Directed BudgetBased Clustering for WSN

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Directed Budget-Based Clustering for WSN
LOCAN 2006

• Leonidas Tzevelekas, Ioannis Stavrakakis
• Department of Informatics Telecommunications
• National and Kapodistrian University of Athens

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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)

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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 -gt energy efficiency
• Our contribution
• A strictly localized algorithm for fixed-size
  cluster formation in large-scale Wireless Sensor
  Networks

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Distributed cluster formation Budget-based
Clustering

• Distributed cluster formation methodology
  (adopted in our work)
• Main idea Growing clusters 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

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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

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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

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Rapid, Persistent major drawback

• Blindness in budget distribution process
• No awareness of neighbors clustering status at
  each distributing node
• Even budget distribution always among ALL
  physical neighbors
• Tokens directed to bad neighbors gt 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)

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Proposals for improved clustering performance

• Fighting inter-cluster token distribution
  contentions
• Nodes already clustered under previous
  inititiator gt 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 clusters tokens should not contend for
  common unclustered nodes
• Significantly reduces token distribution
  contentions for a single growing cluster

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Directed Budget-Based Clustering (DBB)

• Assumption for radically distributed networks
• Periodic HELLO messages to set-up/ maintain local
  physical network topology
• gt Same HELLO messages to set-up/ maintain local
  clustered network topology
• DBB algorithms 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 algorithms
  steps
• Algorithms steps coincide with the periodicity
  of HELLO messages
• Clustering messages (tokens, ACKs) embedded into
  HELLO messages

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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

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Directed Budget-Based Clustering with Random
Delays (DBB-RD)
(Fighting intra-cluster token distribution
contentions)

• Introducing random delay factor r as an integer
  amount of rounds of HELLO message exchanges to
  delay the current budget distribution at 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

• Effect to desynchronize budget distributions
  at neighboring nodes for a single growing cluster

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Network simulation scenario

• Network simulation settings
• N6000 nodes
• Square plane of size l1000m with random x, y
  coordinates for each node
• Individual nodes transmission range r25m
• Average connectivity degree ?11.781 nodes
• Clusterbound targeted B30/ 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 ? 0,a-1), where a is
  random delay parameter

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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 IN/B)

Analysis of results K5 independent runs for each
set of parameter settings Measured quantities are
averaged and 0.95-confidence intervals are
presented
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Simulation results for Rapid/ Persistent
Rapid low average clustersize compared with B
(8.69 when B30 and 11.13 when B60) Rapid very
fast network decomposition (3258 rounds for 6000
nodes when B30) Persistent high average
clustersize compared to B (21.03 when B30 and
37.99 when B60) Persistent up to six times more
rounds required than Rapid (18992 rounds for 6000
nodes with B30 compared with 3258 rounds for
Rapid)
Sims verify negative effects of token waste in
clustering performance of Rapid, Persistent
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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 B30,
26.6 higher for B60) 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 a, though additional overall
delay for network decomposition
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DBB/ DBB-RD average decomposition time
a ? 0, 3, 5, 10 (a0 is DBB algorithm) Additiona
l delay in network decomposition time with
growing interval for random delay factor r
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DBB/ DBB-RD average clustersizes
a ? 0, 3, 5, 10 From a0 to a3, relative
increase of metric by 27.85 From a3 to a5,
relative increase of metric by 4.88 From a5 to
a10, relative increase of metric lower than 4
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THANK YOU

• Any questions?

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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
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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 nodes neighbors
  or itself