The document summarizes research on clustering algorithms for highly mobile ad hoc networks. It discusses key early research projects from the 1970s through 1990s that helped establish the field. It then reviews different categories of clustering algorithms, including dominating set-based, mobility-aware, energy efficient, and combined metrics-based approaches. Various algorithms within each category are described and compared. The document concludes by noting that a lack of realism in modeling MANET scenarios and system designs poses risks to their practical application.

1. The Research of Clustering Algorithms for
Highly Mobile Ad Hoc Networks

2. 2

3. 3

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. 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

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. (Mobile Ad Hoc Network,
MANET)
8

9. 9

10. 10

11. 11

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. 2.1
• UCLA, M. Gerla,
• Cornell, Z. Hass,
• UIUC, N. Vaidaya,
• Maryland, S. Tripathi,
• UCSB, E. Belding-Royer,
• UCSC, J. Garcia,
13

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. 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. 2.1
16

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. a.
(Unweighted Graph) G
S G S S
S (Dominating Set) DS
(Connected Dominating Set)
18

19. b.
19

20. b.
19

21. b.
20

22. b.
20

23. c.
F
Th
E
N
21

24. c.
F
Th N
E
Y
22

25. d.
No. of Cluster Members ∈ [Opt. Lower Bound, Opt. Upper Bound]
✓ !
23

26. e.
∑
i
Parameter i x Weighting Factor i
W1 W2 W3 W4
24

27. Dominating Set - Based Energy Efficient Combined-metrics-based
Mobility - Aware Load Balancing
Newly Proposed Algorithms
25

28. 2.3
26

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. 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. 2.3
29

32. 2.3
30

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. 2.3
for Others
for Speci c Functions
for Re-Transmission
for Mobility
for Basic Operations
33

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. 35

37. 3.1
36

38. 3.2
Wi Σ
j
. ωj
37

39. Wi . ω 1
. ω 2
. ω 3
. ω 4
...
38

40. Wi v ω
. 1
. ω 2
. ω 3
. ω 4
...
v
39

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. 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. 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. Wi v ω E ω
. 1
. 2
. ω 3
. ω 4
...
70%
30%
43

45. Fixed Data Generation Model
Dynamic Data Generation Model
44

46. Wi v ω E ω δ ω
. 1
. 2
. 3
. ω 4
...
[a, b]
45

47. Wi v ω E ω δ ω d ω
. 1
. 2
. 3
. 4
...
#
#
!
!
# #
46

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. 3.2
48

50. 3.2
Stochastic Geometry
Point Process Theory
Random Graph
The Probabilistic Method
49

51. 3.2
Active
(Almost All)
Active Clustering
Routing Passive
Hybrid (Proposed)
Clustering
Passive Routing
Hybrid
Routing (?)
Clustering
50

52. 3.2
k-hop
1-hop 3-hop
51

53. 52

54. MatLab
Scenarios
Function
GUI
Algorithms
Nodes
53
4.1

55. 4.1
J.G. Proakis,
A. Goldsmith,
T.S. Rappaport,
S. Haykin,
J.G. Proakis,
B.A. Forouzan,
54

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. 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. 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. Stage 1
1 2 3 4 5 6 7 8 9 10 11 12
2010
58

60. Stage 2
1 2 3 4 5 6 7 8 9 10 11 12
2010
58

61. Stage 3
1 2 3 4 5 6 7 8 9 10 11 12
2010
58

62. Stage 4
12 1 2 3 4 5 6 7 8 9 10 11 12
2011
59

63. Stage 5
12 1 2 3 4 5 6 7 8 9 10 11 12
2011
59

64. Stage 6
12 1 2 3 4 5 6 7 8 9 10 11 12
2011
59

65. 2009.12