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Unified Clustering Mechanism for Multi-Cluster Mobile Ad Hoc Networks

Unified Clustering Mechanism for Multi-Cluster Mobile Ad Hoc Networks. Department of Electrical Engineering The University of Texas at Dallas Final Oral Examination for Ph.D. Summer 2003 Aqeel A. Siddiqui. Research Objectives. Unification of clustering mechanisms

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Unified Clustering Mechanism for Multi-Cluster Mobile Ad Hoc Networks

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  1. Unified Clustering Mechanism for Multi-Cluster Mobile Ad Hoc Networks Department of Electrical Engineering The University of Texas at Dallas Final Oral Examination for Ph.D. Summer 2003 Aqeel A. Siddiqui

  2. Research Objectives • Unification of clustering mechanisms • Is the unified clustering mechanism stable? • Propose new performance measures for clustering mechanisms • Performance analysis of clustering mechanisms

  3. 0 8 7 2 A Cluster-based Ad Hoc Network Clusterheads: 1, 5, 8 Gateways: 3, 4, 6 Backbone: 1, 3, 4, 5, 6, 8 9 6 1 3 5 4

  4. RESEARCH BACKGROUND

  5. Mobile Ad Hoc Network (MANET) • Characteristics: • Wireless links • Dynamic network topology • All nodes can act as router • Resource poor nodes • Also called wireless multihop networks • Applications: • Military • Emergency • Sensor networks • Bluetooth • MANET: http://www.ietf.org/html.charters/manet-charter.html

  6. MANET – Other Routing Approaches • Flooding • Destination-Sequenced Distance Vector (DSDV) • Ad Hoc on Demand Distance Vector (AODV) • Dynamic Source Routing (DSR)

  7. Signalling Data Data Application Protocol Data Transport Protocol Clustering Protocol Routing Protocol Packet Forwarding Protocol Link Layer Protocol Physical Layer Protocol Cluster-basedAd HocNetwork Protocol Layers

  8. Existing Clustering Mechanisms

  9. Existing Node-id Based Clustering • Each node at most one hop away from clusterhead • Node with highest ID in a cluster becomes clusterhead • Poor clusterhead load distribution • Dennis J. Baker and Anthony Ephremides. The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm. IEEE Transactions on Communications, Vol. Com-29, No. 11, November 1981, pages 1694-1701.

  10. Existing Connectivity-based Clustering • Node with highest connectivity in a cluster becomes clusterhead • Yields minimum number of clusters • Poor clusterhead load distribution and clusterhead stability • Mario Gerla and Jack Tzu-Chieh Tsai. Multicluster, Mobile, Multimedia Radio Network. ACM Journal on Wireless Networks, Vol. 1, No. 3:255-265, 1995.

  11. Research Objectives • Unification of clustering mechanisms • Is the unified clustering mechanism stable? • Propose new performance measures for clustering mechanisms • Performance analysis of clustering mechanisms

  12. Clusterhead-time Based Clustering • In node-id and connectivity-based mechanisms, the load distribution is unfair. • Nodes with less average clusterhead-time should be preferred to become clusterhead • Good clusterhead load distribution • Poor clusterhead stability • A threshold to prevent too frequent changes in clusterheads

  13. Unified Clustering Mechanism Availability Factor • Availability factor ai(t), range 0-1, dependant on either one of the following: • identity of node, i • connectivity, ci(t) • fraction of time the ithnode remains a clusterhead, qi(t)

  14. Unified Clustering Algorithm Few definitions • t: Incremental period • wi(t): Indication if ith node is clusterhead • vi(t): Indication if ith node is covered • li,j (t): Link status between ith and jth node

  15. Unified Clustering Algorithm Clustering Criteria • The ith node decides at time (t+t) to become a clusterhead, if at time t • wi(t)=0, • wj(t)=0 for all neighbors j, • ai(t) aj(t), for all uncovered neighbors j < i, and • ai(t) aj(t), for all uncovered neighbors j > i.

  16. Unified Clustering Algorithm Clustering Criteria (contd.] • The ith node decides at time (t+t) to remain a clusterhead, if at time t • wi(t)=1, • ai(t) aj(t), for all clusterhead neighbors j < i, and • ai(t) aj(t), for all clusterhead neighbors j > i.

  17. Unified Clustering Algorithm Clustering Criteria (contd.] • The ith node decides at time (t+t) to takeover the role of clusterhead, if at time t • wi(t)=0, • wj(t)=1 for at least one neighbor j, and • ai(t) - aj(t)  aTH, for all clusterhead neighbors j < i, and • ai(t) - aj(t)  aTH, for all clusterhead neighbors j > i.

  18. Unified Clustering Algorithm Clustering Criteria (contd.] • The ith node decides at time (t+t) to assume the role of regular node in all other cases.

  19. wj from other nodes aj from other nodes i fn ain fa wi (t+t) aic ai ci fc aiq ai to other nodes qi wi to other nodes fq hq Block diagram for node i Unified Clustering Mechanism Model

  20. Research Objectives • Unification of clustering mechanisms • Is the unified clustering mechanism stable? • Propose new performance measures for clustering mechanisms • Performance analysis of clustering mechanisms

  21. Discrete Linear Control System • Definition • Unified clustering mechanism • x[kT] is clusterhead state, wi(t) • u[kT] is the link status, li,j (t) Unified Clustering Mechanism is Non-Linear!

  22. Discrete Non-Linear Control SystemDefinition • Stability in the sense of Liapunov Consider a region  in the state space enclosing an equilibrium point x0. This equilibrium point is stable provided that there is a region (), which is contained within , such that any trajectory starting in the region  does not leave the region . This permits the existence of a continuous oscillation about the equilibrium point. • Asymptotic stability An equilibrium point is asymptotically stable if, in addition to being stable in the sense of Liapunov, all trajectories approach the equilibrium point. This is the stability definition usually used in control-system design.

  23. Unified Clustering MechanismStability in different scenarios • Case: Node-id or Connectivity based For a given u, there will be a unique set of availability factor a. Thus we will get a unique output w. Therefore, such a system will be stable. • Case: Clusterhead-time based For a given (fixed) u, the equilibrium point will change from time to time. The output w will follow the trajectory w(t1), w(t2), w(t3), and so on, in the state space. But for each equilibrium point w(tk), the system is designed such that it approaches equilibrium. Thus, the system is still stable.

  24. Research Objectives • Unification of clustering mechanisms • Is the unified clustering mechanism stable? • Propose new performance measures for clustering mechanisms • Performance analysis of clustering mechanisms

  25. Proposed Clustering Performance Measures • Clusterhead Granularity Fraction of nodes which are clusterhead. • Clusterhead Load distribution Distribution of clusterhead role among nodes. • Clusterhead Stability Frequency of changes in clusterheads.

  26. Clustering PerformanceClusterhead Granularity • Let G(t) be the granularity of clusterheads of the system as defined below:

  27. Clustering PerformanceClusterhead Load Distribution • Let D(t) be the clusterhead load distribution of the system as defined below:

  28. Clustering PerformanceClusterhead Stability • Let S(t) be the clusterhead stability of the system as defined below:

  29. System G S D N-clusterhead 1 1 1 1-clusterhead non-hopping 1/N 1 <<1 1-clusterhead slow hopping 1/N 1 1-clusterhead fast hopping 1/N 1 Clustering PerformanceExamples

  30. Research Objectives • Unification of clustering mechanisms • Is the unified clustering mechanism stable? • Propose new performance measures for clustering mechanisms • Performance analysis of clustering mechanisms

  31. D and G Relationship • Assumptions • Static nodes (wireless connection but no movements) • Node-id or Connectivity based clustering • Result

  32. Impact of Availability Factor Threshold • The purpose of availability factor threshold is to limit frequent changes in clusterheads. • The availability factor threshold introduces a hysteresis, thus favoring a node to remain clusterhead once it becomes clusterhead. • The larger the availability factor threshold the higher will be the clusterhead stability of the system. • As a consequence it may also result in reduced clusterhead load distribution.

  33. a 1.0 x1 x3 x4 x2 aTH x5 ttrans x6 t t0 t1 t2 t3 T T T Clusterhead-time based clustering State Transitions – affect of threshold Static nodes with wireless links

  34. Clusterhead-time based clustering Performance (Static Network) • The number of clusterhead changes ( ) is proportional to The larger the availability factor threshold the higher will be the stability of the system. The larger the availability factor threshold the smaller the clusterhead load distribution.

  35. Clustering PerformanceSimulation Parameters • Network: • Number of mobile nodes, N = 20 • Service area, A = 1 km2 (1000m x 1000 m) • Maximum coverage radius, R = 250 m • Various average speeds 0-5 m/s • Mobility: • Two patterns

  36. Clustering PerformanceSimulation Protocols • Clustering: • Link Information Broadcast (LIB) • Link Information Unicast (LIU) • Member Link Info (MLI) • System Info (SYS) • Beacon (BEA) • Routing: • Routing Request (RRQ) • Routing Response (RRP) • Application: • Data Request (DRQ) • Data Response (DRP)

  37. Clustering PerformanceSimulation Results – node-id based

  38. Clustering PerformanceSimulation Results – connectivity based

  39. Clustering PerformanceSimulation Results – CH-time based

  40. Performance of CH-time based ClusteringAverage Mobility 3m/s

  41. ADDITIONAL EXPIREMENTS

  42. Additional Work • Clustering based on the mobility of the nodes is added to the unified mechanism. Experiments show that it improves the clusterhead stability. • Many simulations are performed with various combinations of the clustering mechanisms (e.g. connectivity + clusterhead-time based). Results show performance trade-offs. • Impact of clustering gap on unserviced index is studied using simulations. Results show that larger clustering gap results in larger unserviced index.

  43. SUMMARY

  44. Summary • Defined unified clustering mechanism using generic availability factor. • Clusterhead-time based mechanism helps improve the clusterhead load distribution. • Mathematical/matrix formulation of clustering algorithm. • Application of Non-linear Control Systems Stability theory shows that the unified clustering mechanism is stable. • Defined clustering performance measures to be used with unified clustering mechanism. • Unified clustering mechanism is useful in comparing various clustering mechanisms. It also makes it easy to introduce a new clustering (based on some new parameter) in future. • Change in the availability factor threshold affects the Clusterhead Stability and Clusterhead Load Distribution.

  45. FUTURE ENHANCEMENTS

  46. 4 6 3 2 1 5 Clustering PerformanceWorst Case Analysis – GranularityMaximum degree = 2 Clusterhead Granularity is ½ if N is even, (N+1)/2N if N is odd.

  47. 8 9 3 7 1 4 10 2 6 5 Clustering PerformanceWorst Case Analysis – GranularityMaximum degree = 3

  48. Level Nodes per level Total nodes Number of clusterheads Clusterhead granularity 1 1 1 1 1 2 3 4 1 1/4=0.25 3 6 10 7 7/10=0.7 4 9 19 7 7/19=0.368 5 12 31 19 19/31=0.613 6 15 46 19 19/46=0.413 7 18 64 37 37/64=0.578 Clustering PerformanceWorst Case Analysis – GranularityMaximum degree = 3

  49. 10 8 9 4 3 1 11 7 5 2 6 12 13 Clustering PerformanceWorst Case Analysis – GranularityMaximum degree = 4

  50. Level Nodes per level Total nodes Number of clusterheads Clusterhead granularity 1 1 1 1 1 2 4 5 1 1/5=0.2 3 8 13 9 9/13=0.692 4 12 25 9 9/25=0.36 5 16 41 25 25/41=0.61 6 20 61 25 25/61=0.41 7 24 85 49 49/85=0.576 Clustering PerformanceWorst Case Analysis – GranularityMaximum degree = 4

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