This document summarizes an energy efficient clustering algorithm proposed for wireless sensor networks. It discusses the objectives, existing system, proposed system, simulation results and conclusions. The existing system uses a distributed self-organization balanced clustering algorithm (DSBCA) that has uniform cluster sizes and issues with node dropout. The proposed energy efficient clustering algorithm (EECA) forms unequal cluster sizes based on average neighbor energy and selects cluster heads through uneven competition ranges. Simulation results show the heterogeneous EECA provides longer network lifetime, higher efficiency and throughput than the homogeneous EECA.

1. ENERGY EFFICIENT CLUSTERING
ALGORITHM FOR WSN
TEAM MEMBERS
Manonmani.A
Minu@ Maharayazhini.S
Priyadharshini.M
SandrineNadiad.B
UNDER THE GUIDANCE
OF
Mrs.C.P.Subha
Associate Professor & PG
Coordinate

2. CONTENTS
• OBJECTIVE
• EXISTING SYSTEM
• PROPOSED SYSTEM
• SIMULATION RESULTS
• CONCLUSION AND FUTURE SCOPE
• REFERENCE

3. OBJECTIVE
• To propose an energy efficient clustering algorithm for
extending the network lifetime in WSNs

4. INTRODUCTION
• Wireless sensor network (WSN) consists of spatially
distributed autonomous sensors.
• WSN is built of "nodes“ from a few to several
hundreds or even thousands.
• Monitor physical or environmental conditions, such
as temperature, sound, pressure.

5. CLUSTERING
• Clustering of nodes is an energy efficient approach
for wireless sensor networks.
• Nodes are grouped to form clusters.
• Each cluster has atleast one cluster head (CH).
• Nodes send data to their corresponding CH by single
or multi hop communication.

6. ADVANTAGES OF CLUSTERING
• Transmit aggregated data to the data sink
• Reducing number of nodes taking part in
transmission
• Useful energy consumption
• Scalability for large number of nodes
• Reduces communication overhead for both single
and multi hop

7. LITERATURE SURVEY OF CLUSTERING
ALGORITHMS
• HEED: A hybrid energy efficient distributed clustering
approach for ad-hoc sensor networks
• MRECA: Mobility resistant efficient clustering
approach for ad-hoc sensor networks
• Energy efficient dynamic clustering algorithm for ad-
hoc sensor networks
• LEACH-Energy efficient communication protocol for
WSN
• EEDC-Dynamic clustering and energy efficient routing
technique for WSN

8. EXISTING SYSTEM
• Distributed self-organization balanced clustering
algorithm(DSBCA).
• Purpose is to generate clusters with more balanced
energy and avoid creating excessive clusters with
many nodes.
• The basic idea of DSBCA is based on connectivity
density and clustering radius.
• Clustering radius is determined by distance.

9. TOPOLOGY OF EXISTING SYSTEM

10. 3 Stages of DSBCA
• Cluster head phase
• Cluster building phase
• Cycle phase

11. Cont…
Cluster head selecting phase:
Cluster head selection depends on
• Distance from the base station
• Node connection density

12. Cont…….
Cluster building phase:
• DSBCA sets the threshold of cluster size.
• Number of cluster node cannot exceed the threshold.
• Comparing the size of cluster with threshold to accept
new member and update the count of cluster nodes.

13. Cont…….
Cycle phase:
• Cluster is stable until the process of reelecting cluster
head.
• Cluster Head gathers the weight of all member nodes
and selects node with highest weight as next head
node.
• The average overall communication overhead in per
cluster is calculated.

14. DRAWBACKS
• Uniform cluster size
• Nodes dropout
• Packets dropout

15. PROPOSED WORK
• Energy efficient clustering algorithm elects the cluster
head based on average Residual energy of neighbor
nodes.
• Uses uneven competition ranges to form cluster of
unequal sizes.
• Cluster head near to BS/Sink have small cluster sizes
to preserve energy.

16. NETWORK MODEL
• Sensor nodes are randomly distributed in a circular
area
• Sink is located at the centre
SINK

17. RADIO MODEL
Fig. Radio Energy Dissipation Model
Power consumption during transmission is given by,
ET x(k, d) = ET x − elec(k) + ET x − amp(k, d)
= {k Eelec + kεfsd2, d < d0
{k Eelec + kεmpd4, d ≥ d0
Power consumption during reception is given by,
ERx(k) = Erx − elec(k) = kEelec
Transmit
Electronics
Tx
Amplifier
Receive
Electronics
K bit packetK bit packet
Erec(k)
Eelec *(k)εamp*k*dnEelec *(k)
ET x (k,d)
d

18. MECHANISM
SETUP PHASE
• Network Deployment
Phase
• Neighbor Node Phase
• Cluster Head Competition
Phase
• Cluster Formation Phase
DATA TRANSMISSION
PHASE
• Intra – Cluster
Communication
• Inter – Cluster
Communication

19. CLUSTER SETUP PHASE

20. NETWORK DEPLOYMENT PHASE
• Nodes are randomly deployed
• Sink broadcasts signal
• Each node compute its distance from BS
• Helps nodes to select the proper power level to
communicate with the BS

21. NEIGHBOR NODE PHASE
• Each node broadcasts Hello_Msg.
• Hello_Msg contains node id and Residual energy Er.
• At same time it recieves Hello_msg from its
neighbors.
• Each node calculates its average residual energy Ea.
d
Ea = (1/d)Σ si . Er
i=1

22. Cont….
Where Si - One of the neighbor node
Si . Er - Residual energy of Si
d - Number of neighbor node
• Each node calculates waiting time T1 for CH
competition phase.
T1 = [(1-α) *Ni/Nmax ]+[α*Vrandom]
Where Ni - number of neighborhood nodes of node i
Nmax -number of total sensor nodes
Vrandom -random number 0.9 and 1
α -constant coefficient between 0 and 1

23. CLUSTER COMPETITION PHASE
• After time T1 expires, nodes starts competition phase.
• All nodes calculate time T2 in this phase using
t = { (Ea/Er) T2 vr , Er≥Ea
{T2 Vr , Er<Ea
• Each node calculates its competition radius Rc .
dmax− d(si,BS)
Rc =1 – c ------------------------- Ro C
dmax− dmin

24. Cont….
Where dmax and dmin - maximum and minimum distance
b/w nodes and BS
c - weighted factor
RoC - maximum competition radius
Cluster Head Selection
• Final cluster Head elected based on Residual Energy.
• For any node Si, if it receives no Head_Msg when time T2
expires, it broadcasts the Head_Msg.
Cluster Head
ID
Residual
Energy
Distance from node to BS
(d)
No. of
Neighbor
Nodes
1
.
.
1.0
.
.
80
.
.
6
.
.

25. CLUSTER FORMATION PHASE
• Last subphase of cluster setup phase
• Each non cluster-head node chooses the nearest
cluster head and sends the Join _Msg
• Each cluster head creates a node schedule list
according to the received Join_Msgs
• Sends the schedule list to the cluster members by
broadcasting Sync_Msg

26. Cont…
Control messages and its descriptions:
Control messages Descriptions (fields)
Hello_Msg
Head-Msg
Join_Msg
Sync_Msg
Node ID, Residual Energy E
Node ID
Node ID, Head ID
Node ID, Residual Energy E

27. DATA TRANSMISSION PHASE

28. INTRA-CLUSTER COMMUNICATION
• Cluster members sense and collect local data
• Send collected data to the cluster heads

29. INTER-CLUSTER COMMUNICATION
• Cluster heads receive and aggregate the data
• Threshold distance is used to find the distance from
cluster head Si to the BS.
• If the distance d is less than DTHR, Si communicates
with the BS directly.
• Otherwise, Si selects CH with high residual energy
from its neighborhood CH.
• To calculate the energy consumption Erelay
Erelay=d2(si, sj)+ d2(sj, BS)
Where, sj is the nearest CH with high residual energy

30. Cont..
• Algorithm : Choosing nearest CH for data Transmission
DTHR=min
Si=CH
d=distance from si to BS
if (d<DTHR) then
si directly communicate with BS
else
repeat
si selects sj from its neighbor
if(sj=CH of its neighbor)
forward the data from si to sj
Erelay=d2(si,sj)+d2(sj,BS)
endif
While (sj=CH)
Endif

31. ADVANTAGES
• Consume less energy during the intra cluster and inter
cluster communication
• No “isolate points” and cover all the network nodes
• Unequal clusters generated by using Rc will be more
effective in prolonging the network lifetime

32. SIMULATION PARAMETERS
• Simulation tool - NS2,UBUNTU
• Number of nodes - 150
• Dimension - 800*600
• Topology - Flat grid
• Protocol type - AODV, DSR
• Antenna type - Omni Antenna

33. SIMULATION RESULTS FOR
HOMOGENEOUS EECA

34. NODE INITIALIZATION

35. DATA TRANSMISSION

37. SIMULATION RESULT FOR
HETEROGENEOUS EECA

38. NODE INITIALIZATION

39. DATA TRANSMISSION

40. SIMULATED GRAPHS

41. NETWORK LIFETIME
• The lifetime of network remains 100% about 300sec for
heterogeneous and 260sec for homogeneous network

42. EFFICIENCY
• Heterogeneous EECA gives upto 2750 rounds and Homogeneous
EECA gives only 2400 rounds. It shows that the efficiency of
heterogeneous network is higher than the homogeneous network.

43. THROUGHPUT
• Heterogeneous EECA transmits upto 5200 packets of data to
sink. Homogeneous EECA transmits upto 4800 packets of data
to sink. It shows that heterogeneous EECA is more when
compared to homogeneous EECA.

44. CONCLUSION AND FUTURE SCOPE
• Our proposed work will consume less energy during
the intra cluster and inter cluster relay traffic which
can balance the energy consumption among cluster
heads and extend the lifetime of the network up to
85%.
• For further enhancement in network lifetime EECA
can be simulated using different table driven routing
protocols and on demand routing protocols.

45. REFERENCES
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