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Infocom'04Ossama Younis, Purdue University1 Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University

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Infocom'04Ossama Younis, Purdue University2 Contributions A new distributed clustering protocol for sensor networks that has the following properties: Energy-efficient Terminates rapidly Considers cluster quality, e.g., load-balanced clusters or dense clusters Has low message/processing overhead

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Infocom'04Ossama Younis, Purdue University3 Sensor Networks Application-specific, e.g., Monitoring seismic activities Surveying military fields Reporting radiation levels at nuclear plants Nodes are usually: Densely deployed Limited in processing, memory, and communication capabilities Constrained in battery lifetime Left unattended

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Infocom'04Ossama Younis, Purdue University4 Goals Scalability, data and state aggregation, robustness, and prolonged network lifetime Time until the first node dies Time until the last node dies How to prolong the network lifetime? Deploy redundant nodes Apply energy-efficient protocols, e.g., MAC layer protocols can reduce energy waste Topology management can distribute energy consumption What is network lifetime?

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Infocom'04Ossama Younis, Purdue University5 Topology management Cell-based approachCluster-based approach observer

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Infocom'04Ossama Younis, Purdue University6 Outline System model and requirements The Hybrid, Energy-Efficient, Distributed clustering protocol (HEED) HEED properties Evaluation Related Work Conclusion

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Infocom'04Ossama Younis, Purdue University7 System Model A set of n sensor nodes are dispersed uniformly and independently in a field Sensor nodes are Quasi-stationary Unattended Equally significant Location un-aware Homogeneous (similar capabilities) Serving multiple observers Possess a fixed number of transmission power levels

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Infocom'04Ossama Younis, Purdue University8 Requirements Our goal is to design a new clustering approach that has the following properties: Completely distributed Terminates in O(1) iterations Has low message/processing overhead Generates high energy, well-distributed cluster heads Can provide other characteristics, such as balanced or dense clusters

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Infocom'04Ossama Younis, Purdue University9 Approach (HEED) We propose the Hybrid, Energy-Efficient, Distributed clustering approach (HEED) Heed is hybrid: Clustering is based on two parameters HEED is distributed: Every node only uses information from its 1-hop neighbors (within cluster range) HEED is energy-efficient: Elects cluster heads that are rich in residual energy Re-clustering results in distributing energy consumption

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Infocom'04Ossama Younis, Purdue University10 HEED - Parameters Parameters for electing cluster heads Primary parameter: residual energy (E r ) Secondary parameter: communication cost (used to break ties) i.e., maximize energy and minimize cost Cost definition node degree (for load balancing) AMRP: Average min. reachability power (for min. intra-cluster comm. energy) 1/node degree (for dense clusters)

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Infocom'04Ossama Younis, Purdue University11 HEED – Algorithm at node v Initialization Main processing Finalization Discover neighbors within cluster range Compute the initial cluster head probability CH prob = f(E r /E max ) If v received some cluster head messages, choose one head with min cost If v does not have a cluster head, elect to become a cluster head with CH prob. CH prob = min(CH prob * 2, 1) Repeat until CH prob reaches 1 If cluster head is found, join its cluster Otherwise, elect to be cluster head

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Infocom'04Ossama Younis, Purdue University12 HEED - Example Compute CH prob and cost Elect to become cluster head Resolve ties Select your cluster head (0.2,2) (0.4,3) (0.2,3) (0.1,2) (0.1,4) (0.6,2) (0.2,5) (0.5,3) (0.8,4) (0.2,3) (0.6,4) (0.5,4) (0.1,4) (0.9,4) (0.3,2) (0.7,5) (0.3,2) (0.2,3) a1 c4 a3 a2 a5a6 c3 a12 a11 a13 a9 a7 a8 a4 a10 c2 c1 a14 Discover neighbors

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Infocom'04Ossama Younis, Purdue University13 HEED - Analysis HEED has the following properties: Completely distributed Clustering terminates in O(1) iterations: Message overhead: O(1) per node Processing overhead: O(n) per node Cluster heads are well distributed. Pr{two CHs are within the same cluster range}: (p = initial CH prob )

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Infocom'04Ossama Younis, Purdue University14 HEED – Inter-cluster communication Lemma 1 (Blough and Santi’02): Assume n nodes are dispersed uniformly and independently in an area R=[0,L] 2. If R c 2 n=aL 2 lnL, for some a>0, Rc >1, then lim n,N→∞ E(number of empty cells) = 0, where a cell is an area Lemma 2: There exists at least one cluster head a.a.s. in any area of size

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Infocom'04Ossama Younis, Purdue University15 HEED – Inter-cluster communication Theorem 1: Two cluster heads in two neighboring areas can communicate if Theorem 2: HEED produces a connected multi-hop cluster head graph (structure) asymptotically almost surely 2.7R c RtRt CH 1 CH 2

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Infocom'04Ossama Younis, Purdue University16 Performance evaluation 2000x2000 network field with 1000 nodes Demonstrating HEED properties: fast termination, rich-energy cluster heads, and cluster quality

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Infocom'04Ossama Younis, Purdue University17 Performance evaluation (cont’d) Apply HEED to an application where nodes directly contact a far observer Compare to multi-hop LEACH clustering 100x100 network Initial E r = 2 Joule 1 round = 5 TDMA frames

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Infocom'04Ossama Younis, Purdue University18 Related Work Topology management protocols suffered from at least one of the following problems: Dependence on location awareness (e.g., GAF) Slow convergence (i.e., dependent on the network diameter) (e.g., DCA) Energy efficiency was not the main goal of many protocols, e.g., Max-Min D-clustering No focus on clustering quality, such as having cluster heads well-distributed in the network (e.g. LEACH)

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Infocom'04Ossama Younis, Purdue University19 Conclusion We have proposed HEED clustering HEED is fast and has low overhead HEED can provide other features, such as load- balancing HEED is independent of: Homogeneity of node dispersion in the field Network density or diameter Distribution of energy consumption among nodes Proximity of querying observers HEED can be extended to provide multi-level hierarchy

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