Directed Budget-Based Clustering for WSN

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

2. Large-scale Wireless Sensor Networks Sensor motes characteristics • Cheap, tiny, embeddeddevices • Used in orders of thousands (or millions) • Resulting in extremely highnetwork 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. 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. 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. A 2 2 D B=11 2 C Rapid cluster 2 F E 2 1 G Persistent cluster B A 2 2 D 2 C B=11 2 F 6 E G 5 2 J 1 H 1 I Rapid, Persistent examples B • A.Rapid algorithm • Fast, one-way budget distribution process • Even distribution of budget among neighbors except from the parent node • No accounting for wasted tokens • Poor clustering performance network-wide • B.Persistent algorithm • Recursive elaboration of Rapid • Even distribution of budget to neighbors except from the parent node • Persistent re-distribution of budget shortfall (if any) • Good clustering performance network-wide at cost of higher message complexity

6. Initiators selection process • 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 • 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. 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. Proposals for improved clustering performance • Fighting inter-clustertoken distribution contentions • Nodes alreadyclusteredunder 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) • Fighting intra-cluster token distribution contentions • A growing cluster’s tokens should not contend for commonunclustered nodes • Significantly reduces token distribution contentions for a single growing cluster

9. Directed Budget-Based Clustering (DBB) Assumption for radically distributed networks • Periodic HELLOmessages 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. 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 transparentin localized HELLO message exchanges • STRICTLY LOCALIZED CLUSTERING PROTOCOL

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

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=11.781nodes • 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. Network simulation scenario Metrics: • Time required or consecutive rounds of HELLO message exchanges required till overall network decomposition • Average clustersize achieved over all clusters formed (optimum is the targeted bound B) • 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. Simulation results for Rapid/ Persistent Rapid: low average clustersize compared with B (8.69 when B=30 and 11.13 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 37.99 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. 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 clustersizesthan DBB for all values of α, though additional overall delay for network decomposition

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. DBB/ DBB-RD average clustersizes a є {0, 3, 5, 10} From a=0 to a=3, relative increase of metricby27.85% From a=3 to a=5, relative increase of metricby4.88% From a=5 to a=10, relative increase of metric lower than 4%

18. THANK YOU Any questions?

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

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 HELLOmessages 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