An Energy Efficient Spectrum Sensing in Cognitive
Radio Wireless Sensor Networks
Atchutananda Surampudi and Krishnamoorthy Kalimuthu

arXiv:1711.09255v1 [] 25 Nov 2017

Department of Electronics and Communication Engineering,
SRM University,
Chennai, India 603203.
atchutasurampudi812@gmail.com, kalimuthu.k@ktr.srmuniv.ac.in

Abstract—The cognitive radio wireless sensor networks
have become an integral part of communicating spectrum
information to the fusion centre, in a cooperative spectrum
sensing environment. A group of battery operated sensors or
nodes, sensing information about spectrum availability in the
radio links, needs an energy efficient strategy to pass on the
binary information. A proper routing method through intelligent
decision making can be a good solution. In this paper, an energy
efficient routing protocol has been introduced and a performance
comparison is made with the existing system in terms of it’s
energy consumption. The proposed routing technique considers
both uniform and non-uniform clustering strategies and proves
to be an energy efficient method of communicaton.
Index Terms: Cluster head, clustering, minimum spanning tree,
prims algorithm, routing.

I. I NTRODUCTION
The cognitive radio wireless sensor network (CR-WSN) is
a group of sensors, performing the task of sensing spectrum
availability information in a cognitive radio (CR) environment
[1]. The spectrum sensing can happen in a cooperative or
a non-cooperative way. In a cooperative spectrum sensing
these sensor nodes communicate their respective information
to an information sink called the fusion centre (FC), by
forming an interconnected wireless network [2]. At the FC
the data is aggregated and further decision for utilization of
spectrum is taken. So, once a wireless network is formed,
the one bit information present with each node is routed
along a wireless route with the help of routing protocols and
associated algorithms defining the protocols. There have been
several routing protocols proposed for routing information
along these sensor networks [3], [4], [5]. In this paper, the
focus has been made on the routing strategy used by sensors
which sense spectrum availability information in cognitive
radio systems.
The existing system of sending the binary sensing
information is done by hierarchical clustering using the low
energy adaptive clustering hierarchy (LEACH) protocol and
there have been several developments on the same [6], [7]. In
such systems, energy efficiency has been the most important
key point of the network routing design. Here we introduce
a novel routing protocol which combines with hierarchical
clustering and inter cluster routing to communicate the

information to the FC in an energy efficient manner. This
research puts light on the energy demand for the sensors
to send the sensed information to the FC directly or along
a certain intelligently decided path. A greedy algorithm
called the prim’s algorithm [8] is used to form a low energy
minimum spanning tree at every round of cluster formation.
The rest depends on the elected cluster head to take an
energy efficient decision to send the sensed information. In
this analysis, both uniform and non uniform size of clustering
have been considered.
This paper describes the proposed methodology of the new
routing protocol and compares the energy parameter with
that of the existing protocol, the LEACH. This paper is
organized as follows: In section II, the clustering strategy
of existing system is described and the proposed method of
communication protocol is analyzed in section III. Section IV
discusses the results and comparisons made with the existing
system. Finally the paper concludes with section V.
II. E XISTING S YSTEM
A group of Wireless Sensors are placed for spectrum sensing
along with the FC, where the data is aggregated. These nodes
are further differentiated into advanced and normal nodes and
are powered by their residual energy. A probability of selection
of Cluster Head (CH) is pre-specified. The system undergoes
a certain number of rounds and selection of CH is based on
distance and energy parameters of each node from the FC.
From [9], the condition for electing the CH for each round is
given as

T (s) =





p
1 ;s
1−p(rmod p
)

∈ G,
(1)

0, otherwise,

where r is the current round of clustering, G is the set of nodes
that have not been CHs in the last 1/p rounds. Each sensor
node decides independently of other senor nodes whether it
will claim to be a CH or not, by picking a random value
between 0 and 1 and comparing it with a threshold T(s) based
on a user-specified probability p. After the CH is selected, it
aggregates information from its cluster of nodes and calculates
its distance from the fusion centre. This distance is used to

calculate the cost to send the one bit information to the fusion
centre soon after election and the sensing information is sent.
In this method, all elected CHs transmit the aggregated data to
the FC directly. If the CH is far away to the FC, then it needs
more energy to transmit. So, the overall lifetime becomes very
short and a considerable number of nodes die after certain
rounds.
III. P ROPOSED M ETHOD
In the proposed method an energy efficient routing protocol
has been introduced. As a further extension of the previously
described method, the proposed method introduces the
process of intelligent decision making and forwarding the
information along a minimum energy route by forming a
minimum spanning tree (MST) using a greedy algorithm
called the Prim’s algorithm. A greedy algorithm formulates
a certain path in a stepwise manner by greedily choosing
the next step with respect to the previous one, according to
the least value of the available metric. The Prim’s algorithm
finds an MST in a connected, weighted graph. It begins with
a vertex and no edges and then applies the greedy rule,
Add an edge of minimum weight that has one vertex in the
current tree and the other not in the current tree.
Using this Prims algorithm, the proposed communication is
made as follows
• Node is elected as a CH using (1). But it doesn’t directly
send the sensed information to the FC soon after election.
• At once all the required ’M’ number of CH’s are elected
for the ’rth’ round and each CH now calculates it’s
Cartesian distance from the fusion centre and distances
from every other node.
• Using this information an adjacency matrix A(i,j) is
formed, with distance as the metric.
• MST is formed using the Prims algorithm. This connects
all the elected ’M’ number of CH’s along a minimum
energy route in that round. This tree varies dynamically
with every round.
Now, as a development with respect to the earlier protocol,
each CH maintains a sensing table which provides a comprehensive information about the one bit information sensed by
itself and all other CH’s that are present. So, the CH sends
’M’ bit information rather than one bit along an intelligently
decided path. It calculates the respective cost (C) to the fusion
centre and to the nearest CH along the minimum spanning tree,
using the previously calculated distances as

 (Et + Ed )M + Ef M d2 ; d ≤ do ,
C=
(2)

(Et + Ed )M + Em M d4 ; d > do .

where Et is energy spent by transmitting one bit information,
Ed is data aggregation energy, do is relative distance, Ef and
Em is energy spent for free space path loss and multipath
loss respectively. Now the CH takes a decision on which
route to select, either along the minimum spanning tree or

Table I
PARAMETERS : T HIS TABLE SHOWS THE PARAMETERS CONSIDERED IN
THIS STUDY.
PARAMETER
Initial energy Eo
Tranmission energy Et
Aggregation energy Ed
Reception energy Er
Free space energy Ef
Multipath energy Em
Path loss exponent α
Maximum number of rounds rmax
Probability of CH election p
Number of sensor nodes in the area N

VALUE
0.5J
50 × 10−9 J
5 × 10−9 J
50 × 10−9 J
10 × 10−12 J
0.0013×10−12 J
0.3
1500
0.1
100

directly to the fusion centre, based on the costs calculated.
The minimum cost path is selected. So, all the CHs perform
intelligent decision making and finally the fusion centre acquires complete information about the available spectrum in a
short period of time. Moreover network life time is extended
and comparatively very few nodes die after the same number
of rounds considered earlier. The energy expended during
transmission Et and reception Er for M bits to a distance
d between transmitter and receiver for the secondary user is
given by
Et = Ee M + Ea αd.
Er = Ee M.
where α is path loss component, Ea is energy constant
for propagation, Ee is the electronics energy. The following
assumptions are made while making the analysis,
• A secondary users’ network is stable and consists of one
FC, one primary transmitter and N number of cognitive
radio users or sensor nodes.
• The base station acts as the Fusion Centre and it has
the location information of all secondary users, possibly
determined using Global Positioning System (GPS).
• The instantaneous channel state information of the channel is available at the each secondary user.
• The channel between any two secondary users in the same
cluster is perfect since they are close to each other.
• In this research work, minimum tree cost determination
is made with the adjacency matrix of distances.
IV. S IMULATION R ESULTS
The performance of the proposed algorithm is carried with
the following parameters given in Table I. The performance
of the proposed routing protocol is compared with the
conventional protocol.
In the uniform clustering method, the number of clusters is
restricted to 10. From Fig. ??, the proposed communication
protocol proves to be energy efficient by 1.4 times (for 1500
rounds) and this increases the lifetime of the network. Moreover, in this research, when uniform clustering is restricted to
10 CHs, the network residual energy is still higher by 27% (for
1500 rounds) than non-uniform clustering (where the cluster

Energy remaining in all the nodes in Joules

60
50
40
30

LEACH
LEACH using Prims algorithm (non uniform clustering)
LEACH using Prims algorithm (uniform clustering)

20
10

R EFERENCES
0

200

400

600

800 1,000 1,200 1,400

Number of rounds (r)

Figure 1. This graph shows the total energy remaining in Joules, in all the
alive nodes N , at the end of each round r. Uniform and non-uniform clustering
strategies have also been compared for the proposed protocol.

Number of alive sensor nodes (N )

scales down a large size of network and proves to be an elegant
design paradigm of reducing energy consumption. Moreover,
all the elected CHs, along with the FC have complete information about the spectrum availability and as a result, network
convergence has taken place. When M number of bits are
sent only by those CHs who are nearer to FC, the problem of
channel congestion is also eliminated. As an extension work,
the sensing tables can be used to store control information
as well as to information storage and intelligence for sending
the sensed information. This may be useful in case of mission
critical situations like natural disasters, where network failure
is a common phenomenon.

100
80
60
40

LEACH
LEACH using Prims algorithm (non uniform clustering)
LEACH using Prims algorithm (uniform clustering)

20
0

200

400

600

800 1,000 1,200 1,400

Number of rounds (r)
Figure 2. This graph shows the number of alive sensor nodes N , at the end of
each round r. Uniform and non-uniform clustering strategies have also been
compared for the proposed protocol.

size is not uniform for every cluster). Thus, it is observed that
the overall network residual energy using proposed protocol
with uniform size clustering is much greater than the existing
system and non-uniform clustering as well. From Fig. 2, it is
observed that the number of active nodes after 1500 rounds of
clustering and spectrum sensing, in the proposed protocol with
uniform clustering is more than the number of active nodes in
the existing protocol by 7 times. So, with this it can be shown
that the Proposed method is an energy efficient strategy with
respect to the existing system of communication for CR-WSN.
V. C ONCLUSION
The analysis and simulation results show that the proposed
protocol with uniform or non-uniform clustering shows better
performance in terms of energy consumption. Moreover, this

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