
∑∞
k=1 P11(k)P12(k)
Computation of P11(k). We assume that the nodes are uniformly distributed
over an area A with a density ρ. They have a transmission range R and their
positions are independent of each other. For a large number of nodes, the prob-
ability that k nodes are located in a given area A can be approximated with a
Poisson distribution (Eq. 1) [4].
P (number of nodes = k) =
(ρA)k
k!
e−ρA (1)
Computation of P12(k). Let A be the source node and B a node randomly
located in the half-disk centered at A with respective polar coordinates (0, 0)
and (r, θ). We refer to the extreme points of the diameter of the half-disk as A1
and A2. In order not to interfere with B, a node should respect the following
conditions:
1. to be at transmission range of A
2. not to be at transmission range of B
On the Probability of Finding Non-interfering Paths 917
To find the probability that at least two nodes randomly located in half a
disk do not interfere, we adopt the following method. We choose a point B in
the transmission area of node A and determine the probability that there exists
at least one node non-interfering with B (therefore located in one of the dashed
areas as shown in Fig 1) and this, for all possible positions of B. Depending on
the position of B, two scenarios can occur:
– Case 1: B is located at a distance less than R from either A1 or A2 (Fig.
1 (a)). One single solution area exists. This corresponds to the case where
cos(θ) ≥ R2r .
– Case 2: B is located at a distance greater than R from both A1 and A2
(Fig. 1 (b)). Two solution areas exist. This corresponds to the case where
cos(θ) ≤ R2r .
B
A1 A2A
(a) Case 1: 1 feasible solution area
B
A1 A2A
(b) Case 2: two feasible solution
areas
Fig. 1. Computation of P12(k) (the non-interfering zones are dashed)
A general formulation of the probability to find two nodes distanced by at
least R can be expressed as follows.
Theorem 2. Let us assume that there are N nodes at transmission distance of
A. The probability P that at least two of these N nodes are at a distance greater
than R is:
P = 1 −
∫ R
r=0
∫ π
2
θ=0
2
πR
(1 − (2(Ainter(r, θ))
πR2
)n−1)∂r∂θ (2)
where Ainter(r, θ) is the interference area of a node with polar coordinate (r, θ)
in the solution domain (half-disk).
In order to determine the actual value of the interference area Ainter(r, θ), we
need to breakdown the computation into the two cases previously described.
Case 1: One feasible region Let N be the number of nodes in the half-disk area
obtained by properly choosing the network density so that the probability to
find at least 2 nodes tends to 1. If we consider one node (Node B) among these
N nodes, the probability that at least one of the remaining N − 1 nodes is at
least at a distance R from B can be determined by computing the complement
of the probability that all the nodes are at a distance less than R from B.
918 S. Waharte and R. Boutaba
Let us first calculate the intersection between the coverage areas of A re-
stricted to the half-disk oriented towards the destination node and the coverage
of B. The intersection area between the disk centered at A and the disk cen-
tered at B form a lens whose area is referred to as Alens. This area can be
straightforwardly computed geometrically as follows:
Alens = 2R2 arccos(
r
2R
) − r
2
√
4R2 − r2 (3)
θ
A
B
C
D
E
(a) Case 1: 1 feasible region
B
A1 A A2x1
x′1
x2
x′2
(b) Case 2: 2 feasible regions
Fig. 2. Computation of the non-interfering zone (dashed)
We can also observe that since A and B are at transmission range of each
other, these points are necessarily located in the lens whose area has been previ-
ously computed. In particular, we can establish the following relation: SACD =
SBCD − SABC
The area formed by BCD consists of a disk section that can be directly com-
puted:
SBCD =
̂BCD ∗ R2
2
(4)
To compute ̂BCD, let us define AC = x. By construction, we have ̂BAC =
π − θ, AB = r and BC = R. Using the law of cosine, we determine x:
x = −r cos(θ) +
√
R2 − r2(sin(θ))2
̂BCD can therefore be deducted using the same method.
To obtain the area of ABC, we apply Heron’s formula:
s =
x + r + R
2
SABC =
√
s(s − r)(s − x)(s − R) (5)
On the Probability of Finding Non-interfering Paths 919
By combining Eq. 4 and Eq. 5, we obtain:
SACD = arccos(
R2 + r2 − x2
2Rr
)
R2
2
−
√
s(s − r)(s − x)(s − R) (6)
We can therefore compute the intersection area:
Sinter(r, θ) =
Alens
2
− SACD + R2
θ
2
(7)
Finally, the probability that at least one of these N − 1 nodes does not fall in
this area is:
Pcase1 = 1 − (
Sinter(r, θ)
πR2
2
)N−1 (8)
Case 2: Two feasible regions
In this case, node B is at a distance at least R away from A1 and A2. Without
lack of generality, let us assume that B is located in the same quarter of disk as
A1 (Fig. 2 (b)).
The disk centered at B cut the x-axis in two points x1 and x2 such that
x1 < x2. Obviously a solution zone exists in this area only if |x1| < R. Let x′1 be
the intersection point with the smallest x-coordinate between the circle centered
at A and the circle centered at B.
The solution area is therefore bounded by A1x1x′1. By geometric considera-
tions we can observe that:
SA1x1x′1 = SABx′1A1 − SABx′1x1
By calculating SABx′1A1 and SABx′1x1 , we find the solution area.
Due to space limitations, we omit the details of the computation that follows
similar steps as in the previous case. The probability to find two non-interfering
nodes in this second case is:
Pcase2 = 1 − (
πR2
2
− SA1x1x′1 − SA2x2x′2)
N−1 (9)
Evaluation. To evaluate the accuracy of the upper bound of our analysis, we
compared the results obtained by our derivation with the ones obtained through
simulations by computing the distance between two pairs of nodes in a random
distribution. We ran the experiments 1000 times for various network densities.
The results of the simulations are depicted in Fig. 3.
We can see that our analysis provides a close approximation of the probability
of finding two non-interfering paths when compared with simulations.
3.3 Computation of P2
For the subsequent hops along each path, it is sufficient to determine the prob-
ability that each node has at least one neighbor towards the destination while
respecting the interference constraint, that is to say that the chosen node should
920 S. Waharte and R. Boutaba
0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Network density
P
ro
ba
bi
lit
y 
of
 fi
nd
in
g 
tw
o 
no
de
s 
di
st
an
t o
f a
t l
ea
st
 R
 
 
Theory
Simulations
Fig. 3. Probability of finding two non-interfering nodes
not interfere with the nodes on the other path [2]. In addition, for quality of
service purposes, it is necessary to limit the number of hops along each path.
This can be achieved by constraining the possible location of the next-hop node.
Considering that each node has a fixed transmission range, we can adjust the
width ε of the band depending on the network density and quality-of-service
constraints (Eq. 10). For a network density of 8e-4 nodes/m2 and a transmission
range of 250m, the probability to find a next-hop node with a probability of 95%
is achieved with a value of epsilon of 15m.
P (at least 1 neighbor) = 1 − P (no neighbor)
P (at least 1 neighbor) = 1 − e−ρRε
ε =
− ln(1 − P (at least 1 neighbor)))
ρR
(10)
Let h be the number of hops between the source and the destination, h ≥ 2.
The probability to find 2 totally-disjoint paths can be expressed as:
P2 = (1 − e−ρRε)2(h−2) (11)
4 Conclusion
With the increasing deployment of wireless networks, the necessity to cope with
limited link capacity becomes more and more stringent. This problem is further
exacerbated when transmission occurs over multiple hops. The gain in flexibility
in network deployment is counterbalanced by a reduction in available through-
put. This is directly linked to the problem of interference. All devices in the
same vicinity have to share the transmission medium, which consequently re-
duces the capacity available to each. To tackle this issue, some research works
have focused on developing routing protocols that could account for interference
and improve the network performance. Multipath routing has been proposed as
an alternate solution to single path routing due to its potential to improve the
On the Probability of Finding Non-interfering Paths 921
network throughput by balancing the load more evenly. However, to take full
advantage of this routing method, interference has to be accounted for during
the choice of the routing paths.
The contributions of this work are the followings. First we studied the com-
plexity of finding two paths between a given source and destination and we
proved the NP-completeness of this problem. Then, we analytically derived the
probability of finding two non-interfering paths given a certain network density.
The results obtained are noteworthy as they can be directly applied to the
choice of a routing strategy. The network density and therefore the probability
of finding non-interfering paths can lead to different routing decisions. The re-
sults derived in this paper can consequently enable adaptive routing strategies
depending on the network characteristics.
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On the probability of finding non-interfering paths in wireless multihop networks

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