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Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University

Distributed Clustering for Scalable, Long-Lived Sensor Networks. node degree (for load balancing). AMRP: Average min. reachability power (for min. intra-cluster comm. energy). 1/node degree (for dense clusters). Ossama Younis and Sonia Fahmy

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Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University

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  1. Distributed Clustering for Scalable, Long-Lived Sensor Networks node degree (for load balancing) AMRP: Average min. reachability power (for min. intra-cluster comm. energy) 1/node degree (for dense clusters) Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University 3. Clustering Challenges 2. System Challenges 1. Model • How can the system provide: • Scalability (to thousands of nodes)? • Prolonged network lifetime? • Data and state aggregation? • Robustness in the face of unexpected failures? • Security of sensor communications? • Approach Clustering • Completely distributed • Energy-efficient • O(1) iterations to terminate (i.e., independent of network diameter) • Low message/processing overhead • High energy, well-spread cluster heads • Other cluster characteristics, such as balanced or dense clusters • Approach HEED (Hybrid, Energy-Efficient, Distributed clustering) • Network: • Rectangular field with a large number of dispersed sensor nodes • Sensor nodes: • Location un-aware and quasi-stationary • Homogeneous and equally significant • Unattended (infeasible to recharge) • Example applications: • Seismic monitoring or field surveillance. 5. HEED at node v 6. Examples 4. Parameters • Primary parameter (maximize energy) • Residual Energy (Er) • Secondary parameter (minimize cost) • Used to break ties Balanced clusters • Discover neighbors(v) within cluster range • , 0 <Cprob< 1, Emax = max. battery power, CHprob ≥ pmin (e.g., pmin = 10-4) CH: Cluster Head Cost definition N Y CHprob≤1 ? Dense clusters Covered? Covered? N N Y v is CH Y 7. Properties received CH msg? • Completely distributed √ • Clustering requires, i.e., O(1) iterations √ • Message overhead: O(1) per node√ • Processing overhead: O(N) per node √ • Pr{two CH’s within the same cluster range}, ,where p=initial CHprob N Y Stop Clustering 1000 nodes shows the least variance in cluster size for the load balanced organization Elect to become CH with prob. CHprob Pick CH with lowest cost CHprob = CHprob x 2 9. Now what ? 8. Sample Performance Results • How effective is HEED when integrated with a hierarchical routing organization? • Is it possible to extend HEED to serve networks operating in environments where unexpected failures occur? Yes, we are currently designing theREAD (Robust, Energy-Aware, Distributed)clustering protocol. READ can generate network graphs that are k-fault tolerant by building multiple independent cluster structures. • How can HEED be adapted to secure sensor communications? • Contact us: {oyounis,fahmy}@cs.purdue.edu • For more information: http://www.cs.purdue.edu/homes/fahmy/ • Compare HEED clustering quality to a weight-based clustering approach (e.g., DCA). Setup: 2000x2000 area, 1000 nodes, node residual energy = Uniform(0,1) Joule • Apply to an application and compare to an optimized version of LEACH (Heinzelman et al., 2002) and direct communication to demonstrate how clustering prolongs the network lifetime HEED selects CHs that are rich in energy HEED overhead is not significant Energy dissipates slowly as the distance increases

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