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Clustering in Mobile Ad hoc Networks

Clustering in Mobile Ad hoc Networks. Why Clustering?. Cluster-based control structures provides more efficient use of resources for large dynamic networks Clustering can be used for Transmission management (link-cluster architecture) Backbone formation Routing Efficiency.

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Clustering in Mobile Ad hoc Networks

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  1. Clustering in Mobile Ad hoc Networks

  2. Why Clustering? • Cluster-based control structures provides more efficient use of resources for large dynamic networks • Clustering can be used for • Transmission management (link-cluster architecture) • Backbone formation • Routing Efficiency

  3. Link-Clustered Architecture[Baker+ 1981a, 1981b, Ephremides+ 1987] • Reduces interference in multiple-access broadcast environment • Distinct clusters are formed to schedule transmissions in a contention-free way • Each cluster has a clusterhead, one or more gateways and zero or more ordinary nodes • Clusterhead schedules transmission and allocates resources within its cluster • Gateways connect adjacent clusters • To establish link-clustered control structure • Discover neighbors • Select clusterhead to form clusters • Decide on gateways between clusters

  4. Cluster Clusterhead Gateway Ordinary node Link-Clustered Architecture[Baker+ 1981a, 1981b, Ephremides+ 1987]

  5. Clusterheads • Resemble base stations in cellular networks, but dynamic • Responsible for resource allocation • Maintains network topology • Acts as routers – forwards packets from one node to another • Aware of its cluster members • Aware of its one-hop neighboring clusterheads Since clusterheads decide network topology, election of clusterheads optimally is critical

  6. Previous Work • Highest-Degree Heuristic [Gerla+ 1995, Parekh 1994] • Computes the degree of a node based on the distance (transmission range) between the node and the other nodes • The node with the maximum number of neighbors (maximum degree) is chosen to be a clusterhead and any tie is broken by the node ids Drawbacks: • A clusterhead cannot handle a large number of nodes due to resource limitations • Load handling capacity of the clusterhead puts an upper bound on the node-degree • The throughput of the system drops as the number of nodes in cluster increases

  7. Previous Work • Lowest-ID Heuristic [Baker+ 1981a, 1981b, Ephremides+ 1987] • The node with the minimum node-id is chosen to be a clusterhead • A node is called a gateway if it lies within the transmission range of two or more clusters • Distributed gateway is a pair of nodes that reside within different clusters, but they are within the transmission range of each other Drawbacks: • Since it is biased towards nodes with smaller node-ids, leading to battery drainage • It does not attempt balance the load for across all the nodes

  8. Previous Work • Node-Weight Heuristic [Basagni 1999a, 1999b] • Node-weights are assigned to nodes based on the suitability of a node being a clusterhead • The node is chosen to be a clusterhead if its node-weight is higher than any of its neighbor’s node-weights and any tie is broken by the minimum node ids Drawbacks: • No concrete criteria of assigning the node-weights • Works well for “quasi-static” networks where the nodes do not move much or move very slowly

  9. Weighted Clustering Algorithm(WCA) [Chatterjee+ 2000, 2002] • Aclusterhead can ideally support nodes • Ensures efficient MAC functioning • Minimizes delay and maximizes throughput • A clusterhead uses more battery power • Does extra work due to packet forwarding • Communicates with more number of nodes • A clusterhead should be less mobile • Helps to maintain same configuration • Avoids frequent WCA invocation • A better power usage with physically closer nodes • More power for distant nodes due to signal attenuation

  10. Weighted Clustering Algorithm (WCA) Steps 1. Compute the degreedv each node v Coordinate distance, predefined transmission range. • Compute the degree-differencefor every node For efficient MAC (medium access control) functioning. Upper bound on # of nodes a cluster head can handle.

  11. 3 2 12 13 4 1 14 17 15 7 16 5 6 Weighted Clustering Algorithm (WCA) Steps 3. Compute the sum of the distancesDv with all neighbors Energy consumption; more energy for greater dist. communication. Power required to support a link increases faster than linearly with distance.(For cellular networks)

  12. Yt Yt-1 time Xt-1 Xt Weighted Clustering Algorithm (WCA) Steps 4. Compute the average speed of every node; gives a measure of mobilityMv where and are the coordinates of the node at time and Component with less mobility is a better choice for clusterhead.

  13. Weighted Clustering Algorithm (WCA) Steps • Compute the total (cumulative) timePv a node acts as clusterhead Battery drainage = Power consumed 6. Calculate the combined weightWv for each node Wv = w1Δv + w2Dv + w3Mv + w4Pvfor each node 7. Find min Wv; choose node v as the cluster head, remove all neighbors of v for further WCA • Repeat steps 2 to 7 for the remaining nodes

  14. Load Balancing Factor (LBF) • It is desirable to balance the loads among the clusters • Load balancing factor (LBF) has defined as (should be high) where, is the number of clusterheads is the cardinality of cluster i and is the average number of neighbors of a clusterhead (N being the total number of nodes in the system)

  15. Connectivity • For clusters to communicate with each other, it is assumed that clusterheads are capable of operating in dual power mode • A clusterhead uses low power mode to communicate with its immediate neighbors within its transmission range and high power mode is used for communication with neighboring clusters • Connectivity is defined as (for multiple component graph) • Probability that a node is reachable from any other node ( 0 – 1; 1 being most desirable)

  16. Scattered nodes in the network

  17. Clusterheads are identified

  18. Clusters are formed

  19. Clusters are connected

  20. Features of WCA • Invocation of WCA is on-demand • Reduces information exchange by less system updates • Reduces computation/communication costs • Manages mobility by reaffiliations • Delays (avoids) invocation of clustering as far as possible • WCA is distributive • No clusterhead is over loaded • Balances load by limiting the cluster size

  21. Performance Metric • Number of clusterheads • Number of reaffiliations • a process where a node detaches from one clusterhead and attaches to another • Number of dominant set updates • when a node can no longer attach to any of the existing clusterheads These parameters are studied for the varying number of nodes transmission range maximum displacement

  22. Simulation Environment • System with N nodes on a 100x100 grid • N was varied between 20 and 60 • Nodes moved in all directions randomly • Velocity of nodes were varied uniformly between 0 and 10 • Transmission range of nodes was varied between 0 and 70 • Ideal degree was fixed at = 10 • Weighing factors: w1 = 0.7, w2 = 0.2, w3 = 0.05 and w4 = 0.05

  23. Max displacement = 5 (const) Transmission range = 0 - 70 Number of nodes = 20 - 60 Ideal degree = 10 Experimental Results

  24. Max displacement = 1 - 10 Transmission range = 30 (const) Number of nodes = 20 - 60 Ideal degree = 10 Experimental Results

  25. Load Balancing

  26. Connectivity

  27. Performance of WCA

  28. References [Baker+ 1981a] D.J. Baker and A. Ephremides, A Distributed Algorithm for Organizing Mobile Radio Telecommunication Networks, Proceedings of the 2nd International Conference on Distributed Computer Systems, April 1981, pp. 476-483. [Baker+ 1981b] D.J. Baker and A. Ephremides, The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm, IEEE Transactions on Communications COM-29(11), 1981, pp. 1694-1701. [Basagni 1999a] S. Basagni, Distributed Clustering for Ad hoc Networks, Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks, June 1999, pp. 310-315. [Basagni 1999b] S. Basagni, Distributive and Mobility-Adaptive Clustering for Multimedia Support in Multi-hop Wireless Networks, Proceedings of Vehicular Technology Conference, VTC, Vol. 2, 1999-Fall, pp. 889-893. [Chatterjee+ 2002] M. Chatterjee, S. K. Das and D. Turgut, WCA: A Weighted Clustering Algorithm for Mobile Ad hoc Networks. Journal of Cluster Computing (Special Issue on Mobile Ad hoc Networks), Vol. 5, No. 2, April 2002, pp. 193-204. [Chatterjee+ 2000] M. Chatterjee, S. K. Das and D. Turgut, An On-Demand Weighted Clustering Algorithm (WCA) for Ad hoc Networks. IEEE GLOBECOM 2000, pp. 1697-1701. [Ephremides+ 1987] A. Ephremides J.E. Wieselthier and D.J. Baker, A Design Concept for Reliable Mobile Radio Networks with Frequency Hopping Signaling, Proceedings of IEEE, Vol. 75(1), 1987, pp. 56-73. [Parekh 1994] A.K. Parekh, Selecting Routers in Ad-hoc Wireless Networks, Proceedings of the SBT/IEEE International Telecommunications Symposium, August 1994.

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