Report

A novel Energy-Efficient and Distancebased 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 Conclusion and Future Works WSC'17 Provided by: M. Mehdi Afsar 2 Wireless Sensor Networks (WSNs) WSN Communication Architecture WSC'17 Provided by: M. Mehdi Afsar 3 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 WSC'17 Provided by: M. Mehdi Afsar 4 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 WSC'17 Provided by: M. Mehdi Afsar 5 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 WSC'17 Provided by: M. Mehdi Afsar 6 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: • • 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 Provided by: M. Mehdi Afsar 7 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 Provided by: M. Mehdi Afsar 8 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 9 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 10 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 13 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 15