1. A novel Energy-Efficient and Distance- based Clustering approach for Wireless Sensor Networks M. Mehdi Afsar, 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 2Provided by: M. Mehdi AfsarWSC'17

3. Wireless Sensor Networks (WSNs) Provided by: M. Mehdi Afsar3 WSN Communication Architecture WSC'17

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 4Provided by: M. Mehdi AfsarWSC'17

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 5Provided by: M. Mehdi AfsarWSC'17

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 Afsar6WSC'17

7. Cluster-head Election Algorithm at node i Local Competition ―Compute and broadcast P CCH (i) Probability in range of competition R comp (P CCH (i)=E residual /E initial ) ―Wait for t wait seconds to receive this probability from all the neighbors ―Node i is a candidate cluster-head if P CCH (i) is greater than all the received P CCH 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 (D thr ) node i is a candidate and its distance to other candidates is smaller than D thr,but has higher node degree and node ID —Otherwise node i remains an ordinary node Provided by: M. Mehdi Afsar7WSC'17

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 Afsar8WSC'17

9. Performance evaluation First Set & First Scenario Provided by: M. Mehdi Afsar9 Average dissipated energy in entire the network by all the nodes Average energy of the elected cluster-heads to the average energy of all the ordinary nodes WSC'17

10. Performance evaluation First Set & First Scenario Provided by: M. Mehdi Afsar10 Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) WSC'17

11. Performance evaluation First Set & Second Scenario Provided by: M. Mehdi Afsar11 Average dissipated energy in entire the network by all the nodes Average energy of the elected cluster-heads to the average energy of all the ordinary nodes WSC'17

12. Performance evaluation First Set & Second Scenario Provided by: M. Mehdi Afsar12 Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) WSC'17

13. Performance evaluation Second Set (Comparison of EEDC to LEACH and HEED Protocols) Provided by: M. Mehdi Afsar13 Dissipated energy in entire the network by all the nodes Average energy of the elected cluster-heads to the average energy of all the ordinary nodes WSC'17

14. Performance evaluation Second Set Provided by: M. Mehdi Afsar14 Network lifetime as time until the First Node Dies (FND) Network lifetime as time until the Half of the Nodes Alive (HNA) WSC'17

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. Mehdi Afsar15WSC'17