CO2 : Design Fault-tolerant Relay Node Deployment
Strategy for Throwbox-based DTNs
Wenlin Han and Yang Xiao
Department of Computer Science
The University of Alabama
342 H.M. Comer, Box 870290
Tuscaloosa, AL 35487-0290 USA
emails: [email protected], [email protected]
Abstract. Delay Tolerant Networks (DTNs) are not like the Internet, where contemporaneous connectivity among all nodes is always available. We need to design a new relay node deployment strategy, which can make DTNs more reliable
and fault-tolerant. In this paper, we propose a fault-tolerant relay node deployment strategy for throwbox-based DTNs. It employs an approximation algorithm
to choose throwbox placement locations, and to construct a 2-COnnected DTN,
called CO2 , where each mobile node can communicate with at least two relay
nodes within its activity scope. Simulation results based on Tuscaloosa bus transit system have shown its effectiveness and high efficiency when compared to two
types of popular relay node deployment strategies in the literature.
Keywords: DTNs, Throwbox, Fault-tolerance, Relay strategy, Intermittent connection,
Reliability.
1
Introduction
Delay Tolerant Networks (DTNs) [1] are a class of emerging networks attracting
various interests. The history of DTNs can date back to late 1990 [2] with the growth of
interest in mobile ad-hoc networks and interplanetary Internet [3]. Other popular applications of DTNs include vehicular ad-hoc networks, rural village networks, underwater
acoustic networks, disaster recovery networks, and social networks.
DTNs are not Internet-like networks. They are networks, where contemporaneous
connectivity among all nodes does not always exist. Because of long and variable delay, relay nodes, such as throwboxes, are needed to increase contact opportunities and
to reduce delay. Relay node deployment strategies can be classified into three categories: contact-oblivious deployment, contact-based deployment, and customized deployment [4]. In the contact-oblivious deployment, throwboxes are deployed without
considering the contact opportunities between mobile nodes and throwboxes, such as
regularly deploying throwboxes in an area to form a grid. In the contact-based deployment, throwboxes are placed to maximize contact opportunities between nodes, such as
placing throwboxes in the areas mostly visited by mobile nodes. In the papers [5, 6],
learning from primates’ scent marking, the authors use sensors nodes or RFID (Radio Frequency Identification) tags left for messages or traces for other mobile robots
2
Wenlin Han and Yang Xiao
for information. However, all the traditional deployment strategies have not addressed
the problem of fault-tolerance. We need a novel strategy to deploy relay node that can
achieve the fault-tolerance purpose, which means when some of the relay nodes fail,
the network can still work properly without performance loss.
Fault-tolerant relay strategy has been studied in wireless sensor networks (WSNs) [7,
8]. In the paper [9], the problem of deploying relay nodes to provide the desired faulttolerance through multi-path connectivity (k-connectivity) is studied, and they propose
an algorithm, which is based on an evolutionary scheme to place an optimum number
of energy-constrained relay nodes. It can achieve the desired connectivity between homogeneous wireless sensor nodes with the same communication range of each sensor
node.
In this paper, we propose a novel strategy, called CO2 , which can deploy relay
nodes in DTNs to achieve the fault-tolerance purpose while maintaining a relatively
small number of relay nodes. From a set of potential locations of relay throwboxes,
CO2 chooses some nodes to construct a 2-connected graph. These throwboxes cover
all the mobile nodes in a DTN letting each mobile node connect with at least two relay
nodes. Every node in the 2-connected DTN can reach another node via two node disjoint
paths so that it can guarantee fault-tolerance. When some of the relay nodes are out of
work, the DTN can still work properly and maintain high performance. Our experiments
simulate real bus transit system in Tuscaloosa, Alabama, USA, and the experimental
results show that the relay nodes chosen by the proposed CO2 strategy can make DTNs
fault-tolerant with relatively small number of relay throwboxes. The main contributions
of this paper include:
– It is the first strategy addressing fault-tolerant relay node deployment problem in
DTNs, to the best of our knowledge;
– We analyze the impact of different routing protocols and mobility models working
with CO2 ;
– We compare performance among three deployment strategies: the contact-oblivious
deployment, the contact-based deployment, and CO2 .
The rest of the paper is organized as follows: Section 2 outlines and models the problem of fault-tolerant relay node deployment. Section 3 presents the strategy of CO2 and
the related relay node selection algorithm. In Section 4, we simulate the real world bus
transit system in Tuscaloosa and conduct experiments based on it to show performance
improvement. Finally, we conclude the paper and propose future work in Section 5.
2
Problem statement
In this section, we will present the main problem that the proposed CO2 strategy
aiming to address.
The intermittent connection is the most prominent feature of DTNs, and it is also
a very challenging problem whenever we need to design a new protocol, algorithm or
scheme for DTNs. To increase contact opportunity, throwboxes are employed to enhance network performance. As shown in Fig. 1, it is a simple scenario of a Vehicular
DTN. Each bus moves within its range of activity - the oval area, follows its certain
route. Contact and message delivery can happen when two buses meet each other, but
2-Connected DTN for Throwbox-based DTNs
3
the contact opportunity is rare when the buses are very few. Some throwboxes are placed
in the oval areas, so that a bus can transfer its messages to a certain throwbox, and waits
for another bus to pick them up. In this way, buses can communicate with each other.
For example, Bus Group G1 can communicate with Bus Group G7 via multiple hops
between throwboxes and other buses. However, throwboxes are resource-constrained
mobile devices with limited battery capacity. They are deployed outdoor, even in rural
areas, baring harsh environment, which means that they stand big chances of failure. If
some throwboxes are out of work, some buses may not be able to communicate with
each other. For example, if Throwboxes x1, x4 and x5 fail, Bus G1 will lose communication with Bus Group G7.
G2
x2
G6
x1
G1
x3
x6
G3
G5
x4
x5
G7
x7
Fig. 1: Fault-tolerance problem scenario of a simple throwbox-based DTN.
We need to design a new relay node placement strategy, which can work well when
some of the throwboxes fail, while the number of the throwboxes deployed is the minimum. Here, we define a set of throwboxes as 2-connected if they can still communicate
with each other within one or multiple hops when one of them fails.
3
CO2 : relay node placement strategy
CO2 is to construct a 2-connected DTN with a reasonable number of relay nodes.
The basic idea is to maximize node availability and link availability, while maintaining
two disjoint paths with minimum hops. In this section, we will introduce the proposed
relay node placement strategy, CO2 , in details.
Let’s define:
T is the total number of time segments, where t = 0, 1, · · · , T ;
B is the set of mobile nodes, such as buses;
U is the set of possible unchosen locations of relay throwboxes;
4
Wenlin Han and Yang Xiao
R is the set of already chosen locations of relay throwboxes, and it is ∅ initially;
label[b] is the number of times that a mobile node b is covered by relay throwboxes,
b ∈ B;
C(u) is the set of mobile nodes, which can be covered by a relay throwbox u.
Now the problem is to select an appropriate R from U . To observe the links, we
assume that we have placed a throwbox v in each possible location in U . Moreover, we
name the set of these throwboxes as V . We observe the links between each pair of two
throwboxes in V , and name the set of the links as E. Correspondingly, the link states
set is named as S. Now we get a graph G(V, E, S, P ), where P is the same as defined
previously.
To make it concise, we mix the concepts of a throwbox and the location of this
throwbox. Thus, when we talk about “pick up a node from U ”, we mean ”pick up a
location where we can place a throwbox”, and further examine the connections between
this throwbox and other throwboxes or mobile nodes. The algorithm mainly includes
four steps.
Step I. Pick a node u0 from U satisfying the following four conditions:
1. At least at sometimes, the node u0 can work normally, that is
pu0 ̸= 0.
(1)
2. The node u0 has at least one available link, that is
T
∨ ∨
su0 vt = 1,
(2)
∀v∈V t=0
where su0 vt is the link state of Link eu0 v during time period t and v is a node in V ,
u0 ̸= v.
∑
∨T
3. The number of nodes in V that u can communicate with is calculated as ∀v∈V t=0 suvt .
Moreover, pu is the probability of working normally. In this step, we pick up a node
that can maximize node availability. Let us define W1u0 as:
T
∑ ∨
{
}
W1u0 = max pu
suvt ,
∀u∈U
(3)
∀v∈V t=0
where u ̸= v.
4. Let us denote the set of nodes satisfying Equation (3) as U1 . If there are more than
one nodes in U1 , pick the one of the maximum link availability, that is:
W2u0
T
∑ ∑
{
}
= max pu
suvt ,
∀u∈U1
(4)
∀v∈V t=0
where u ̸= v.
Mark each node b ∈ B ∩ C(u0 ) as 1, that is label[b] = 1. Put the relay node u0 into the
set R, and remove u0 from U .
Step II. In the new set U , find a relay node u′ that satisfies the following three
conditions:
2-Connected DTN for Throwbox-based DTNs
5
1. u′ can reach u0 through two node-disjoint paths. If there are more than two nodedisjoint paths, select the two paths with the minimum number of hops. Define the
set of nodes in the two paths as Qu′ , where u′ ∈ Qu′ and u0 ∈
/ Qu′ . A similar
definition for a node u is Qu .
2. In this step, we choose u′ , which makes Qu′ mostly cover the nodes in B that
already covered by u0 , and the number of relay throwboxes in Qu′ is relatively
small. Let us define c(b, Qu ) as the number of times that b is covered by the relay
throwboxes in the set of Qu ∩ U , that is
c(b, Qu ) = |{q ′ ∈ Qu ∩ U : b ∈ C(q ′ )}|.
Also, we need to consider node failure probability. It satisfies:
∑
{ pu b∈B min(c(b, Qu ), 2 − label[b]) }
.
W3u′ = max
∀u∈U
|Qu ∩ U |
(5)
(6)
3. Let us define the set of nodes satisfying Equation (6) as U2 . If there are more than
one nodes in U2 , pick the one that can maximize the link availability when considering the links between the nodes in Qu and all other nodes in V .
{ ∑
T
∑ ∑
}
pu∗ su∗ vt ,
(7)
label[b] = label[b] + min(c(b, Qu′ ), 2 − label[b]).
(8)
W4u′ = max
∀u∈U2
∀u∗ ∈Qu ∀v∈V t=0
where u∗ ̸= v and u ̸= v .
Mark each node b ∈ B ∩ C(Qu′ ∩ U ), e.g.,
Put all relay nodes in Qu′ into the set R, and remove all nodes in Qu′ from U . For all b
in B, if label[b] = 2, remove b from B.
Step III. Add an artificial node v ′ into G to construct a graph G′ , and connect each
node in R with v ′ . Pick a node u′′ from U , which meets the following three conditions:
1. u′′ can reach v ′ via at least two node disjoint paths. Define two of these disjoint
paths with minimum number of hops as Qu′′ , where u′′ ∈ Qu′′ and v ′ ∈
/ Qu′′ .
2. Qu′′ can mostly cover the nodes in B that already covered by R, and has relatively
smaller amount of nodes. u′′ satisfies:
∑
{ pu b∈B min(c(b, Qu ), 2 − label[b]) }
W3u′′ = max
.
(9)
∀u∈U
|Qu ∩ U |
This equation is similar to Equation 6, but the values of the variables have already
changed.
3. Let us define the set of nodes satisfying Equation (9) as U3 . If there are more than
one nodes in U3 , pick the one achieving the maximum link availability of the links
between the nodes in Qu and all other nodes in V .
W4u′′ = max
∀u∈U3
where u∗ ̸= v and u ̸= v .
{ ∑
T
∑ ∑
∀u∗ ∈Qu ∀v∈V t=0
}
pu∗ su∗ vt ,
(10)
6
Wenlin Han and Yang Xiao
Mark each node b ∈ B ∩ C[Qu′′ ∩ U ], e.g.,
label[b] = label[b] + min(c[b, Qu′′ ], 2 − label[b]).
(11)
Put all relay nodes in Qu′′ into the set R, and remove all nodes in Qu′′ from U . For all
b in B, if label[b] = 2, remove b from B.
Step IV. If B = ∅, we get the solution R; otherwise, repeat Step III.
4
Performance evaluation
In this section, we will present some experiments to show the effectiveness of the
CO2 strategy.
4.1 Experimental settings
We simulate bus transit system at Tuscaloosa, Alabama, USA. There are six routes
represented by different colors: gold is for Greensboro Route; amaranth represents Stillman Route; University Shuttle Route is red; green represents Shelton State Route; blue
is for Holt Route; and V.A./University Route is crimson [10].
Group
Type
ID
Number
Speed/FR
Route/Location
1
b1_
4
10-40 km/h
Stillman Route
2
b2_
4
10-40 km/h
University Shuttle Route
3
b3_
4
25-60 km/h
Shelton State Route
4
b4_
4
10-40 km/h
Greensboro Route
5
b5_
4
25-60 km/h
Holt Route
6
b6_
4
10-50 km/h
V.A./University Route North
7
b7_
4
25-60 km/h
V.A./University Route South
8
x1
1
80%
G1,G2
9
x2
1
0%
G2,G6
10
x3
1
40%
G6,G5
11
x4
1
0%
G3,G4
12
x5
1
80%
G5,G7
13
x6
1
20%
G1,G3
14
x7
1
20%
G4,G7
Fig. 2: Experimental settings based on real Tuscaloosa bus transit system. FR is the failure rate of
a throwbox.
Fig. 2 illustrates the experimental settings. The simulation tool is Opportunistic
Network Environment Simulator (ONE) [11].
2-Connected DTN for Throwbox-based DTNs
4.2
7
Experimental results
We evaluate CO2 performance working with Epidemic [12], a multi-copy routing protocol. We choose delivery ratio, contact time and fault-tolerance as criteria, and
compare CO2 with the contact-oblivious deployment and the contact-based deployment
strategies.
Delivery ratio Fig. 3 shows delivery ratio comparison among three strategies. The
routing protocol in Fig. 3 is Epidemic routing. Regular deployment is contact-oblivious,
and it performs the worst among three strategies. The contact-based deployment works
better than the regular deployment. CO2 achieves the best delivery ratio performance.
It improves delivery ratio from less than 50% to over 80%.
Epedemic routing delivery ratio comparison
90%
CO2
Contact-based
Regular
Delivery ratio (%)
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
5000
10000 15000 20000 25000 30000 35000 40000
Time (second)
Fig. 3: Delivery ratio comparison among three deployment strategies: Regular deployment,
Contact-based deployment and CO2 deployment.
Fault-tolerance Fig. 4 shows the comparison of fault-tolerance performance among
three strategies, and the routing protocol is the Epidemic routing protocol. When the
other strategies cannot deliver any message, CO2 can still work properly, and the delivery ratio can reach over 40%.
Contact time Fig. 5 shows accumulated contact time comparison among three strategies. The routing protocol is the Epidemic routing protocol in Fig. 5. Since the regular
deployment strategy is contact-oblivious, the contact opportunities among nodes are not
8
Wenlin Han and Yang Xiao
Fault-tolerance comparison (Epidemic)
45%
CO2
Contact-based
Regular
Delivery ratio (%)
40%
35%
30%
25%
20%
15%
10%
5%
0%
0
5000
10000
15000
20000
25000
30000
35000
40000
Time (Second)
Fig. 4: Fault-tolerance comparison among three deployment strategies: Regular deployment,
Contact-based deployment, and CO2 deployment.
considered during the relay nodes placement process. Its contact time performance is
the worst. Compared to the contact-based deployment, CO2 nearly doubles the contact
time.
5
Conclusion
In this paper, we proposed a relay node deployment strategy for DTNs, named CO2 .
This 2-connected DTN can handle message delivery even when some of the relay nodes
fail, and the number of throwboxes deployed is relatively small. We have carried out
simulations based on Tuscaloosa bus transit system, and the simulation results have
demonstrated that CO2 achieves much better performance than the contact-oblivious
and the contact-based relay node deployment strategies. As a future work, we will further analyse the applicability of CO2 , and its performance working with other routing
protocols.
Acknowledgment
This work was supported in part by the National Science Foundation (NSF) under
grants CCF-0829827 and CCF-0829828.
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2-Connected DTN for Throwbox-based DTNs
9
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