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Distributed Clustering in Ad-hoc Sensor
Networks: A Hybrid, Energy-Efficient
Approach
Ossama Younis and Sonia Fahmy
Department of Computer Sciences,
Purdue University
Infocom'04
Ossama Younis, Purdue University
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Contributions
A new distributed clustering protocol for
sensor networks that has the following
properties:
Energy-efficient
Terminates rapidly
Considers cluster quality, e.g., load-balanced
clusters or dense clusters
Has low message/processing overhead
Infocom'04
Ossama Younis, Purdue University
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Sensor Networks
 Application-specific, e.g.,
Monitoring seismic activities
Surveying military fields
Reporting radiation levels at
nuclear plants
 Nodes are usually:
Densely deployed
Limited in processing, memory,
and communication capabilities
Constrained in battery lifetime
Left unattended
Infocom'04
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Goals
 Scalability, data and state aggregation,
robustness, and prolonged network lifetime
What is network lifetime?
Time until the first
Time until the last
node dies
node dies
 How to prolong the network lifetime?
 Deploy redundant nodes
 Apply energy-efficient protocols, e.g.,
 MAC layer protocols can reduce energy waste
 Topology management can distribute energy
consumption
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Topology management
Cell-based approach
Cluster-based approach
observer
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Outline
 System model and requirements
 The Hybrid, Energy-Efficient, Distributed
clustering protocol (HEED)
 HEED properties
 Evaluation
 Related Work
 Conclusion
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System Model
 A set of n sensor nodes are dispersed uniformly
and independently in a field
 Sensor nodes are
Quasi-stationary
Unattended
Equally significant
Location un-aware
Homogeneous (similar capabilities)
Serving multiple observers
Possess a fixed number of transmission power levels
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Requirements
 Our goal is to design a new clustering approach
that has the following properties:
Completely distributed
Terminates in O(1) iterations
Has low message/processing overhead
Generates high energy, well-distributed cluster
heads
Can provide other characteristics, such as
balanced or dense clusters
Infocom'04
Ossama Younis, Purdue University
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Approach (HEED)
 We propose the Hybrid, Energy-Efficient,
Distributed clustering approach (HEED)
 Heed is hybrid:
Clustering is based on two parameters
 HEED is distributed:
Every node only uses information from its 1-hop
neighbors (within cluster range)
 HEED is energy-efficient:
Elects cluster heads that are rich in residual energy
Re-clustering results in distributing energy
consumption
Infocom'04
Ossama Younis, Purdue University
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HEED - Parameters
 Parameters for electing cluster heads
Primary parameter: residual energy (Er)
Secondary parameter: communication cost (used
to break ties)
i.e., maximize energy and minimize cost
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HEED – Algorithm at node v
 Initialization  Discover neighbors within cluster range
 Compute the initial cluster head
probability CHprob = f(Er/Emax)
 Main
 If v received some cluster head messages,
processing
choose one head with min cost
 If v does not have a cluster head, elect to
become a cluster head with CHprob .
 CHprob = min(CHprob * 2, 1)
 Repeat until CHprob reaches 1
 Finalization  If cluster head is found, join its cluster
 Otherwise, elect to be cluster head
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Ossama Younis, Purdue University
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HEED - Example
(0.4,3)
a13
a10
(0.1,4)
a11
(0.2,2)
(0.2,5)
a12
(0.2,3)
a7
(0.6,2)
c2
c3
(0.8,4)
(0.1,2)
a6
(0.2,3)
(0.1,4)
a5
a14
c1
c4
(0.7,5)
(0.3,2)
a3
(0.2,3)
Compute CHprob
and cost
Elect to become
cluster head
a2
a4
(0.6,4)
a9
(0.9,4)
(0.5,4)
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a8
(0.5,3)
Discover
neighbors
(0.3,2)
a1
Ossama Younis, Purdue University
Resolve ties
Select your
cluster head
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HEED - Analysis
HEED has the following properties:
Completely distributed
Clustering terminates in O(1) iterations:

1 
N iter  log 2
 1
p min 

Message overhead: O(1) per node
Processing overhead: O(n) per node
Cluster heads are well distributed. Pr{two CHs
are within the same cluster range}:


1 
(p = initial CHprob )
  log( )  1 


p

pnbr 

 1  p2 

i
2
i 0
Infocom'04
Ossama Younis, Purdue University
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HEED – Inter-cluster communication
 Lemma 1 (Blough and Santi’02):
Assume n nodes are dispersed uniformly and
independently in an area R=[0,L]2. If Rc2n=aL2lnL, for
some a>0, Rc << L, and n>>1, then limn,N→∞E(number
R
R
of empty cells) = 0, where a cell is an

2
2
area
c
c
 Lemma 2:
There exists at least one cluster
head a.a.s. in any area of
1
1
) Rc  ( 2 
) Rc
size (2 
2
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HEED – Inter-cluster communication
 Theorem 1:
2.7Rc
Two cluster heads in two
neighboring areas can
communicate if
CH2
2.7Rc
Rt  6 Rc
Rt
CH1
 Theorem 2:
HEED produces a connected
multi-hop cluster head graph
(structure) asymptotically
almost surely
Infocom'04
Ossama Younis, Purdue University
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Performance evaluation
 2000x2000 network field
with 1000 nodes
 Demonstrating HEED
properties: fast termination,
rich-energy cluster heads,
and cluster quality
Infocom'04
Ossama Younis, Purdue University
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Performance evaluation (cont’d)
 Apply HEED to an
application where
nodes directly contact a
far observer
 Compare to multi-hop
LEACH clustering
100x100 network
Initial Er = 2 Joule
1 round = 5 TDMA
frames
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Ossama Younis, Purdue University
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Related Work
 Topology management protocols suffered from at
least one of the following problems:
Dependence on location awareness (e.g., GAF)
Slow convergence (i.e., dependent on the network
diameter) (e.g., DCA)
Energy efficiency was not the main goal of many
protocols, e.g., Max-Min D-clustering
No focus on clustering quality, such as having
cluster heads well-distributed in the network (e.g.
LEACH)
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Ossama Younis, Purdue University
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Conclusion
 We have proposed HEED clustering
 HEED is fast and has low overhead
 HEED can provide other features, such as loadbalancing
 HEED is independent of:
Homogeneity of node dispersion in the field
Network density or diameter
Distribution of energy consumption among nodes
Proximity of querying observers
 HEED can be extended to provide multi-level
hierarchy
Infocom'04
Ossama Younis, Purdue University
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