Efficient Clustering scheme in Cognitive Radio Wireless sensor network

Efficient Clustering Scheme in Cognitive Radio Wireless
Sensor Network
Under the Guidance of
Mr. Nitin Gupta
Assistant Professor,
CSE Department
Presented By:
Mohammad Aziz
14MI544
Dual Degree in CSE
MTech Dissertation on
Department of Computer Science & Engineering
National Institute of Technology Hamirpur
June, 2019

Contents
●
Introduction
●
Motivation
●
Problem Statement
●
Literature Review
●
Objectives
●
Proposed approach and Algorithm
●
References

Introduction
●
Cognitive Radio Network
Cognitive radio network is intelligent and dynamically adapts the
transmission parameter through the environment accordingly.
●
Cognitive radio wireless sensor network
CR-wireless sensor networks (CR-WSNs) are a specialized ad hoc
network of distributed wireless sensors that are equipped with cognitive
radio capabilities.
●
Clustering Scheme
K-mean clustering -It is an algorithm of unsupervised machine learning
which is utilized to generate a normal cluster in unlabelled information.

Continue...
●
AP clustering - It based on the concept of
"message passing" between data points.
●
Sleep and wake up - Sleep-wake strategy is the
process of a node to get into sleep and wake mode.

Motivation
●
Cognitive radios offer the guarantee of being a innovation
technology that will empower the futurejwireless world.
●
Cognitive radios are completely programmable remote gadgets that
can detect theirkenvironment and dynamicallysadjust their
transmission waveform, spectrum use,channeliaccess method, and
networking protocol as required for a good system and application
execution.
●
The main motivation is to design efficient and less power
consumption clustering scheme in a cognitive radio networks.

Problem Statement
Inefficient clustering in CRN may lead to high
power consumption and reduction of the lifetime of
nodes in the cognitive radio network. The objective
of the proposed work is to design a clustering
algorithm such that a lesser number of the cluster
are created while the number of nodes are
increased.

Literature review
[6] A novel clustering-based spectrum sensing (CBSS) in CR-WSNs was
proposed by Qu et al. [6]. In this method, sensor nodes are grouped into
different sets based on their similarity in sensing results. An objective
function is proposed to identify the optimal cluster number. Details for
cluster formation and CH selection were not given, and the performance
was not compared with existent algorithms.
[8] A spectrum-aware cluster-based energy-efficient multimedia
(SCEEM) routing protocol was proposed by Shah et al. In this protocol,
SU nodes form a cluster with a higher number of commonly available
idle channels. The CHs are selected based on energy and relative
spectrum awareness, such that noncontiguous available spectrum bands
are clustered and scheduled to provide continuous transmission
opportunities.

Continue...
●
Rauniyar and Shin [7] proposed a clustering scheme for
cooperative spectrum sensing based on other
approaches. In the scheme, a pair of nodes in a group
can alternate between sleep and wake modes during the
sensing process.
●
Manoor and Shahid [9] proposed a clustering scheme
called BECHR for CR-WSNs. This scheme selects a
CH based on residual energy in an iterative manner. It
works like conventional WSN clustering methods but
with some variations.

Continue...
●
Ozger et al. [10] and Ozger and Akan [5] proposed event-
driven spectrum-aware clustering (ESAC) and mobility-aware
Event-to-sink Spectrum-Aware Clustering (mESAC) protocols
that form temporal clusters for each event in CR-WSNs. These
protocols determine eligible nodes for clustering based on the
local position of nodes between the event and the sink. CHs
are selected from among the candidate nodes based on node
degree, channel availability, and distance to the sink. Because
this is event-driven clustering, the clusters are immediately
dismissed after finishing data transmission. Hence, it is not
suitable for other scenarios.
●
●

Objectives
●
Devising an approach for making formation of
cluster efficient in such a way that lesser number of
clusters are created while number of nodes are
increased.
●
To analyze and understand the current approaches
designed for the Clustering in CRWSNs

Proposed approach and Algorithm
●
Proposed flow diagram

Explanation
●
First start with checking whether a certain node is in
the active(wake-up) mode or in the sleep mode in the
network.
●
The capacity of the battery node should be greater
than or equal to threshold value.
●
Cluster head or centroid are chosen by using affinity
propagation method.
●
finding out the node distance from the centroid using
Euclidean distance formula.

Continue...
●
Checks if two nodes having its distances are equal.
●
Higher degree node is selected otherwise minimum
distance is selected for the cluster.

Simulation
●
The proposed approach of the Efficient Clustering in
cognitive radio wireless sensor network is simulated using
the MATLAB version R2018a.
Comparison of the number of Clusters against
number of nodes
●
The simulation are shown in the below table.
no.of
nodes
Cogmesh
Cluste
based
approach
Proposed
approach
SOC
approach
50 10 12 12 12
100 11 17 16 22
150 16 18 15 32
200 19 20 15 37
250 21 23 17 39
300 26 24 19 42

Continue...
●
Increase in the number of node also lead to
increase in the number of clusters for all the three
approaches along with the proposed model.
●
It is seen that less number of cluster are
constructed by proposed approach while
comparing with other three approaches.
●
Thus, this proposed model approach is efficient
clustering method for cognitive radio network.

Future Scope
The scope of future work can be considered in
energy utilization in mobile cognitive radio
network using a cluster-based algorithm to deduce
the jamming in the network and for the lifetime of
the cognitiv network.

References
[1] Ozgur B Akan, Osman B Karli, and Ozgur Ergul. Cognitive
radio sensor networks. IEEE network, 23(4):34–40, 2009.
[2] Jong-Hong Park, Yeonghun Nam, and Jong-Moon Chung.
Sustainability enhancement multihop clustering in cognitive
radio sensor networks. International Journal of Distributed
Sensor Networks, 11(10):574340, 2015.
[3] Huazi Zhang, Zhaoyang Zhang, Huaiyu Dai, Rui Yin, and
Xiaoming Chen. Distributed spectrum-aware clustering in
cognitive radio sensor networks. In 2011 IEEE Global
Telecommunications Conference-GLOBECOM 2011, pages 1–
6. IEEE, 2011.

Continue...
[4] Ibrahim Mustapha, Borhanuddin M Ali, A Sali, Mohd FA Rasid,
and H Mohamad. Energy-aware cluster based cooperative spectrum
sensing for cognitive radio sensor networks. In 2014 IEEE 2nd
international symposium on telecommunication technologies (ISTT),
pages 45–50. IEEE, 2014.
[5] Chunhung Richard Lin and Mario Gerla. A distributed
architecture for multimedia in dynamic wireless networks. In
Proceedings of GLOBECOM’95, volume 2, pages 1468– 1472.
IEEE, 1995.
●
[6] Y Tawk, J Costantine, and CG Christodoulou. Cognitive-radio and
antenna functionali ties: A tutorial [wireless corner]. IEEE Antennas
and Propagation Magazine, 56(1):231243, 2014.

Continue...
●
[7] Rauniyar A., Shin S.Y. A novel energy-efficient clustering
based cooperative spectrum sensing for cognitive radio sensor
networks. Int. J. Distrib. Sens. Netw. 2015;2015 doi:
10.1155/2015/198456.
●
[8] Shah G.A., Alagoz F., Fadel E.A., Akan O.B. A spectrum-
aware clustering for efficient multimedia routing in cognitive
radio sensor networks. IEEE Trans. Veh. Technol. 2014;63:3369–
3380. doi: 10.1109/TVT.2014.2300141.
●
[9] Mansoor U., Shahid M.K. Cluster based Energy Efficient
Sensing for Cognitive Radio Sensor Networks. Int. J. Comput.
Appl. 2014;88:14–19. doi: 10.5120/15363-3849.
●

Continue...
●
[10] Ozger M., Fadel E., Akan O. Event-to-sink
Spectrum-Aware Clustering in Mobile Cognitive
Radio Sensor Networks. IEEE Trans. Mob.
Comput. 2015;15:2221–2233. doi:
10.1109/TMC.2015.2493526.

Efficient Clustering scheme in Cognitive Radio Wireless sensor network

Efficient Clustering scheme in Cognitive Radio Wireless sensor network

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Efficient Clustering scheme in Cognitive Radio Wireless sensor network