Improving Route Discovery Using Stable Connected Dominating Set in MANETS

International journal on applications of graph theory in wireless ad hoc networks and sensor networks
(GRAPH-HOC) Vol.4, No.1, March 2012
DOI : 10.5121/jgraphoc.2012.4102 15
IMPROVING ROUTE DISCOVERY USING STABLE
CONNECTED DOMINATING SET IN MANETS
Ramalakshmi R1
and Radhakrishnan S2
1
Assistant Professor, 2
Senior Professor
2
Department of Computer Science Engineering, Kalasalingam University, India
rama,srk@klu.ac.in
ABSTRACT
A Connected Dominating Set (CDS) based virtual backbone plays an important role in wireless ad hoc
networks for efficient routing and broadcasting. Each node in the network can select some of its 1-hop
neighbors as Multi Point Relay (MPR) to cover all its 2-hop neighbors. A MPR based CDS is a promising
approach for broadcasting. A node in the CDS consumes more energy and the energy depletes quickly
than non dominating nodes. Although previous CDS construction algorithms achieve good results in terms
of the size of CDS, a minimum size CDS does not necessarily guarantee an optimal network performance
from an energy efficient point of view. In this paper, we propose a distributed algorithm for energy efficient
stable MPR based CDS construction to extend the lifetime of ad hoc wireless networks by considering
energy and velocity of nodes. We have also implemented route discovery protocol to make use of the CDS
nodes to relay route request messages. The simulation results show that our algorithm increases the
lifetime up to 25% than previous works and 60% reduction in the route request messages during route
discovery process.
KEYWORDS
Connected Dominating Set, Multi Point Relay, Ad Hoc Networks, Energy Efficient, Route Discovery
1. INTRODUCTION
Wireless ad hoc networks are self configuring networks, can be deployed for many applications
such as automated battlefield, search and rescue and disaster relief. Mobile ad hoc network
(MANET) consists of wireless nodes that communicate with each other without any
infrastructure. A communication session is achieved either through a single hop radio
transmission if the communication parties are within the transmission range, or through relaying
by intermediate nodes otherwise. Two important features of an ad hoc network are its dynamic
topology and resource limitation. These features make routing decision very challenging. Every
node in mobile ad hoc networks can move in any direction at any time and any speed. A
temporary infrastructure or a virtual backbone can be formed to provide communication. This
virtual backbone may be broken due to the node movement.
A network can be modeled as a Unit Disk Graphs (UDG) where two nodes are connected if they
are within each other’s transmission range. To support various network functions, some wireless
nodes are selected to form a virtual backbone. It is proved that multipoint relaying (MPR) is an
efficient stated for on-the-fly broadcasting in Mobile Ad Hoc Networks. The relaying nodes
which are selected by the source node are responsible for flooding a receiving packet. A
connected dominating set (CDS) consists of all the relay nodes. Finding connected dominating

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set is a NP-hard problem. There are many algorithms based on MPR to reduce the size of the
CDS. In the original MPR based CDS schemes [1, 2, 11, 12, 13], nodes are chosen based on node
id or node degree. The CDS selection has to consider other information such as energy,
bandwidth and mobility in order to provide suitable links for some specific applications.
In this paper, we propose an approach to generate quality connected dominating set based on
MPR by considering the energy and link velocity factors. There are many works on MPR based
CDS construction [1, 2, 11, 12, 13, 14], but none of work considered the energy of nodes and
their mobility together for the construction work.
1.1 Problem Definitions
The connected dominating set or virtual backbone is proposed to facilitate routing, broadcasting
and establishing a dynamic infrastructure. Minimizing the CDS produces a simpler abstracted
topology of the MANET and allows for using shorter routes between any pair of hosts. A
wireless network is usually modeled as a unit disk graph G = (V, E) where V is the set of nodes
and E is the set of links in the network. Each node has uniform transmission range R. Each node
in V is associated with coordination in 2-D Euclidean space. A wireless link (u,v) ∈ E if and only
if the Euclidean distance between nodes u and v is smaller than transmission range R.
Connected Dominating set:
A subset S ⊆ V is called a dominating set (DS) of G if { v ∈V, either v is in S or has an adjacent
neighbor in S}. The subset S is called Connected Dominating Set if the graph G’, induced by S is
connected i.e, G’[S] is connected.
Multipoint Relay:
For a given a graph G=(V,E) and a node v ∈ V, let N1(v) and N2(v) represent the set of 1-hop and
2-hop neighbors of v, respectively. MPR asks for a minimum size subset MPR of N1(v) such that
N2(v) is covered by MPR.
The rest of the paper is organized as follows: Section 2 describes the works related to CDS
construction; Section 3 describes the energy efficient stable connected dominating set
construction algorithm and route discovery process using CDS nodes. We describe our simulation
environment and the performance metrics in Section 4. Section 5 concludes the paper.
2. RELATED WORKS
There are many work on MPR based CDS construction for efficient broadcasting in Manet. Adjih
et al [12] proposed a source independent MPR called the MPR-CDS. The algorithm starts by
having every node v calculate its source dependent MPR. After that every node v decides whether
it belongs to the MPR-CDS or not according to the following simple rules:
• Rule 1: Node u ∈ MPR-CDS iff v has the smallest ID in its 1-hop neighborhood.
• Rule 2: Node v ∈MPR-CDS iff v ∈ w's MPR where w's ID is the smallest in v's 1-hop
neighborhood.
In [1] Wu, has noticed that in many occasions nodes added by rule-1 of MPR-CDS algorithms are
useless. Moreover the algorithm used to calculate the source dependent MPR does not benefit
from Rule-2 of the MPR-CDS algorithm. In [1] Wu modified Rule 1 as follows:

17
• EMPR: node v ∈ MPR-CDS iff v has the smallest ID in its 1-hop neighborhood and v
has at least two unconnected neighbors.
Moreover, Wu modified the MPR calculation algorithm in [12] by having every node v start by
adding all its free neighbors to its MPR set. A node u is a free neighbor of node v iff u ∈N(v) and
v is not the smallest ID neighbor of u.
Chen et al [11] observed that the node degree is more related to the size of a CDS than the node
ID and three improvements are proposed. They replaced the EMPR rule with two rules based on
degree called DEMPR.
• Rule 1: node v ∈MPR-CDS if v has the largest node degree among all its one-hop
neighbors and v has two unconnected neighbors.
• Rule 2: node v ∈ MPR-CDS if v has been selected as an MPR and its selector has the
largest node degree among its one-hop neighbors.
Badis et al [13], proposed heuristic referred to as the QoS based MPR-1(QMPR-1) follows the
same steps as the original MPR heuristic but it modifies the tie breaking procedure. Instead of a
maximum node degree, a node with high bandwidth is chosen when multiple choices exist.
There are few works on energy efficient CDS construction. In [6] Kim extended the Mac-layer
timer based connected dominating set protocol by considering energy level at each node to
construct energy aware CDS. In [7], Ruiyun Yu proposed an energy efficient dominating tree
construction (EEDTC) algorithm with two phases, marking phases followed by connection phase.
In the marking phase, a Maximal Independent Set (MIS) is constructed and connectors are added
to make it as CDS. In [14], Wu proposed a method to calculate power aware connected
dominating set. They used degree and residual energy level of nodes to reduce the CDS size to
prolong the lifespan of the nodes in the CDS.
Only few works are done in stable connected dominating set construction. In [15], Change
proposed Dynamic Power-aware and Stability-aware Multipoint relays which avoid selecting the
border nodes as the forwarding nodes. They used power adaptive broadcasting by reducing the
transmission range of mobile nodes to save energy. The range buffer based approach is proposed
to further enhance the stability of the forwarding nodes. In [4], Meganathan proposed an
algorithm to determine stable connected dominating set based node velocities. Their algorithm
prefers slow moving nodes with lower velocity rather than the usual approach of preferring nodes
with a larger number of uncovered neighbors. They compared their method with another work
which is based on node degree.
In [17], dominant pruning rules are applied to distribute the route request messages in AODV
protocol. Authors have proposed an algorithm for Two Hop Horizon Pruning in [16] to distribute
route request messages using 2-hop dominating set. They have applied virtual radio range and
radio range to remove some of the neighbors from 1-hop neighbor list.
Although previous CDS construction algorithms achieve good results in terms of the size of the
CDS, a minimum size CDS does not necessarily guarantee the optimal network performance from
an energy efficient point of view. This motivated us to construct an energy efficient stable
connected dominating set construction to prolong the network lifetime.

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3. ENERGY AWARE STABLE CONNECTED DOMINATING SET CONSTRUCTION
ALGORITHM (EAS-CDS)
2.1. Notations and Assumptions
We assume that every node in the network has same transmission range R. Two nodes are
connected if the Euclidean distance between the nodes is less than R. We used the notations for
our algorithm as in Table 1.
Table 1 Notations
N1(u) Open neighbor set of node u
N1[u] Closed neighbor set of node u N[u] = N(u) U {u}
N2(u) 2-hop neighbor set of node u
Ergx remaining residual energy at node x
Velx Velocity of node x
MPR(u) MPR set of node u
Degu No of neighbors of u
2.2. EES-CDS Algorithm
Our algorithm for energy aware stable connected dominating set construction (EAS-CDS)
consists of three phases: Neighbor Discovery Phase, CDS Formation Phase and pruning phase.
During the Neighbor Discovery Phase, there is an initial exchange of messages via which a node
u, made aware of its N2(u). In the CDS Formation Phase, a node u locally selects a set MPR(u) of
its N1(u) as its multipoint relays by using simple greedy algorithm and in the pruning phase, two
rules are applied to reduce the connected dominating set size.
In [1], Wu proposed a simple decentralized algorithm for the formation of connected dominating
set in ad hoc networks. This algorithm is based on marking process. We have used their algorithm
to choose the multipoint relays for a node. In [14], Dai proposed two rules for power aware CDS
construction using node degree and residual energy level of nodes. In this work, we have
modified the rules proposed by [14] with energy level and velocity to prolong the stability and to
reduce the size of a connected dominating set generated from the marking process.
Let the graph induced by CDS be G’.
Rule 1: Consider two marked vertices v and u in G’. The marker of v is changed to gray if one
of the following conditions holds:
i) N[v] ⊆N[u] in G and Ergv < Ergu
ii) N[v] ⊆ N[u] in G and Velv > Velu when Ergv = Ergu
iii) N[v] ⊆N[u] in G and Degv < Degu when Ergv=Ergu and Velv = Velu
The above rules indicate that when the closed neighbor set of v is covered by that of u, vertex v
can be removed from G’ if the energy level of v is smaller than u. Velocity is used to break a tie
when energy levels of u and v are same. Degree is used to break the tie if both energy levels and
velocity of u and v are same. Node ID can be used to break the tie, in case all the values are same.

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Rule 2: Assume that u and w are two marked neighbors of marked vertex v in G’. The marker of
v can be changed to Gray if one of the following conditions holds:
1. N(v) ⊆ N(u) U N(w), but N(u) ⊄ N(v) U N(w) and N(w) ⊄ N(u) U N(v) in G
2. N(v) ⊆ N(u) U N(w) and N(u) ⊆ N(v) U N(w), but N(w) ⊄ N(u) U N(v) in G and one of
the following conditions holds:
a. Ergv < Ergu or
b. Ergv = Ergu and Velv > Velu or
c. Ergv = Ergu and Velv = Velu and Degv < Degu
3. N(v) ⊆ N(u) U N(w) and N(u) ⊆N(v) U N(w), but N(w) ⊆ N(u) U N(v) in G and one of the
following conditions holds:
a. Ergv < Ergu and Ergv < Ergw or
b. Ergv = Ergu < Ergw and Velv > Velu or Degv <Degu when Velv = Velu
c. Ergv = Ergu = Ergw then
i. Velv > Velu and Velv > Velw or
ii. Velv = Velu > Velw and Degv < degu or
iii. Velv = Velu = Velw and Degv = min{Degv, Degu, Degv}
The above rule indicates that when v is covered by u and w; the conditions are
1) if neither u or w is covered by the other two among u, v and w then v is unmarked
2) if neighbor set of v is covered by u, w and neighbor set of u is covered by v, w but neighbors
of w are not covered by u, v then v is unmarked if the energy level of v is smaller than u.
Velocity is used to break the tie if energy levels are same. Degree is used to break the tie if
both energy and velocity values are same.
3) If neighbor set of v, u and w are covered by the other two among u, v and w then node v is
unmarked with conditions: energy level of v is less than v and w; the energy level of v is
same as u but smaller than w, velocity value is used for unmarking. If velocity values are
same then degree of v is used to break the tie.
The procedure for the energy efficient CDS construction algorithm is given in Table2.
It is clear that node u only has to wait for the information about its two hop neighbors. The set of
all MPR is a connected dominating set of the entire network. The node terminates the
construction phase by communicating its final decision to all its neighbors. The result of this
algorithm for a sample network with 10 nodes is given in figure 1. The MPR of node 1 is 2 to
cover its 2-hop neighbor 10. The 2-hop neighbors of node 2 are {4, 5, 8, and 9} and its MPR is
10. Likewise, MPR(9)=7 and MPR(10)=9. Thus the final CDS consists of nodes {2, 10, 9, and 7}.

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Table 2: Algorithm for Energy Aware Stable CDS Construction
Algorithm: EAS-CDS
Input: An undirected graph G(V, E)
Output: Minimum Size CDS which is energy efficient and Stable.
• Neighbor Discovery phase
Nodes periodically exchange hello messages for neighbor discovery. Every node sends
and receives hello messages but does not forward them. A hello message generated by
a node u contains its ID, Energy (Ergu), Velocity (Velu) and list of neighbors N(u). This
one hop neighborhood exchange enables every node u to obtain its two hop
neighborhood information.
• CDS Formation Phase
MPR Selection
i. Initially all nodes are in White colour
ii.Every node v assigns its N1(v) to MPR(v) if it has two unconnected
neighbors.
iii. Mark all the nodes in MPR(v) to Black Colour
iv. Marks all the neighbors of MPR(v) to Gray Colour
• Pruning Phase
Apply Proposed Rule 1 and Rule 2 to all dominating nodes.
Figure 1: CDS = {2, 7, 9, and 10}

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2.3. Route Discovery using EES-CDS
On demand routing protocols like AODV, DSR for ad hoc networks use route discovery process
to find the path between source and destination. In [18], the source node initiates the route
discovery when it has no route to the destination. It broadcasts a route request packet (RREQ) to
its neighbors. Each receiving node in turn broadcasts RREQ packet. This process is repeated until
the packet reaches the destination and the destination node will send the route reply message
(RREP) to the source. This type of route discovery leads to broadcast storm problem. To
overcome this problem, we implemented the route discovery process in AODV using the CDS
nodes only. When a CDS node receives a RREQ packet, it broadcasts the packet and non-CDS
nodes only receive the packet. The non-CDS are not rebroadcasting the RREQ packets. Thus the
number of RREQ packet transmission is reduced and the network congestion is avoided.
4. SIMULATION RESULTS AND ANALYSIS
4.1. Simulation Environment
We implemented our algorithm EAS-CDS in ns-2.34. To evaluate the performance of our
algorithm, we also implemented the approach proposed by Wu in [1] and Meganathan in [4]. In
this section, the simulation results are reported and analyzed. To generate a network, n nodes are
randomly placed in a 1000m x 1000m region. Each node has uniform transmission range 250m
and is associated with an initial energy values from 1J to 15J. In our simulations, the number of
nodes n has been assigned the values 50, 100, 150, 200, 250. This allows us to test our algorithm
from sparse to dense networks. Any two nodes distance less than the transmission range are
considered neighbors. Each node moves randomly in this area with a speed in the range [0 .. Vmax]
and pause time of 100s. The values of Vmax are 5, 10 and 25m/s. Each simulation is conducted
for 600s and it is repeated 10 times. The parameters used in our simulation are listed in Table 3.
Table 3 : Simulation Parameters
Network Area 1000m2
Number of Nodes 50..250
Transmission Range 250m
Mobility Speed 5m/s, 15m/s, 25m/s
Initial Energy 1J..15J.
Energy for transmission 1.4W
Energy for Receiving 1.0W
Idle energy 0.013W
Pause time 100s
Simulation Time 600s
Propagation Model Two-ray Ground
MAC IEEE 802.11
Antenna Omni Antenna
Mobility Model Random Way Point
Data Traffic CBR packets with 512 Bytes
No of Sources 20
Packet Sending Rate 5 packets /sec

22
We implemented this algorithm by using three messages: The first message is for exchanging the
list of neighbors, the second is used by a node u to communicate to a neighbor v for which u is the
MPR selector and the final one is used by a node to make every neighbor aware of its final
decision.
4.2. Result Analysis
We measured the performance of our work in terms of
• CDS Size
It describes the no of nodes included in the CDS to act as broadcast relay nodes. Figure 4.1
shows the average no of nodes included in the CDS with different mobility values 5m/s, 15m./s
and 25m/s. The results show that CDS generated by our algorithm is larger than Wu [1] and less
than Meganathan [4]. In [1], Wu used node degree for selection and Meganathan in [4] selected
nodes with only lower velocity. The average size of the CDS increases with network density.
(a) Velocity [0..5] (b) Velocity [0..25]
Figure 4.1 CDS Size
• CDS Stability
The simulation stops when the energy level of at least one node becomes 0. Figure 4.2 shows
comparison of our work with Wu [1] and Meganathan [4] in terms of lifetime. Our work
outperforms well than the other two works because nodes in the CDS generated by our algorithm
has high energy level and minimum velocity. The CDS stability is high when the node velocity is
low. In [4], priority is given to slow moving nodes but they don’t consider the energy level of the
nodes.

23
(a) Velocity [0..5 ] (b) Velocity [0..25 ]
Figure 4.2 CDS Lifetime
• Packet Delivery Ratio
We have implemented the route discovery process of AODV using only CDS nodes. As in
Figure 1, only nodes {2, 7, 9, and 10} will relay the RREQ packets and other nodes will receive
the packet, they don’t broadcast. We tested this route discovery process with different network
size. We generated 100 CBR packets of 512 bytes per second with pause time 100s. The AODV
protocol uses simple flooding mechanism, so the no of packet transmission is high. But, only
CDS nodes are used for RREQ transmission, the number of RREQ transmission is minimum than
AODV. The results are given in Figure 4.3. The packet delivery ratio is given in Figure 4.4. We
assumed 30 sources with different packet flows. The results show that the delivery ratio is high
when packet flow is less, otherwise the performance is close to AODV.
Figure 4.3 #RREQ Transmission Figure 4.4 Packet Delivery Ratio
5. CONCLUSION AND FUTURE WORK
In this work, we have proposed an algorithm for power efficient stable connected dominating set
construction. We have proposed two rules to reduce the connected dominating size and to prolong
the life span of the nodes with residual energy level and velocity. We have also implemented the
route discovery process of AODV using CDS nodes. The results from the simulation show that
our work outperforms in terms of stability and it increases the lifetime by 25% to 30%. The route

24
discovery process over CDS reduces the route request transmission by 60%. We plan to verify the
performance of OLSR protocols using our MPR nodes as relays.
ACKNOWLEDGEMENT
The authors would like to thank the Project Coordinator and Project Directors of TIFAC-CORE
in Network Engineering, Kalasalingam University for providing the infrastructure facilities in
Open Source Technology Laboratory and also thank Kalasalingam Anand Ammal Charities for
providing professional development allowance for this work.
References
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MANETs”, IEEE Transaction on Computers, vol. 55, pp334-347.
[2] J.Wu, (2003) “An Enhanced Approach to Determine a Small Forward Node Set Based on Multipoint
Relay”, Proc. IEEE Semi-Ann. Vehicular Tech. Conference.
[3] N.Meganathan, & A.Farago, (2008) “On the Stability of Paths, Steiner Trees and Connected
Dominating Sets in Mobile Ad Hoc Networks”, Ad hoc Networks, Vol. 6, pp744-769.
[4] N.Maganathan, (2010) “Use of Minimum Node Velocity Based Stable Connected Dominating Sets
for Mobile Ad hoc Networks”, IJCA special issues on “Mobile” Ad-Hoc Networks, 89-96.
[5] F.Dai & J.Wu, (2004) “An Extended Localized algorithms for Connected Dominating Set Formation
in Ad Hoc Wireless Networks”, IEEE Trans. Parallel and Distributed Systems, Vol. 15.
[6] B. Kim,D.Zhou, J.Yang & M.Sunl, (2005) “Energy-Aware Connected Dominating Set Construction
in Mobile Ad Hoc Networks,” Auburn University Technical Report, CSSE05-07, 2005
[7] Ruiyun Yu, Xingwei Wang, & Sajal K.Das, (2009) “EEDTC: Energy-Efficient Dominating tree
Construction in Multi-hop Wireless Networks”, Pervasive and Mobile Computing, Vol. 5, pp318-33.
[8] S. Funke, A. Kesselman, U. Meyer, & M. Segal. (2006) “A simple improved distributed algorithm for
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[9] P.Wan, K K.M. Alzoubi & O. Frieder, (2002) “Distributed construction of connected dominating set
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[15] Change Y, Ting Y, & Wu S, (2007) “Power-Efficient and Path-Stable Broadcasting scheme for
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[17] Marc Mosko, J.J. Garcia-Luna-Aceves & Charles E. Perkins (2003), “Distribution of Route Requests
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Authors
RAMALAKSHMI R received her Master of Engineering degree from Anna University, Chennai. She is doing
her doctoral program. She is a member of CSI, ISTE and Network Technology group of TIFAC-CORE in
Network Engineering. Her areas of interest include Wireless Sensor Networks, Mobile Ad Hoc Networks
and Graph Theory applications in ad hoc networks.
RADHAKRISHNAN S received his Master of Technology and Doctorate in Philosophy from Banaras Hindu
University, Banaras. He is the Senior Professor in Computer Science Department of Kalasalingam
University, Srivilliputhur. He is a member of ISTE. He is the Project Director of Network Technology
Group of TIFAC-CORE in Network Engineering. He has produced 12 Ph.D’s and currently guiding
research scholars in the areas of Network Security, Sensor Networks, Evolutionary optimization, Mesh
Networks and Cloud Computing.

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