A novel Energy-Efficient and Distancebased Clustering approach for Wireless
Sensor Networks
M. Mehdi Afsar, Mohammad-H. Tayarani-N.
Outline
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Wireless Sensor Networks
Network Model
Clustering Objectives
Proposed EEDC Approach
Cluster-head Election Algorithm
Performance Evaluation
Conclusion and Future Works
WSC'17
Provided by: M. Mehdi Afsar
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Wireless Sensor Networks (WSNs)
WSN Communication Architecture
WSC'17
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Network Model
• N sensor nodes are dispersed uniformly and
independently in a field of size M X M
• The Base station (BS) is stationary and located at the
center of the field
• Transmission channel is secure
• Operational time is divided into a number of rounds
• Sensor nodes are:
– Stationary
– Homogeneous
– Location un-aware
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Clustering Objectives
• The clustering should be:
– Completely distributed
– Efficient in complexity of message and time
– Guarantees load-balancing
• The cluster-heads should be well-distributed
across the network
• The clustered WSN should be fully-connected
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Proposed EEDC Approach
• Cluster-head Election Phase
– Local Competition
• Select the nodes with the highest residual energy as candidate
– Distance Condition
• Select the candidates with proper distance to each other as cluster-head
• Cluster Formation Phase
– Join the nearest cluster-head
• Route Update Phase
– Find the next-hop based on lowest cost (lowest delay)
• Data Transmission Phase
– Send data to the BS by multi-hop path among the cluster-heads
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Cluster-head Election Algorithm at node i
• Local Competition
― Compute and broadcast PCCH(i) Probability in range of
competition Rcomp (PCCH(i)=Eresidual/Einitial)
― Wait for twait seconds to receive this probability from all the
neighbors
― Node i is a candidate cluster-head if PCCH (i) is greater than all the
received PCCH probability
• Distance Condition
— Node i can be a cluster-head If:
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it is a candidate and its distance to other candidates is greater than a
Threshold Distance (Dthr)
node i is a candidate and its distance to other candidates is smaller
than Dthr,but has higher node degree and node ID
— Otherwise node i remains an ordinary node
WSC'17
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Performance Evaluation
• Two sets of simulations are performed here:
– Parameter study on EEDC
– comparing EEDC to other approaches
• Two scenarios of simulations:
– 400 nodes in a field of size 200m X 200m
– 800 nodes in a field of size 400m X 400m
WSC'17
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Performance evaluation
• First Set & First Scenario
Average dissipated energy in entire
the network by all the nodes
WSC'17
Average energy of the elected
cluster-heads to the average
energy of all the ordinary nodes
Provided by: M. Mehdi Afsar
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Performance evaluation
• First Set & First Scenario
Network lifetime as time until
the First Node Dies (FND)
WSC'17
Network lifetime as time until the
Half of the Nodes Alive (HNA)
Provided by: M. Mehdi Afsar
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Performance evaluation
• First Set & Second Scenario
Average dissipated energy in entire
the network by all the nodes
WSC'17
Average energy of the elected
cluster-heads to the average
energy of all the ordinary nodes
Provided by: M. Mehdi Afsar
11
Performance evaluation
• First Set & Second Scenario
Network lifetime as time until
the First Node Dies (FND)
WSC'17
Network lifetime as time until the
Half of the Nodes Alive (HNA)
Provided by: M. Mehdi Afsar
12
Performance evaluation
• Second Set (Comparison of EEDC to LEACH
and HEED Protocols)
Average energy of the elected
Dissipated energy in entire the
network by all the nodes
WSC'17
cluster-heads to the average
energy of all the ordinary nodes
Provided by: M. Mehdi Afsar
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Performance evaluation
• Second Set
Network lifetime as time until
the First Node Dies (FND)
WSC'17
Network lifetime as time until the
Half of the Nodes Alive (HNA)
Provided by: M. Mehdi Afsar
14
Conclusion and Future Work
• We have proposed EEDC clustering approach
• EEDC provides:
– Energy-Efficiency
– Distributed clustering
– Load-balancing
– Fast termination
• EEDC can be extended to meet other QoS
requirements
WSC'17
Provided by: M. Mehdi Afsar
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