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A novel Energy-Efficient and Distance-based Clustering approach for Wireless Sensor Networks

A novel Energy-Efficient and Distance-based Clustering approach for Wireless Sensor Networks. M. Mehdi Afsar , Mohammad-H. Tayarani -N. Outline. Wireless Sensor Networks Network Model Clustering Objectives Proposed EEDC Approach Cluster-head Election Algorithm Performance Evaluation

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A novel Energy-Efficient and Distance-based Clustering approach for Wireless Sensor Networks

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  1. A novel Energy-Efficient and Distance-based Clustering approach for Wireless Sensor Networks M. MehdiAfsar, Mohammad-H. Tayarani-N.

  2. Outline • Wireless Sensor Networks • Network Model • Clustering Objectives • Proposed EEDC Approach • Cluster-head Election Algorithm • Performance Evaluation • Conclusion and Future Works Provided by: M. Mehdi Afsar

  3. Wireless Sensor Networks (WSNs) WSN Communication Architecture Provided by: M. Mehdi Afsar

  4. 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 Provided by: M. Mehdi Afsar

  5. 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 Provided by: M. Mehdi Afsar

  6. 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 Provided by: M. Mehdi Afsar

  7. 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 iis 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: • 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 Provided by: M. Mehdi Afsar

  8. 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 Provided by: M. Mehdi Afsar

  9. Performance evaluation • First Set & First Scenario Average energy of the elected cluster-heads to the average energy of all the ordinary nodes Average dissipated energy in entire the network by all the nodes Provided by: M. Mehdi Afsar

  10. Performance evaluation • First Set & First Scenario Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) Provided by: M. Mehdi Afsar

  11. Performance evaluation • First Set & Second Scenario Average energy of the elected cluster-heads to the average energy of all the ordinary nodes Average dissipated energy in entire the network by all the nodes Provided by: M. Mehdi Afsar

  12. Performance evaluation • First Set & Second Scenario Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) Provided by: M. Mehdi Afsar

  13. Performance evaluation • Second Set (Comparison of EEDC to LEACH and HEED Protocols) Average energy of the elected cluster-heads to the average energy of all the ordinary nodes Dissipated energy in entire the network by all the nodes Provided by: M. Mehdi Afsar

  14. Performance evaluation • Second Set Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) Provided by: M. Mehdi Afsar

  15. 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 Provided by: M. MehdiAfsar

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