QoS Routing & Distributed Clustering in Mobile Ad Hoc Networks

1. QoS Routing & Distributed Clustering in Mobile Ad Hoc Networks Mohit Garg Guide: R. K. Shyamasundar

2. What is Quality of Service? Creating “circuit switched” paths in a packet switched network… • Qualitatively • Prioritizingtraffic flows • Giving preferential treatment to certain data • Quantitatively • Bandwidth (data rate) • End-to-End delay

3. QoS in MANets Issues :- • Mobility of the nodes • Unpredictable and varying link states • Completely Distributed System -- Lack of Central Control QoS guarantees in MANets can only be “soft” i.e. probabilistic in nature

4. Some Directions… • Mobility :: Group nodes together! • Unpredictable Links :: Periodically update link status in each node • Look for Distributed algorithms!

5. Routing packets

6. Traditional Approaches Broadly two types of techniques • Pro-Active :: Keep routes to every possible destination in a routing table These tend to involve large table update overhead esp. for large networks • Reactive :: Find a route only when needed These involve large delays in route finding which may not be desirable Clearly, a compromise is needed

7. A cluster-based approach • Intra-Cluster routing :: • Pro-active • Each node keeps a table of routes to destinations within the cluster which is updated periodically • Inter-Cluster routing :: • Reactive with acquaintance updates • Border nodes play an important role in routing • Already “known” nodes keep in touch for sometime

8. Acquaintances • Nodes which are not in the same cluster • Which had communicated in the “not so distant past” – a timeout is used (Tacquaintance) • These keep exchanging their cluster bindings if changed – “acquaintanceships” break if no communication for time interval greater than Tacquaintance • Thus, each node keeps track of some nodes and can be useful in reactive route discovery and maintenance

9. Routing A B

10. Clustering

11. Clustering • How to cluster? • Characteristics of each cluster • Maintenance of clusters in the dynamic environment • Distributed algorithm? Local Optimisation should lead to global goals

12. How to cluster? Many approaches have been proposed • Hierarchical approach (cluster heads) • Based on the no. of hops • Predict the mobility of the nodes! We take refuge in pattern recognition techniques

13. Clustering Algorithm Basic Leader-Follower Algorithm (contains no cluster head!) • begininitialise n,t • w1 = x • do accept new x (loop …. • j = arg (mini |x-wi|) (find “nearest cluster”) • if |x-wj| < t (if “distance” less than threshold) • then wj=wj+n.x (join and update the weight of the cluster) • else add new w=x (form a new cluster) • w=w/|w| (normalise weight) • until no more x … until all points are classified) • end

14. 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, join, else forms its own cluster

15. 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 fraction of “old” nodes as the criteria

16. 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

17. Unknown Parameters in the model • Stability Metric Ttransient • Threshold value • Cluster size upper and lower limits • Tacquaintance Simulations may shed some light on how to choose these

18. Simulation Results

19. Scenario… • 100 x 100 units region • 75 nodes • Transmission Range = 15 units • Random number of nodes switched on at random locations in the initial iterations • Half of the nodes were imparted mobility at each instant (at an average)

20. Number of clusters vs.MAX_CLUS_SIZE • Number of clusters decrease when larger clusters are allowed • The MIN_CLUS_SIZE allowed also plays a role • By selecting both these suitably, we can expect to control the cluster sizes and numbers

21. Number of clusters vs.Tx Range • Number of clusters decrease when Tx Range increases

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

23. What does the model achieve? • Adaptive clustering • Completely distributed algorithm • No Cluster Head needed • Routing overhead reduced and acquaintance updates should provide better performance then simple routing