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

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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.

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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

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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 don’t maintain any cluster leader

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Basic Leader Follower (BLF) Clustering Algorithm

(single pass, converges faster and contains no cluster head!)

1. begin initialise n,t

2. w1 = x

3. do accept new x (loop ….

4. j = arg (mini |x-wi|) (find “nearest cluster”)

5. if |x-wj| < t (if “distance” less than threshold)

6. then wj=wj+n.x (join and update the weight of the cluster)

7. else add new w=x (form a new cluster)

8. w=w/|w| (normalise weight)

9. until no more x … until all points are classified)

10. 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)<MIN_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_SIZE=3, MAX_CLUS_SIZE=26 MIN_CLUS_SIZE=10, MAX_CLUS_SIZE=26

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Clustering Messages vs. MIN_CLUS_SIZE

Higher MIN_CLUS_SIZE means more clusters tend to disintegrate

Hence, higher cluster overhead

MAX_CLUS_SIZE=20

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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