Hierarchical clustering algo for wsn

Hierarchical Clustering Algorithm
for Wireless Sensor Network
Guided by
Mrs. Himangi Pande

Presented by
Samruddhi P. Wagh
12418
1

AGENDA
•
•
•
•
•
•
•
•
•
•
•
•

INTRODUCTION
ARCHITECTURE OF WSN
PROTOCOL STACK FOR WSN
ROUTING PROTOCOLS FOR WSN
APPLICATION AND QOS OF WSN
TYPES OF CLUSTERING
CLUSTERED BASED HIERARCHICAL MODEL
EVOLUTION OF HIERARCHICAL CLUSTERING
HIERARCHICAL CLUSTERING ALGORITHM
SIMULATION METHOD
CONCLUSION
BIBLIOGRAPHY

2

INTRODUCTION
• Sensor networks are highly distributed
networks of
small, lightweight wireless nodes, deployed in large numbers
to monitor the environment or system by the measurement of
physical parameters such as temperature, pressure, or relative
humidity.
• Sensors are made up of micro-electro mechanical systems
(MEMS) technology.
• Each node of sensor network consists of 3 subsystems:
 Sensor Subsystem
 Processing Subsystem
 Communication Subsystem

3

Architecture of wsn
Layered Architecture

Clustered Architecture

4

Routing Protocols for WSN
•

Routing Protocols for WSNs generally fall into 3 groups:






Data-Centric(also known as Data Aggregation)
Hierarchical
Location-Based
QOS Oriented

6

Application and QOS of WSN
Application

QOS
•
•
•
•
•
•

Delay, Jitter and Loss
Reliability and Scalability
Responsiveness
Power Efficiency
Mobility
Bandwidth

7

Types of Clustering
Intra-Cluster(within cluster)
• In a cluster, one node act as a
cluster head(CH) and rest of
the node act as a cluster
member(CM).
• CH is selected using Election
algorithm i.e. based on energy
consumption.
• If energy level of
CH<Threshold, then the new
CH selection.
8

Inter-Cluster(between cluster head and every
cluster head and the sink)Types of Clustering
Contd..

9

Evolution of Hierarchical Clustering
• Low-Energy Adaptive
Clustering Hierarchy
(LEACH)
• Energy Efficient
Hierarchical Clustering
(EEHC)
• High Energy Efficient
Distributed (HEED)

Low-Energy Adaptive
Clustering Hierarchy (LEACH)

10

Hierarchical Clustering Algorithm
• “ How to dynamically organize the sensor nodes into
WSN and route sensed information from field sensor to
remote base station?”
• Hierarchical Clustering Algorithm is divided into two
parts:
• Multilevel hierarchical approach in Dynamic Clustering
Election Algorithm(for efficient Cluster Head (CH) selection)
• Dynamic Energy Efficient Hierarchical Routing Algorithm(for
energy efficient routing)
11

Introduction of Dynamic and
multilevel Hierarchical clustering
The cluster formation is restructured based on the set
of nodes without losing its transmission power.

In Dynamic cluster algorithm, ID of node is set
according to its distance to the data sink.

12

Multilevel hierarchy approach

13

Dynamic Clustering Election
Algorithm
• Step 1:
Let the value of Degree of Isolation σ, such as σ = 0.001 Set j = 1;
• Step 2:
While (E ( current CH) < certain threshold)
{
The current CH broadcasts a message to poll the residual energy
level of all its children;
When a sensor receives this message it will report the current
residual energy to its CH;
• Step 3:
The current CH selects the child with the maximum residual energy
as the new CH ; the new Ch changes the radio range to 2R and
broadcasts probing the new delivery node message to all neighbors.
14

Dynamic Clustering Election Algorithm
contd….
• Step 4 : If (sink node == original cluster && hopCount < hopCount of
original cluster)
{
Report its current battery residual energy and its path cost to original
sink ;
After the new CH receives the reply information;
}
• Step 5 : If (path_costold + costij * EREy < minPathCost)
{
minPathCost = path_costold + costij * EREy
}
15

Dynamic Clustering Election Algorithm
contd…
• Step 6 : Change the primary path to corresponding information
• If ( path_costnew > η* path_costold )
{
Initiate path-switching by sending a new probing message to probe
the path to another node;
}
• Step 7 : The new CH broadcast has selected the new delivery node in
its primary path, it will broadcast the new information to all children
in the old cluster.
• Step 8 : All of children in old cluster change its previous hop to the
NEW CH in its primary path.
}
16

Energy Efficiency
• Er the energy consumed in receiving the signal.
• The total amount of energy needed to be consumed in order to
send a packet over the one-hop distance is:
Ei = Eij + Er
where Ei is the energy of node i after sending data to node j;
• Eij is the path loss, which is simply the difference between the
transmission power used by i and the signal power received by j.

17

Dynamic Energy Efficient Hierarchical
Routing Algorithm
• Step 1: Split the number of regions based on the distance d.
• Step 2: Compute the node distance d & energy level Eelec (in Joules).
• Step 3: Select the CH based on the distance between the BS & the
other CH.
• Step 4: Set the cluster ID for all the clusters.
• Step 5: The entire cluster ID is maintained in the Base Station.
• Step 6: During topology discovery phase, a source node sends out a
route request packet, which is flooded to the BS. Each node along a
path also embeds its transmitting power and the cost of the path from
the source into packet sent to its next hop.

18

Routing Algorithm contd..
• Step 7: Upon receiving the multiple copies of the route reply message,
the source finds out a few routes to reach the BS based on distance.
• Step 8: If none of the candidates meet the battery requirement, then
the BS is informed to lower the value of Bref (t)(reference value w.r.t
time) and the procedure repeats.
• Step 9: Once the route is established, the source start to send the data
to BS.
1. By using the reference, the selected routes are more
evenly distributed over the entire network so that the network lifetime
can be prolonged.
2. The Bs does not choose the final route because it does not know the
battery status of the node.
19

3. The value of Bref (t) can be chosen by the BS in terms of the
estimation of the average power consumption per node at the current
time, which can be computed based on the observed total energy
consumption of the network.
• The Routing Table which is maintained in CH is shown in Table.
Node ID

Cluster ID

Distance between
CH

Energy level of
each node

25

2.1

200 m

198 J

33

2.2.1

100 m

256 J

38

3.1

170 m

300 J

45

3.2.3

120 m

357 J

50

3.3.3

125 m

370 J

70

4.1

150 m

400 J

75

4.2.1

175 m

420 J

20


21

Simulation method

Total energy spent vs. number of levels in the clustering hierarchy22

Simulation method contd…

Total Energy Spent vs. number of levels in the clustering hierarchy23

Conclusion
• Hierarchical clustering and routing algorithms will work efficiently
and reduces the energy consumption of sensor nodes.
• As the CH selection is important problem in sensor network, for this
cluster-based routing has been shown to be more energy efficient and
increase the network lifetime through data aggregation.
• The goal of selecting the CH is to minimize the transmission cost and
energy usage.

24

Bibliography
• Kazem Sohraby, Daniel Minoli, Taieb Znati,”WIRELESS SENSOR NETWORKS
Technology, Protocols and Applications,” John Wiley, New York, 2007.
• S.V. Manisekaran, Dr.R.Venkatesan, “Energy Efficient Hierarchical Clustering for
Sensor Networks,” 2010 IEEE Second International Conference on
Computing, Communication and Network Technologies.
• S. Bandyopadhyay and E.J.Coyle, “An Energy Efficient Hierarchical clustering
Algorithm for Wireless Sensor Networks,” 2003 IEEE Electrical and Computer
Engineering, Purdue University, West Lafayette, IN, USA.
• C. Siva Ram Murthy, B. S. Manoj, “AD HOC WIRELESS NETWORKS
Architecture and Protocols,” PEARSON.
• Heinzelman W, Chandrakasan A, Balakrishnan H, “An Application-Specific
Protocol Architecture for Wireless Microsensor Networks IEEE Transactions on
wireless communication, P. 660-670, 2002.
• F.L.LEWIS, D.J.Cook and S.K.Das, “Wireless Sensor Networks Smart
Environments: Technologies, protocols, and Applications John Wiley, New
25
York, 2004.

Hierarchical clustering algo for wsn

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Hierarchical clustering algo for wsn (20)

Recently uploaded (20)

Hierarchical clustering algo for wsn