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1 Directed Budget-Based Clustering for WSN Leonidas Tzevelekas, Ioannis Stavrakakis Department of Informatics & Telecommunications National and Kapodistrian.

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Presentation on theme: "1 Directed Budget-Based Clustering for WSN Leonidas Tzevelekas, Ioannis Stavrakakis Department of Informatics & Telecommunications National and Kapodistrian."— Presentation transcript:

1 1 Directed Budget-Based Clustering for WSN Leonidas Tzevelekas, Ioannis Stavrakakis Department of Informatics & Telecommunications National and Kapodistrian University of Athens LOCAN 2006

2 2 Large-scale Wireless Sensor Networks Sensor motes characteristics Cheap, tiny, embedded devices Used in orders of thousands (or millions) Resulting in extremely high network densities Also: extreme energy constraints of individual nodes Also: short-range wireless connectivity only possible Wireless Sensor Network characteristics Autonomous operation (no human interventions possible) Should be self-organizing, self-healing, self-* Overall: may exhibit arbitrarily sophisticated behavior Highly distributed networking environment (new methods of network organization and operation)

3 3 Hierarchical network self-organization Network self-organization in clusters Enhances sensor node coordination Network management In-network processing of sensed data Clusters with fixed-size number of sensors Reduced routing protocol overhead Accommodating specific service requirements Distributed/ decentralized cluster formation Radically decentralized algorithms required (Rapid, Persistent) Low message complexity -> energy efficiency Our contribution – A strictly localized algorithm for fixed-size cluster formation in large-scale Wireless Sensor Networks

4 4 Distributed cluster formation: Budget-based Clustering Distributed cluster formation methodology (adopted in our work) Main idea: Growing cluster’s nodes do even budget distributions of tokens among their first-hop neighbors Algorithm description (formal) – An initiator node is chosen randomly in the network (among unclustered nodes) – Initiator assigned a budget of B tokens of which it accounts one for himself and distributes B-1 evenly among its neighbors – Subsequent nodes receiving a budget do the same until the budget is exhausted or no more growth is possible

5 5 Rapid, Persistent examples B 2 A C D E F G B=11 2 Rapid cluster 2 A B C D E F G B=11 2 H I J Persistent cluster B. Persistent algorithm Recursive elaboration of RapidRecursive elaboration of Rapid Even distribution of budget to neighbors except from the parent nodeEven distribution of budget to neighbors except from the parent node Persistent re-distribution of budget shortfall (if any)Persistent re-distribution of budget shortfall (if any) Good clustering performance network-wide at cost of higher message complexityGood clustering performance network-wide at cost of higher message complexity A. Rapid algorithm Fast, one-way budget distribution process Fast, one-way budget distribution process Even distribution of budget among neighbors except from the parent node Even distribution of budget among neighbors except from the parent node No accounting for wasted tokens No accounting for wasted tokens Poor clustering performance network-wide Poor clustering performance network-wide

6 6 Initiators selection process 1. Randomized initiators methodology – Nodes run count-down timers with exponentially distributed initial values – Nodes become initiators when their timer fires – Bounded probability that multiple initiators are concurrently active in the same neighborhood 2. Sequential approximation for initiator picking (useful for computer simulations) – Next initiator picked only after currently growing cluster completes – Associated cluster is allowed to fully grow – Only one initiator active network-wide at each time instant – Identical with optimistic randomized timers methodology

7 7 Rapid, Persistent major drawback “Blindness” in budget distribution process  No awareness of neighbor’s clustering status at each distributing node  Even budget distribution always among ALL physical neighbors  Tokens directed to “bad” neighbors => token waste Tokens are frequently wasted/ returned (Rapid/ Persistent) – Resulting in bad clustering performance: Very low average clustersizes (Rapid) High number of budget shortfall redistributions (Persistent)

8 8 Proposals for improved clustering performance 1. Fighting inter-cluster token distribution contentions – Nodes already clustered under previous inititiator => receive NO tokens from growing cluster Tokens should be directed away from clustered nodes Eliminate inter-cluster token distribution contentions (sequential initiators) Significantly reduce inter-cluster token distribution contentions (randomized initiators) 2. Fighting intra-cluster token distribution contentions – A growing cluster’s tokens should not contend for common unclustered nodes Significantly reduces token distribution contentions for a single growing cluster

9 9 Directed Budget-Based Clustering (DBB) Assumption for radically distributed networks – Periodic HELLO messages to set-up/ maintain local physical network topology => Same HELLO messages to set-up/ maintain local clustered network topology DBB algorithm’s specific characteristics Utilize HELLO messages to convey additional clustering status information of nodes Minimal overhead of 1-bit flag only (at HELLO messages) Some overhead for storing clustering status information in tables (at nodes) Nodes update their neighbors’ clustering status information prior to executing the algorithm’s steps Algorithm’s steps coincide with the periodicity of HELLO messages Clustering messages (tokens, ACKs) embedded into HELLO messages

10 10 Directed Budget-Based Clustering (DBB) (Fighting inter-cluster token distribution contentions) Example: Spatial evolution of clustering process for DBB Tokens “bounce” on clustered nodes of another initiator and are directed away, thus avoiding to be wasted or returned  clustering process becomes completely transparent in localized HELLO message exchanges  STRICTLY LOCALIZED CLUSTERING PROTOCOL

11 11 Directed Budget-Based Clustering with Random Delays (DBB-RD) Effect: to “desynchronize” budget distributions at neighboring nodes for a single growing cluster 1.Introducing: random delay factor r as an integer amount of rounds of HELLO message exchanges to delay the current budget distribution at each node 2.Advantage: subsequent HELLO message exchanges allow for updating of the clustering status information among nodes 3.Drawback: additional delays to complete overall network decomposition (Fighting intra-cluster token distribution contentions)

12 12 Network simulation scenario Network simulation settings N=6000 nodes Square plane of size l=1000m with random x, y coordinates for each node Individual nodes transmission range r=25m Average connectivity degree ρ= nodes Clusterbound targeted B=30/ 60 for medium/ large-sized clusters Connectivity of graph is checked by displacing any disconnected nodes after initial random placement Sequential picking of initiators is used (always one cluster growing in the network) Random delay factor r є [0,α-1), where a is random delay parameter

13 13 Network simulation scenario Metrics: 1. Time required or consecutive rounds of HELLO message exchanges required till overall network decomposition 2. Average clustersize achieved over all clusters formed (optimum is the targeted bound B) 3. Average number of clusters in the network formed (optimum is I=N/B) Analysis of results K=5 independent runs for each set of parameter settings Measured quantities are averaged and 0.95-confidence intervals are presented

14 14 Simulation results for Rapid/ Persistent Rapid: low average clustersize compared with B (8.69 when B=30 and when B=60) Rapid: very fast network decomposition (3258 rounds for 6000 nodes when B=30) Persistent: high average clustersize compared to B (21.03 when B=30 and when B=60) Persistent: up to six times more rounds required than Rapid (18992 rounds for 6000 nodes with B=30 compared with 3258 rounds for Rapid) Sims verify negative effects of token waste in clustering performance of Rapid, Persistent

15 15 Simulation results for DBB/ DBB-RD DBB: results indicate clustering performance improvements due to avoiding inter-cluster token waste DBB: average clustersizes significantly higher compared with Rapid (28% higher for B=30, 26.6% higher for B=60) DBB: Faster network decomposition than both Rapid AND Persistent DBB-RD: results confirm the positive effect of “desynchronization” of budget distributions (fighting intra-cluster token distribution contentions) DBB-RD: higher clustersizes than DBB for all values of α, though additional overall delay for network decomposition

16 16 DBB/ DBB-RD average decomposition time a є {0, 3, 5, 10} (a=0 is DBB algorithm) Additional delay in network decomposition time with growing interval for random delay factor r

17 17 DBB/ DBB-RD average clustersizes a є {0, 3, 5, 10} From a=0 to a=3, relative increase of metric by 27.85% From a=3 to a=5, relative increase of metric by 4.88% From a=5 to a=10, relative increase of metric lower than 4%

18 18 THANK YOU Any questions?

19 19 Hierarchical network self-organization for large scale sensor networks Our work: a strictly localized protocol aiming at decomposing large scale sensor networks into non-overlapping clusters of bounded size Localized protocols, algorithms for network self- organization seem to fit to special characteristics/ constraints of the WSN

20 20 Localized/ strictly localized protocols /algorithms  LOCALIZED protocols/ algorithms Inherently distributed algorithms utilizing local interactions among neighbor nodes to achieve a well-defined global objective overall in the network Already used for maintaining/updating local network topology at each node, and other things like energy efficient flooding, broadcasting, etc. Primarily enabled through the exchange of periodic HELLO messages among 1-hop neighbors of nodes  STRICTLY LOCALIZED protocols/ algorithms Information processed by a node is either (a) local in nature or (b) global in nature, but obtainable by querying only the node’s neighbors or itself


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