1. 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. 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. 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

4. 4 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. 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.

6. 6 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)

7. 7 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

8. 8 Basic Leader Follower (BLF) Clustering Algorithm (single pass, converges faster and contains no cluster head!) 1.begin initialise n,t 2. w 1 = x 3. do accept new x (loop …. 4. j = arg (min i |x-w i |) (find “nearest cluster”) 5. if |x-w j | < t(if “distance” less than threshold) 6. then w j =w j +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

9. 9 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.

10. 10 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

11. 11 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

12. 12 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

13. 13 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(); }

14. 14 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(); }

15. 15 Unknown Parameters in the model  Stability Metric  Stability Threshold  Cluster size upper and lower limits Simulations: shed light on how to choose the parameters

16. 16 Simulation Results

17. 17 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

18. 18 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

19. 19 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

20. 20 …Variations w.r.t. Cluster Size Limits MIN_CLUS_SIZE=3, MAX_CLUS_SIZE=26MIN_CLUS_SIZE=10, MAX_CLUS_SIZE=26

21. 21 Clustering Messages vs. MIN_CLUS_SIZE  Higher MIN_CLUS_SIZE means more clusters tend to disintegrate  Hence, higher cluster overhead MAX_CLUS_SIZE=20

22. 22 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

23. 23 What does the model achieve?  Adaptive clustering  Completely distributed algorithm  No Cluster Head needed  Can control cluster properties using simple techniques?

24. 24 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