Seema Bandyopadhyay and Edward J. Coyle

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Presentation transcript:

Seema Bandyopadhyay and Edward J. Coyle An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks Seema Bandyopadhyay and Edward J. Coyle Prepared by Mohamed Elsersy March 2010

About the paper Appeared in INFOCOM 2003 11 pages 30 references Cited by 673 papers 11/11/2018 Elsersy 2010 CPSC601.08

Topic classification Algorithms Issues Network Type Deployment Sensing WSN Deployment Sensing Decision Making Routing Flat Location-based Hierarchical 11/11/2018 Elsersy 2010 CPSC601.08

Paper Structure Elsersy 2010 CPSC601.08 11/11/2018 EEHCA paper Introduction Related Work Single-level Clustering Alg. Algorithm Optimal parameters CH Probability Maximum hops Simulation Results Hierarchical clustering Alg. Numeric Results Additional Consideration Conclusion & Future Work 11/11/2018 Elsersy 2010 CPSC601.08

Related Work LCA LCA2 WCA DCA DMAC LEACH non-purely hierarchical models 11/11/2018 Elsersy 2010 CPSC601.08

Assumptions (1/2) a) The sensors are distributed as per a homogeneous spatial Poisson process of intensity λ in 2-D space. b) All sensors transmit at the same power level so have the same radio ranger. c)Data exchanged between two communicating sensors not within each others’ radio range is forwarded by other sensors. 11/11/2018 Elsersy 2010 CPSC601.08

Assumptions (2/2) d) A distance of d between any sensor and its cluster head is equivalent to d / r hops. e) Each sensor uses one unit of energy to transmit or receive one unit of data. f) A routing infrastructure is in place; so, only the sensors on the routing path forward the data. g) The communication environment is contention- and error-free. (No retransmit) 11/11/2018 Elsersy 2010 CPSC601.08

Single-level Clustering Algorithm Each sensor becomes a cluster-head (CH) with probability p and advertises itself as a CH to the sensors within its radio range. The advertisement is forwarded to all the sensors that are no more than k hops away from CH. Any sensor that receives such advertisements and is not itself a CH joins the cluster of the closest CH. Any sensor that is neither a CH nor has joined any cluster itself becomes a CH. 11/11/2018 Elsersy 2010 CPSC601.08

Optimal parameters 11/11/2018 Elsersy 2010 CPSC601.08

Results (1/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (2/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (3/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (4/4) 11/11/2018 Elsersy 2010 CPSC601.08

Hierarchical Clustering Alg. (1/2) Each sensor decides to become a level-1 CH with p1 and advertises itself as a CH to the sensors within its radio range. This advertisement is forwarded to all the sensors within k1 hops of the advertising. Each sensor that receives an advertisement joins the cluster of the closest level-1 CH; the remaining sensors become forced level-1 CHs. 11/11/2018 Elsersy 2010 CPSC601.08

Hierarchical Clustering Alg. (2/2) Level-1 CHs then elect themselves as level-2 CHs with a certain probability p2 and broadcast their decision of becoming a level-2 CH. This decision is forwarded to all the sensors within k2 hops. The level-1 CHs that receive the advertisements from level-2 CHs joins the cluster of the closest level-2 CH. All other level-1 CHs become forced level-2 CHs. CH at level 3,4,…,h are chosen in similar fashion, with probabilities p3, p4,…, ph respectively 11/11/2018 Elsersy 2010 CPSC601.08

Optimal parameters 11/11/2018 Elsersy 2010 CPSC601.08

Results (1/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (2/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (3/4) 11/11/2018 Elsersy 2010 CPSC601.08

Results (4/4) 11/11/2018 Elsersy 2010 CPSC601.08

Q&A 11/11/2018 Elsersy 2010 CPSC601.08

Conclusion (1/2) A distributed algorithm proposed for organizing sensors into a hierarchy of clusters. Objective is minimizing the total energy spent in the system to communicate the information gathered by these sensors to the information-processing center. The optimal parameter values found for these algorithms that minimize the energy spent in the network. 11/11/2018 Elsersy 2010 CPSC601.08

Conclusion (2/2) Assumed that the communication environment is contention and error free. In a contention-free environment, the algorithm has a time complexity of O(k1 + k2 + ... + kh ), a significant improvement over the many O(n) clustering algorithms in the literature. The algorithm is suitable for networks of large number of nodes. 11/11/2018 Elsersy 2010 CPSC601.08

Future Work Consider an underlying medium access protocol and investigate how that would affect the optimal probabilities of becoming a CH and the run-time of the algorithm. 11/11/2018 Elsersy 2010 CPSC601.08

Thank You Algorithms Issues Network Type Deployment Sensing WSN Decision Making Routing Flat Location-based Hierarchical 11/11/2018 Elsersy 2010 CPSC601.08