The Research of Clustering Algorithms for
Highly Mobile Ad Hoc Networks
2
3
IBM: Smarter Planet
“The world isn’t just getting smaller and atter, it is actually becoming smarter.
Today, almost anythi...
Banking          Buildings      Cities

              Cloud
                               Education      Energy
         ...
6
Television
                          Network

Wireless Sensor                        Vehicle Comm.
   Network !           ...
(Mobile Ad Hoc Network,
MANET)




         8
9
10
11
2.1
                                                               TI,
                                                   ...
2.1

 • UCLA, M. Gerla,
 • Cornell, Z. Hass,
 • UIUC, N. Vaidaya,
 • Maryland, S. Tripathi,
 • UCSB, E. Belding-Royer,
 • ...
2.1
 D.Baker and A. Ephremides, “The architectural organization of a mobile radio network via a
distributed algorithm,” IE...
2.1
 M. Gerla and J.T.C. Tsai, “Multicluster, mobile, multimedia radio network,” ACM Journal on
 Wireless Networks, vol. 1...
2.1




      16
2.2
 J. Yu and P. Chong, “A Survey of Clustering Schemes for Mobile Ad Hoc Networks,” IEEE
 Communications Surveys & Tutor...
a.
          (Unweighted Graph) G
S    G                      S            S
      S     (Dominating Set)                D...
b.




     19
b.




     19
b.




     20
b.




     20
c.



          F


     Th

          E
                   N




              21
c.



          F


     Th            N
          E
                       Y




              22
d.

 No. of Cluster Members ∈ [Opt. Lower Bound, Opt. Upper Bound]



                          ✓                         ...
e.


     ∑
     i
         Parameter i x Weighting Factor i
                    W1 W2 W3 W4




                      24
Dominating Set - Based         Energy Efficient      Combined-metrics-based
               Mobility - Aware              L...
2.3




      26
2.3


       Threshold 1
                               E
    Weighting Factor 2
 Opt. Upper Bound
                       ...
S. Bouk, and I. Sasase, “Energy E cient and Stable Weight Based Clustering for mobile ad
hoc networks,” in Proc. Signal Pr...
2.3




      29
2.3




      30
M. Chatterjee, S. K. Das, and D. Turgut, “An On-Demand Weighted Clustering Algorithm
(WCA) for Ad hoc Networks,” in Proc. ...
2.3

           for Others
           for Speci c Functions

           for Re-Transmission

           for Mobility

    ...
“A lack of realism regarding of the scenario in which MANET will be
applied coupled with a lack of realism during the desi...
35
3.1




      36
3.2




      Wi   Σ
           j
                . ωj



           37
Wi   .   ω   1
                 .   ω   2
                                  .   ω   3
                                    ...
Wi v ω
    .    1
             .   ω   2
                              .   ω   3
                                         ...
Doppler Shift - based Relative Speed Estimation Algorithm

                                                  D
           ...
N    3, 5, 7
θ1 = 15˚, 30˚, 45˚, 60˚, 75˚
n    2.5, 3.0, 3.5
v    5, 10, 15, 20, 25 m/s

3×5×3×5                  225
    ...
Analyzation of Estimation Error in θ1 = 45˚ , N = 5
        v (m/s)     5      10       15       20        25
         n =...
Wi v ω E ω
    .   1
            .   2
                         .   ω   3
                                      .   ω   4
...
Fixed Data Generation Model




Dynamic Data Generation Model


             44
Wi v ω E ω δ ω
    .   1
            .   2
                         .   3
                                   .   ω   4
   ...
Wi v ω E ω δ ω d ω
    .   1
                   .      2
                                          .        3
            ...
Wi v ω E ω δ ω d ω
                .    1
                         .       2
                                             ...
3.2




      48
3.2



      Stochastic Geometry
      Point Process Theory
           Random Graph
 The Probabilistic Method




        ...
3.2



                               Active
                                            (Almost All)
      Active        ...
3.2

                   k-hop




      1-hop                3-hop

              51
52
MatLab
                           Scenarios
            Function
      GUI




                          Algorithms
      ...
4.1

         J.G. Proakis,
       A. Goldsmith,
      T.S. Rappaport,
           S. Haykin,
         J.G. Proakis,


    ...
4.1


      M. Barbeau    Principles of Ad Hoc Networking
       S. Basagni   Mobile Ad Hoc Networking
  A. Boukerche     ...
4.1
 1. M. Ni, H. Wu, B. Ai and Z. Zhong, “Composite Recon gurable Multi-Clustering Ad Hoc Network”,
                     ...
4.2

  J.A. Gubner, Probability and Random Process for ECE
  P.V. Mieghem, Performance Analysis of Communications Network ...
Stage 1

1    2       3   4   5        6   7   8   9   10   11   12


                          2010




         




   ...
Stage 2

1   2   3   4   5         6   7    8   9   10   11   12


                     2010




                    58
Stage 3

1   2   3   4   5        6   7   8   9     10   11   12


                     2010




                    58
Stage 4

12   1      2      3   4   5    6     7   8   9   10   11   12


                               2011




        ...
Stage 5

12   1   2   3   4   5      6      7   8   9   10   11   12


                         2011




                 ...
Stage 6

12   1   2   3   4   5    6     7   8   9     10   11   12


                         2011




                  ...
2009.12
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clustering Algorithms for Mobile Ad Hoc Networking (Slides for my opening defense)

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clustering Algorithms for Mobile Ad Hoc Networking (Slides for my opening defense)

  1. 1. The Research of Clustering Algorithms for Highly Mobile Ad Hoc Networks
  2. 2. 2
  3. 3. 3
  4. 4. IBM: Smarter Planet “The world isn’t just getting smaller and atter, it is actually becoming smarter. Today, almost anything—any physical object, process or system—can be instrumented, inter- connected and infused with intelligence.” - IBM, ”Let's build a smarter planet” 4
  5. 5. Banking Buildings Cities Cloud Education Energy Computing Food Government Healthcare Smarter ... Infrastructure Intelligence Oil Public Products Rail Safety Retail Stimulus Telecom Tra c Water Work 5
  6. 6. 6
  7. 7. Television Network Wireless Sensor Vehicle Comm. Network ! Network ! an ized an ized -org -org Self Self Internet Ubiquitous Telephone Networks Network Wireless Mesh Special-Purpose Network d ! Network ! a nize an ized g g Sel f-or Sel f-or Electricity Network 7
  8. 8. (Mobile Ad Hoc Network, MANET) 8
  9. 9. 9
  10. 10. 10
  11. 11. 11
  12. 12. 2.1 TI, DARPA, 1997 GloMo, DARPA, 1994 SURAN, DARPA, 1983 PRNet, DARPA, 1973 1995 1990 1 985 1980 MANET WorkGroup IETF, 1997.6 1 975 1 970 IEEE 802.11 WorkGroup “Ad Hoc”, 1991 12
  13. 13. 2.1 • UCLA, M. Gerla, • Cornell, Z. Hass, • UIUC, N. Vaidaya, • Maryland, S. Tripathi, • UCSB, E. Belding-Royer, • UCSC, J. Garcia, 13
  14. 14. 2.1 D.Baker and A. Ephremides, “The architectural organization of a mobile radio network via a distributed algorithm,” IEEE Trans. on Communications, vol. 29, no. 11, pp. 1694-1701, 1981. 14
  15. 15. 2.1 M. Gerla and J.T.C. Tsai, “Multicluster, mobile, multimedia radio network,” ACM Journal on Wireless Networks, vol. 1, no. 3, pp. 255-265, 1995. IEEE Xplore: “Ad Hoc” & “Clustering” (in Journal & Top Conferences) 30 23 15 8 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 15
  16. 16. 2.1 16
  17. 17. 2.2 J. Yu and P. Chong, “A Survey of Clustering Schemes for Mobile Ad Hoc Networks,” IEEE Communications Surveys & Tutorials, vol. 7, no. 1, pp. 32-48, 2004. (Dominating Set) (Mobility-aware) (Energy E cient) (Load Balancing) (Combined-metrics-based) 17
  18. 18. a. (Unweighted Graph) G S G S S S (Dominating Set) DS (Connected Dominating Set) 18
  19. 19. b. 19
  20. 20. b. 19
  21. 21. b. 20
  22. 22. b. 20
  23. 23. c. F Th E N 21
  24. 24. c. F Th N E Y 22
  25. 25. d. No. of Cluster Members ∈ [Opt. Lower Bound, Opt. Upper Bound] ✓ ! 23
  26. 26. e. ∑ i Parameter i x Weighting Factor i W1 W2 W3 W4 24
  27. 27. Dominating Set - Based Energy Efficient Combined-metrics-based Mobility - Aware Load Balancing Newly Proposed Algorithms 25
  28. 28. 2.3 26
  29. 29. 2.3 Threshold 1 E Weighting Factor 2 Opt. Upper Bound A B Scenario 1 ✓ Opt. Lower Bound C Scenario 2 ! D Weighting Factor 1 27
  30. 30. S. Bouk, and I. Sasase, “Energy E cient and Stable Weight Based Clustering for mobile ad hoc networks,” in Proc. Signal Processing and Communication Systems 2008, pp. 1-10. WCA EECA EECA EECA 600 1200 1800 2400 3000 3600 28
  31. 31. 2.3 29
  32. 32. 2.3 30
  33. 33. M. Chatterjee, S. K. Das, and D. Turgut, “An On-Demand Weighted Clustering Algorithm (WCA) for Ad hoc Networks,” in Proc. IEEE Globecom 2000, pp. 1697–701. WCA WCA DWCA DWCA 32
  34. 34. 2.3 for Others for Speci c Functions for Re-Transmission for Mobility for Basic Operations 33
  35. 35. “A lack of realism regarding of the scenario in which MANET will be applied coupled with a lack of realism during the design of MANET are the main causes of MANET running a high risk of failure.” M. Conti and S. Giordano, “Multihop Ad Hoc Networking: The Theory”, Communications Magazine, IEEE (2007) vol. 45 (4) pp. 78 - 86 34
  36. 36. 35
  37. 37. 3.1 36
  38. 38. 3.2 Wi Σ j . ωj 37
  39. 39. Wi . ω 1 . ω 2 . ω 3 . ω 4 ... 38
  40. 40. Wi v ω . 1 . ω 2 . ω 3 . ω 4 ... v 39
  41. 41. Doppler Shift - based Relative Speed Estimation Algorithm D fA !" !" A A fA E θ2 E D C fdC θ2 θ 1 t∆ PC θ1 v C m m t∆ fdC B fdB B PC fdB PB PB v Approaching Scenario Receding Scenario fdB · c P∆ P∆ P∆ v= · 2· 4 + − fA PB K PC K PB K 40
  42. 42. N 3, 5, 7 θ1 = 15˚, 30˚, 45˚, 60˚, 75˚ n 2.5, 3.0, 3.5 v 5, 10, 15, 20, 25 m/s 3×5×3×5 225 × 100 22500 41
  43. 43. Analyzation of Estimation Error in θ1 = 45˚ , N = 5 v (m/s) 5 10 15 20 25 n = 2.5 0.435 0.926 1.931 2.702 3.645 e n = 3.0 1.456 2.039 3.325 4.155 4.747 v n = 3.5 1.333 2.298 4.747 5.890 6.429 n = 2.5 2.347 4.521 5.904 7.218 8.120 σe n = 3.0 2.842 5.863 8.472 9.433 10.687 n = 3.5 5.506 5.159 6.736 11.433 12.723 42
  44. 44. Wi v ω E ω . 1 . 2 . ω 3 . ω 4 ... 70% 30% 43
  45. 45. Fixed Data Generation Model Dynamic Data Generation Model 44
  46. 46. Wi v ω E ω δ ω . 1 . 2 . 3 . ω 4 ... [a, b] 45
  47. 47. Wi v ω E ω δ ω d ω . 1 . 2 . 3 . 4 ... # # ! ! # # 46
  48. 48. Wi v ω E ω δ ω d ω . 1 . 2 . 3 . 4 ... Scenario 1 ω2 ω4 ω1 ω3 Scenario 2 Gray Theory ω1ω2ω3ω4 based Algorithm Scenario 3 ω1ω2ω3ω4 47
  49. 49. 3.2 48
  50. 50. 3.2 Stochastic Geometry Point Process Theory Random Graph The Probabilistic Method 49
  51. 51. 3.2 Active (Almost All) Active Clustering Routing Passive Hybrid (Proposed) Clustering Passive Routing Hybrid Routing (?) Clustering 50
  52. 52. 3.2 k-hop 1-hop 3-hop 51
  53. 53. 52
  54. 54. MatLab Scenarios Function GUI Algorithms Nodes 53 4.1
  55. 55. 4.1 J.G. Proakis, A. Goldsmith, T.S. Rappaport, S. Haykin, J.G. Proakis, B.A. Forouzan, 54
  56. 56. 4.1 M. Barbeau Principles of Ad Hoc Networking S. Basagni Mobile Ad Hoc Networking A. Boukerche Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks L. Gavrilovska Ad Hoc Networking Towards Seamless Communications R. Hekmat Ad-hoc Networks: Fundamental Properties and Network Topologies P. Santi Topology Control in Wireless Ad Hoc and Sensor Networks 55
  57. 57. 4.1 1. M. Ni, H. Wu, B. Ai and Z. Zhong, “Composite Recon gurable Multi-Clustering Ad Hoc Network”, , Vol. 33, No.2, 2009, p 94-97. 2. M. Ni, Z. Zhong and H. Wu, “A Novel Energy E cient Clustering Algorithm for Dynamic Wireless Sensor Network”, to appear in Journal of Internet Technology, No.4, 2009. 1. M. Ni, Z. Zhong, H. Wu and D. Zhao, “An Energy E cient Clustering Scheme for Mobile Ad Hoc Networks”, Submitted to IEEE VTC ‘2010 Spring. 2. M. Ni, Z. Zhong, H. Wu and D. Zhao, “A New Stable Clustering Scheme for Highly Mobile Ad Hoc Networks”, Accepted for IEEE WCNC 2010. 3. X. Qiao, M. Ni and Z. Tan, “A Directional Antennas-Based Topology Control Algorithm for Two- tiered Wireless Sensor Network”, IEEE WiCOM 2009. 4. M. Ni, Z. Zhong and R. Xu, “An Energy E cient Routing Scheme for Wireless Sensor Network in Heavy Haul Railway Transportation”, International Conference of International Heavy Haul Association (IHHA) 2009. 56
  58. 58. 4.2 J.A. Gubner, Probability and Random Process for ECE P.V. Mieghem, Performance Analysis of Communications Network and System A. Baddeley, Spatial Point Process and their Applications IEEE Wireless Comm., IEEE Comm., IEEE Network, IEEE Trans. on Networking, ACM MobiCom, ACM MobiHoc, IEEE InfoCom, IEEE GlobeCom, IEEE ICC 8-10 57
  59. 59. Stage 1 1 2 3 4 5 6 7 8 9 10 11 12 2010 58
  60. 60. Stage 2 1 2 3 4 5 6 7 8 9 10 11 12 2010 58
  61. 61. Stage 3 1 2 3 4 5 6 7 8 9 10 11 12 2010 58
  62. 62. Stage 4 12 1 2 3 4 5 6 7 8 9 10 11 12 2011 59
  63. 63. Stage 5 12 1 2 3 4 5 6 7 8 9 10 11 12 2011 59
  64. 64. Stage 6 12 1 2 3 4 5 6 7 8 9 10 11 12 2011 59
  65. 65. 2009.12

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