Cds based energy efficient topology control algorithm in wireless sensor networks

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 10
CDS -BASED ENERGY EFFICIENT TOPOLOGY CONTROL
ALGORITHM IN WIRELESS SENSOR NETWORKS
G. N. Purohit1
, Seema Verma2
, Usha Sharma3
, Gargi Chauhan4
1
Depatment of Mathematics and Statistics, Banasthali University, Banasthali, Rajasthan-304022, India
2
Department of Electronics, Banasthali University, Banasthali, Rajasthan-304022, India
3
Depatment of Mathematics and Statistics, Banasthali University, Banasthali, Rajasthan-304022, India
4
Department of Computers, Banasthali University, Banasthali, Rajasthan-304022, India
Abstract
Wireless Sensor Networks (WSNs) are a self organized network which consists of large number of sensor nodes that collects the
data in a various environment [1, 2]. The sensors work on battery that have limited lifetime so it is a challenge to create an
energy efficient network that can reduce the energy consumption and interference in the network graph and thereby extend the
network lifetime [2]. For saving energy and extending network lifetime the topology is a well-known technique in WSNs and the
widely used topology control strategy is the construction of Connected Dominating Set (CDS) [3, 4]. In this paper, we construct a
CDS based energy efficient topology control algorithm i.e. GCDSTC for WSNs. The performance analysis includes the study of
GCDSTC algorithm in terms of complexity and compares it with EBTC (Energy Balanced Topology Control) algorithm. The
simulation results indicate that the GCDSTC algorithm reduce the energy consumption and interference in the network graph, in
order to enhance the network lifetime.
Keywords: Wireless Sensor Network (WSN), Connected Dominating Set (CDS), Topology Control (TC), etc.
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1. INTRODUCTION
Wireless Sensor Networks (WSNs) consists of a large
number of sensor nodes which is low-power, inexpensive
and unobtrusive [5]. It has been used to collect reliable and
accurate information in the distance and hazardous
environment [2]. And also it has been considered in civilian
application areas such as environmental monitoring, traffic
monitoring, disaster management, health monitoring and
military applications [2, 3].
In WSNs, the sensor node is a tiny device which is small in
size and has limited battery power and communication
capabilities due to which a node can cover only those sensor
nodes that have been occurred within its maximum
transmission range [2, 6]. Sensor nodes can gather
information from source, process it and transmit it to the
sink node or source node [6]. The sensor node has been run
on batteries so that it has been quite difficult to exchange or
recharge node batteries [5, 6]. The lifetime of the sensor
node depends on the lifetime of power resources so that the
improvement of energy efficiency and power management is
a critical issue in the WSNs [5].
Due to this problem, a virtual backbone in WSNs has been
created through which each node of the network can
transmitted data to the sink node effectively. A virtual
backbone is a set of active nodes through which the data has
been transmitted to the sink node by forwarding data to
other neighbouring active nodes [2].
In WSNs, the Connected Dominating Set (CDS) based
Topology Control (TC) is the energy efficient method [2].
Topology Control is one of the known strategies to save
energy or to control energy consumption in WSNs [3, 4].
The topology control has been divided into two components:
topology construction (reduced topology or change of the
initial topology) and topology maintenance (maintenance of
the reduced topology) while ensuring the connectivity and
coverage [2, 3, 7]. In CDS-based TC, the nodes in the CDS
are called dominators or active nodes, and the other nodes
which are not in the CDS are called dominatees or sleep
node. Therefore, CDS works as a virtual backbone in the
WSNs.
The main objective of this paper is to have a network which
has minimum energy consumption and interference and also
has maximum network lifetime. In this paper GCDSTC
algorithm in a directed graph has been constructed, models
the topology as a connected network and finds the set of
active nodes which form a CDS. The network has been
considered as a graph where the vertices correspond to the
nodes and the edges correspond to the links between these
vertices or nodes. One node is considered as the access point
(AP). All the data has been transferred from each node
should reach the AP.
The active nodes and sleep nodes of the network graph have
been found according to the degree and energy of the nodes
and through the active nodes the data has been transmitted to
the AP. These active nodes are also called the dominators.
For implementing this process, GCDSTC algorithm has
been developed which is based on the above approach.

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Through the proposed algorithm the links in the network
graph has been reduced due to which the energy
consumption and interference is minimized. In this way the
lifetime of the network can be extended.
The GCDSTC algorithm has been evaluated using
simulations and compared with the EBTC algorithm [13]. In
terms of complexity the result shows that the GCDSTC
algorithm performs better than EBTC (Energy Balanced
Topology Control) algorithm.
The rest of the paper is organized as follows. Section 2
reviews the related works. Section 3 describes the system
model. Section 4 presents problem statement. Section 5
presents MICDS algorithm. Section 6 describes and
analyzes the simulation results for the proposed algorithm.
Section 7 presents the performance evaluation. Section 8
concludes the paper.
2. RELATED WORK
To solve energy consumption problem in WSNs, many
topology control techniques have been proposed. Ya Xu et
al. (2003) have presented two topology control protocols
that prolong dense ad hoc networks lifetime while
maintaining connectivity, the ability for nodes to reach each
other. By identifying redundant nodes their protocols
conserve energy and turning off their radios. In WSNs the
topology control with per-node transmission power
adjustment has been shown to be effective with respect to
prolonging the lifetime of the network through power
conservation and increasing network capacity through better
spatial bandwidth reuse [8]. A simple yet efficient
distributed topology control algorithm has been proposed in
Jilei Liu and Baochun Li (2003). To compute per-node
minimum transmission power a distributed topology control
algorithm has been presented. To control topology process
for application nodes (AN) and base station (BS), the two-
tiered WSN is good initiative which usually resides in the
upper layer [9]. Jianping Pan et al. (2003) have proposed an
approach which maximizes the lifetime of the topological
network of the WSN by arranging BS location and inter-AN
relaying optimally. By using the computational geometry,
derived the optimal BS locations for mission criticality and
the efficiency of topology control validates the optimality
[10].
Topology control in heterogeneous WSN is more complex.
In Chun-Hsien Wu and Yeh-Ching Chung (2007), a
heterogeneous WSN deployment method based on irregular
sensor model has been proposed. The aim of this method is
to deal with the deployment problem of heterogeneous
sensor nodes with different communication and sensing
range. To approximate the behaviour of sensor nodes the
irregular sensor model has been proposed. The proposed
method can achieve higher coverage rate for the same
deployable sensor nodes [11].
Adopting the transmission power in WSNs is critical to
determine the performance of the network in terms of
energy efficiency, connectivity and spatial reuse since it has
direct impact on the physical network topology. C. Paolo
(2009) has proposed a solution i.e. cooperative and light
weight to the problem of controlling the local connectivity
in WSNs. Evaluate the topology control solution accurately;
a small-scale testbed based on Micaz sensor nodes is
deployed in indoor and outdoor scenarios. The solutions
provides the steady network connectivity while reducing the
overall power consumption [12].
A3 algorithm which is simple distributed and energy
efficient topology construction mechanism have been
proposed by Pedro M. Wightman and Miguel A. Labrador
(2008) that produces an approximate solution to form a sub-
optimal CDS. To allow the communication between any
arbitrary pair of nodes in the network a virtual backbone is
formed in a CDS-based topology control technique [3].
A.Karthikyan et al. (2013) have proposed a CDS based
topology control algorithm TCI which forms an energy
efficient virtual backbone. TCI achieves better connectivity
under topology maintenance and also provides better
sensing coverage [2].
3. SYSTEM MODEL AND NETWORK MODEL
The WSN is represented by a graph 𝐺 = 𝑉, 𝐸 , where
𝑉 = v1,v2,v3,….,vn denotes the set of all nodes in the
graph, including the access point (AP) and  
denotes the set of all edges referred to all the
communication links through which the data packets can be
transmitted from one node to another. All the
communication links have been destined to AP. Every data
packet from a node has been forwarded to its parent node so
that the entire data packet finally reached the AP.
If {vi,vj}  𝑉, the edge 𝑒 = vi,vj ∈ 𝐸 if and only if ‘vj’ is
located within the transmission range of ‘vi’ . In a directed
graph, the edge ‘e’ is called incident from ‘vi’, and incident
to ‘vj’, but in an undirected graph, the edge ‘e’ may be
incident to or from ‘vi’ and ‘vj’. In graph, each node ‘v’ can
communicated with a subset 𝑁 𝑣  𝑉of nodes, where each
node 𝑢 ∈ 𝑁 𝑣 is called the neighbor of ‘v’.
Topology control is one of the techniques that control
energy consumption in WSNs and also increase the lifetime
of the network. It is the management of network topology to
support the network requirements. In the reduced
constructed topology the CDS works as a virtual backbone.
4. PROBLEM STATEMENT
In WSNs, a virtual backbone is a connected dominating set
(CDS). And CDS based topology control is the method for
energy efficient in WSNs. This method has been used to
reduce the energy consumption and interference in network
graph and also used to extend the network lifetime.
GCDSTC algorithm has been constructed which is based on
the CDS based topology control and it has been used to
solve the energy consumption and interference problem in
the directed network graph. In this algorithm the active
nodes and sleep nodes of the network have been found and

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also reduce the number of active nodes and active links.
Through these active nodes the data has been transmitted to
the sink node in the network. So that GCDSTC algorithm
provides the network with less energy consumption and
interference and also provides the increment in the network
lifetime.
5. GCDSTC ALGORITHM
The algorithm constructs the topology which is based on the
CDS- based topology control. It will provide the
transmission of the data to the sink with less energy
consumption and interference due to which the network
lifetime has been extended.
Step 1: Arrange all the vertices of the network graph ‘G’ in
ascending order.
Step 2: Find the out_degree and in_degree of the vertices of
the network graph.
Step 3: If the out_degree(vi)==0 then
v2 sink node (SN)
Put sink node (SN) in active state (AS)
If the in_degree(vi)==0 then
vi Leaf Node (LN)
Put leaf node (LN) in sleep state (SS)
Step 4: Find the neighbor vertices of the sink node
Nsn vi,vi+1,…….,vn
Put the neighboring vertices of the sink node (Nsn) in
active state (AS)
If Nsni==LN then
Nsni SS
Step5: Remove a sink node, leaf node and neighboring node
of sink node from the vertex set ‘V’ of the network graph
‘G’.
V'=[V-(SN+LN+Nsn)]
Step 6: Find the energy of the V' vertices of the network
graph ‘G’
Veng= v1e,v2e,.........,vne
Step 7: Arrange the energy and degree of all the vertices of
the vertex set V' of the network graph ‘G’ in descending
order.
Calculate
If vi(E)>vi+1(E) then
vi(E) ENi
vi(D) Degi
else
vi+1(E) ENi
vi+1(D) Degi
If vi(E)==vi+1(E) then
If vi(D)>vi+1(D) then
vi(E) ENi
vi(D) Degi
else
vi+1(E) ENi
vi+1(D) Degi
Step 8: After finding the energy and degree of the vertices of
the network graph ‘G’ in some order. The user selects the
vertices in active state (AS) and sleeps state (SS) according
to its requirements for the transmission. Put the active state
vertices in connected dominating set (CDS) as dominators
and the sleep state vertices as dominatees.
Thus, the topology of the network graph that has minimum
number of CDS have been obtained.
6. SIMULATION RESULTS
In this section, the performance of GCDSTC algorithm has
been analyzed by simulation in MATLAB version R2007a.
Our evaluations are based on the finding of active nodes and
sleep nodes of the network graph. Using active nodes, the
minimum number of the CDS is found out. Through the
CDS we can transmit all the data to the sink node. And this
can also reduce the energy wastage and collisions or
interference between the nodes of the graph and extend the
lifetime of the network.
The following procedures have been simulated on
MATLAB for finding the active nodes and sleep nodes in
the network graph:
6.1 Procedure GCDSTC
Input: Graph G= (V, E)
‘n’ is the total number of vertices in the graph
‘total’ is the degree of the vertices of the graph
‘AS’ is the active state nodes in the graph
‘SS’ is the sleep state nodes in the graph
‘in_deg’ is the indegree of the vertex
‘out_deg’ is the outdegree of the vertex
For all the vertices ‘V’ of the graph stored at ‘i’ do
Enter the indegree of vertices of ‘i’ stored at ‘in_deg’
Enter the outdegree of vertices of ‘i’ stored at ‘out_deg’
Check if out_degree is zero then
Sink node is ‘i’ whose outdegree is zero
Put that ‘sink node’ in the AS
End
Check if in_degree is zero then
Leaf node is ‘i’ whose in_degree is zero
Put that ‘leaf node’ in the SS
End
Calculate total degree of the all the vertices
End
Check if ‘i’ is sink node then
Enter the adjacent vertices of ‘i’ stored at ‘Nsink’
End
End
Put the ‘Nsink’ in AS
Remove sink node, leaf node and Nsink from the ‘V’
V' is the remaining vertices after the removal of sink node,
leaf node and Nsink
V'= V-(sink node+leaf node+Nsink)
Check if i~= V'
Enter the energy of vertices of ‘i’ stored at ‘ener’

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Find the total energy and degree of each of vertices in V' and
store that value in ‘ee’ and ‘d’.
Sort the ‘ee’ and ‘d’ value in descending order and store that
value in ‘eg’ and ‘deg’.
User selects the vertices from ‘eg’ according to its
requirement. And put that vertices in AS and the remaining
vertices in SS.
Therefore, in the above procedure ‘AS’ and ‘SS’ is the
answer. And the active state and sleep state have been
represented in the form of matrix.
6.2 Analysis
We have analyzed that the transmission of the data in the
network graph using CDS based topology control methods
depends on the active state nodes of the network graph.
Table 1 Active state nodes of the network graph
Number of
vertices
Number of
edges in
graph
Number of
edges in
topology
Active
State
Nodes
11 18 10 7
20 30 19 10
25 38 24 18
30 46 29 19
40 71 39 21
Fig 1 Simulation results of the algorithm
From Table 1 and Figure 1, the four cases have been
analyzed:
1. Minimum number of edges in the topology of the
network is equal to the (n-1) vertices.
2. Topology of the network graph has minimum number
of vertices in the active state which means the
network graph has minimum number of connected
dominating set (CDS).
3. Maximum transmission through each nodes is (n-SN-
LN-Nsink) where ‘n’ is the number of vertices, ‘SN’
is the sink node of the network graph, ‘LN’ is the leaf
node of the network graph and ‘Nsink’ is the
neighbor nodes of the sink node in the network
graph.
4. Minimum vertices in active state are (SN+Nsink).
7. PERFORMANCE EVALUATION
In this section, we have been compared our algorithm i.e.
GCDSTC algorithm with EBTC algorithm in terms of time
complexity [13]. In our algorithm, the time complexity of
the algorithm is O(n). On the other hand, the overall
complexity of the EBTC (Energy Balanced Topology
Control) algorithm is O(n2
) in terms of time, where ‘n’ is the
total number of nodes in the network. Clearly, our algorithm
has less time complexity than EBTC algorithm for running
the whole process.
8. CONCLUSION
In this paper, a new topology control method has been
proposed which is based on CDS-based topology control
method that is GCDSTC algorithm. The algorithm finds the
active state nodes of the network graph that has been created
the minimum CDS of the network graph. Therefore, the
CDS or active state nodes have been used for the
transmission of the data packets to another node or sink
node. The resulting assignment can guarantee that there is
no interference between the nodes due to which the
consumption of energy is decreased and the lifetime of the
network is increased. The complexity of the GCDSTC
algorithm has been calculated which is O(n) and compared it
with EBTC (Energy Balanced Topology Control) algorithm
and find that the GCDSTC algorithm is better than the
EBTC algorithm.
ACKNOWLEDGMENTS
One of the authors of this paper, Gargi Chauhan
acknowledges UGC, New Delhi for the financial assistance
for carrying out this research.
REFERENCES
[1]. Jian Ma, Chen Qian and Qian Zhang, “Opportunistic
Transmission based QoS Topology Control in Wireless
Sensor Networks”,IEEE, 2008, pp 1-6.
[2]. A. Karthikeyan, T. Shankar, V. Srividhya, Siva Charan
Reddy. V and Sandeep Kommineni, “Topology Control
Algorithm for Better Sensing Coverage with Connectivity in
Wireless Sensor Networks”, Journal of Theoretical and
Applied Information Technology, Vol. 52 No.3, 30th June
2013.
[3]. Pedro M. Wightman and Miguel A. Labrador, “A3: A
Topology Construction Algorithm for Wireless Sensor
Networks”, GLOBECOM 2008: 346-351.
[4]. Parikha Chawla, Parmender Singh and Taruna Sikka,
“Enhance Throughput in Wireless Sensor Network Using
Topology Control Approach”, International Journal of Soft
0
10
20
30
40
50
60
70
80
1 2 3 4 5
No. of
vertices
No. of edges
in graph
No. of edges
in topology
Active State
Node

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Computing and Engineering (IJSCE) ISSN: 2231-2307,
Volume-2, Issue-3, July 2012.
[5]. Alexis Papadimitriou, Dimitrios Katsaros and Yannis
Manolopoulos , “ Topology control algorithms for wireless
sensor networks: A critical survey”, CompSysTech 2010: 1-
10.
[6]. Ramakant Sharma and Praveen Kaushik, “Network
Coding Based Virtual Backbone for Wireless Sensor
Network”, International Journal of Computer Science and
Information Technologies (IJCSIT), Vol. 3 (3) , 2012,4341 –
4344.
[7]. G.N. Purohit and Usha Sharma, “Topology Control for
Energy Conservation in Wireless Sensor Network”, Int. J.
Contemp. Math. Sciences, Vol. 7, 2012, no. 2, 53 – 6.
[8]. Ya Xu, S. Bien, Y. Mori, J. Heidemann and D. Estrin,
“Topology Control Protocols to Conserve Energy in
Wireless Ad Hoc Networks”, CENS Technical Report ,
2003.
[9]. Jilei Liu and Baochun Li, “Distributed Topology
Control in Wireless Sensor Networks with Asymmetric
Links”, GLOBECOM 2003 – IEEE Global
Telecommunications Conference, no. 1, pp. 1257-1262, Dec
2003.
[10]. Jianping Pan, Y. Thomas Hou, Lin Cai, Yi Shi and
Sherman X. Shen, "Topology control for wireless sensor
networks" , ACM on Mobile Computing and Networking,
pp. 286-299, Sep. 2003.
[11]. Chun-Hsien Wu and Yeh-Ching Chung,
"Heterogeneous Wireless Sensor Network Deployment and
Topology Control Based on Irregular Sensor Model",
Advances in Grid and Pervasive Computing Lecture Notes
in Computer Science, Volume 4459/2007, 2007.
[12]. C. Paolo, C. Matteo, B. Stefano, and C. Luca, "A
Cooperative Approach for Topology Control in Wireless
Sensor Networks", Pervasive and Mobile Computing, vol. 5,
no. 5, 2009, pp. 526 –541.
[13]. X. Chu and H. Sethu ,“An Energy Balanced Dynamic
Topology Control Algorithm for Improved Network
Lifetime”, Computer Science Networking and Internet
Architecture 12 sep 2013.

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Cds based energy efficient topology control algorithm in wireless sensor networks