1 / 28

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.

kibo-lucas
Download Presentation

Clustering in Mobile Ad hoc Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related