A Distributed Clustering Framework for MANETS

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A Distributed Clustering Framework for MANETS

• Mohit Garg, IIT Bombay
• RK Shyamasundar
• School of Tech. Computer Science
• Tata Institute of Fundamental Research
• Mumbai 400 005, India

2
MANETS

• Mobile Ad-hoc Networks no fixed infrastructure,
  hosts are mobile Security, power management,
  bandwidth efficiency,
• Sensor and ad-hoc wireless networks
• Several challenges, for routing, data
  aggregation, query processing etc.

3
Routing in MANETS

• Pro-Active Routing
• Keeping routes to all possible destinations
• Keep track of link parameters to achieve QoS
• Overhead for maintaining exchanging info.
• Reactive Routing
• Find paths on demand
• Less overhead but large delays
• Even Flooding algorithms can be clubbed under
  this framework

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Routing in MANETS Scalability

• Pro-Active Routing
• Not scalable due to the need of large bandwidth
  required for exchanging network information
• Reactive Routing
• Not Scalable due to large delays when source and
  destinations are separated by multiple hops.
• Clustering Strategies A Tradeoff

5
Clustering Algorithms

• Pro-active approaches within a cluster
• Reactive approaches for inter-cluster routing
• Provides a sort of masking with respect to
  mobility of nodes
• Nodes in the respective clusters update their own
  links and routes when a node moves.

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Distributed Clustering Alg.

• Use mobility to advantage (in certain
  non-real-time situations they increase the
  throughput)
• Restrict cascading effect and achieve stability
• As MANETS have no central authority, useful to
  use completely distributed strategies (emergent
  algorithms)

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Distributed Clustering Algorithm

• Clustering mechanism is independent of the
  routing algorithm
• It should work on a decomposed (partitioned)
  network
• Note that we dont maintain any cluster leader

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Basic Leader Follower (BLF) Clustering Algorithm

• (single pass, converges faster and contains no
  cluster head!)
• begin initialise n,t
• w1 x
• do accept new x (loop .
• j arg (mini x-wi) (find
  nearest cluster)
• if x-wj lt t (if distance
  less than threshold)
• then wjwjn.x (join
  and update the weight of the cluster)
• else add new wx (form a
  new cluster)
• ww/w
  (normalise weight)
• until no more x until all points
  are classified)
• end

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Towards Distributed BLF Alg

• On line algorithm (forms new clusters as and when
  new data points emerge)
• Several unsupervised algorithms form a basis
• Need to define
• Define a measure of closeness to capture
  mobility
• Adapt the algorithm as a distributed alg.

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Distributed BLF algorithm

• Each node wakes up
• Looks around for clusters
• If finds one which satisfies a stability
  threshold, keeps it as a probable candidate
• Compares cluster sizes
• if suitable, joins, else forms its own cluster

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Which one is more stable?

• Each cluster has a stability metric associated
  with it which should lie above a suitably chosen
  threshold for the new node to join it
• Stability metric is important we have currently
  chosen the cluster-age of the node

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Cluster Maintenance

• New nodes do not join clusters if the cluster
  size is equal to the maximum allowed
• Minimum size also specified and clusters smaller
  than that tend to disintegrate
• Clusters can be dynamically maintained in exactly
  the same way in which cluster formation takes
  place

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Algorithm for un-clustered Node

• while(!myself_clustered)
• transmit(clus_find)
• waitforresponses()
• parse_responses()
• choose_suitable_cluster()
• if(suitable_cluster_exists)
• send(clus_join_request)
• waitfor(clus_join_reply)
• if(clus_join_accept) updatemyclus()
• else formownclus()
• else formownclus()

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Algorithm for Clustered Node

• while(1)
• if (size(myclus)ltMIN_CLUS_SIZE
  disintegrate_time)
• transmit(clus_find)
• do_work()
• if(received(clus_info)
• check_suitability()
• if(suitable_cluster_exists)
• send(clus_join_request)
• waitfor(clus_join_reply)
• if(clus_join_accept) updatemyclus()

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Unknown Parameters in the model

• Stability Metric
• Stability Threshold
• Cluster size upper and lower limits
• Simulations shed light on how to choose the
  parameters

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Simulation Results
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Discussion Expected Results

• Average Cluster Size should increase on
  increasing MAX_CLUS_SIZE and MIN_CLUS_SIZE
• Number of Clustering messages should increase
  with MIN_CLUS_SIZE
• Stability Metric and Threshold should govern the
  lifetime of clusters

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Scenarios

• 100 x 100 units region
• 75 nodes
• Transmission Range 15 units
• Nodes switched on at random locations in the
  initial iterations
• On an average half of the nodes were imparted
  mobility at each instant

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Variation w.r.t. Cluster Size Limits

• Number of clusters decrease when larger clusters
  are allowed
• The MIN_CLUS_SIZE does not play a very major
  role. Only helps in small increase in avgerage
  cluster size.
• Choice of these should depend on the number of
  nodes and overheads allowed

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Variations w.r.t. Cluster Size Limits
MIN_CLUS_SIZE3, MAX_CLUS_SIZE26
MIN_CLUS_SIZE10, MAX_CLUS_SIZE26
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Clustering Messages vs. MIN_CLUS_SIZE
MAX_CLUS_SIZE20

• Higher MIN_CLUS_SIZE means more clusters tend to
  disintegrate
• Hence, higher cluster overhead

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Rate of cluster deletions vs.stability threshold

• Number of cluster deletions decrease when
  stability threshold increases
• But higher threshold means larger number of
  clusters which may not be desirable
• Gaussian metric yields lower deletions than Step
  metric

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What does the model achieve?

• Adaptive clustering
• Completely distributed algorithm
• No Cluster Head needed
• Can control cluster properties using simple
  techniques?

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Future work

• Simulation using real mobility sources
• Clustering has a wide role in MANETS Sensor
  networks
• finding routing algorithm taking into account
  the limitations
• Subdividing sensor networks into non-overlapping
  sub-divisions of physically close nodes for
  routing, data aggregation, query processing etc.
• Location finding in the context of sensor networks