Method for predicting the mobility in mobile ad hoc networks

• US 20090046678A1
• Filed: 08/17/2007
• Published: 02/19/2009
• Est. Priority Date: 08/17/2007
• Status: Abandoned Application

• Current Assignee: Industry-Academic Cooperation Foundation Yonsei University
• Original Assignee: Industry-Academic Cooperation Foundation Yonsei University
• Inventors: Xu, Hui, Lee, Young-Koo, Lee, Sungyoung
• Application Number:
  US11/892,031
• Publication Number:
  US 20090046678A1
• Time in Patent Office: Days
• US Class Current: 370/338
• CPC Class Codes: H04W 40/18 based on predicted events
• CPC Class Codes: H04W 40/20 based on geographic position or location
• CPC Class Codes: H04W 40/24 Connectivity information management, e.g. connectivity discovery or connectivity update
• CPC Class Codes: H04W 8/005 Discovery of network devices, e.g. terminals

Abstract

Disclosed are methods for determining the neighborhood local view of a mobile node in time which can facilitate the forwarding decision in the design of network protocols. In conventional mobile ad hoc networks nodes set up local topology view based on periodical received “Hello” messages. The conventional method is replaced with proactive and adaptive methods of predicting locations of nodes based on preserved historical information extracted from received “Hello” messages and constructing neighborhood view by aggregating predicted locations. This method is useful for providing updated and consistent topology local view that a network communication employs to determine optimal forward decisions and improve communication performance.

Claims

1. Method for predicting the mobility in mobile ad hoc networks, the method comprising steps of:

constructing neighborhood local view of a node;

predicting locations of said node and its neighbor nodes at the same future time using said neighborhood local view in prescribed time interval;

updating neighborhood local view by aggregating neighbors'"'"' predicted location;

reconstructing said neighborhood local view by setting smaller neighborhood range.

2. The method as claimed in claim 1, wherein the prescribed time interval is time period within which any neighbor node stays in the transmission range of said node, T_dwell is given by

$E$

[ T dwell ] = π





A E 

[ V ] 

L

where E[T_dwell] is average value of the T_dwell, A is the area of the transmission range, L is the perimeter of this area, E[V] is average value of V and V is relative velocity vector of node.

3. The method as claimed in claim 2, wherein average value of V, E[V] is given by

$E$

[ V ] = 1 π

2 

∫

V min V max 

∫

V min V max 

( 

v 1 + v 2 ) 

F e ( 2 

v 1 

v 2 v 1 + v 2 ) 

f V 

( v 1 ) 

f V 

( v 2 ) 



v 1 





v 2

where

$F_e<br><br>\left(k\right)={\int <br><br>}_0^1<br><br>\sqrt{\frac{1-k^2<br><br>t^2}{1-t^2}}<br><br><br><br><br><br>t$

is complete elliptic integral of the second kind, fv(v1), fv(v2) is the joint pdf of the random variables V1, V2.

4. The method as claimed in claim 1, wherein in said step of predicting locations of said node and its neighbor nodes, each location (x_p, y_p, z_p) at a future time t_p is calculated as

$\{\begin{array}{c}{x}_p=x_{1}\end{array}$

h + x 1 

h - x 2 

h t 1 

h - t 2 

h 

( t p - t 1 

h ) y p = y 1 

h + y 1 

h - y 2 

h t 1 

h - t 2 

h 

( t p - t 1 

h ) z p = z 1 

h + z 1 

h - z 2 

h t 1 

h - t 2 

h 

( t p - t 1 

h )  

where (x_1h, y_1h, z_1h) is a location at a time t_1h, (x_2h, y_2h, z_2h) is a location at a time t_2h, and t_1h>

t_2h.

5. The method as claimed in claim 1, wherein in said step of predicting locations of said node and its neighbor nodes, each location (x_p, y_p, z_p) at a future time t_p is calculated as

$\{\begin{array}{c}{x}_p=x_{1}\end{array}$

h + v x ′



( t p - t 1 

h ) y p = y 1 

h + v y ′



( t p - t 1 

h ) z p = z 1 

h + v z ′



( t p - t 1 

h )  

where (x_1h, y_1h, z_1h) is a location at a time t_1h, (v′

_x, v′

_y, v′

_z) is a velocity of latest update for a particular node.

6. The method as claimed in claim 1, wherein in said step of predicting locations of said node and its neighbor nodes, each location (x_p, y_p, z_p) at a future time t_p is calculated as

$\{\begin{array}{c}{x}_p=x_{1}\end{array}$

h + 2 

v x ′

+ ( v x ′

- v x ″

) 

t p - t 1 

h t 1 

h - t 2 

h 2 

( t p - t 1 

h ) y p = y 1 

h + 2 

v y ′

+ ( v y ′

- v y ″

) 

t p - t 1 

h t 1 

h - t 2 

h 2 

( t p - t 1 

h ) z p = z 1 

h + 2 

v z ′

+ ( v z ′

- v z ″

) 

t p - t 1 

h t 1 

h - t 2 

h 2 

( t p - t 1 

h )  

where (x_1h, y_1h, z_1h) is a location at a time t_1h, (v′

_x, v′

_y, v′

_z) is the velocity of first update for a particular node, and (v−

_x, v″

_y, v″

_z) is the velocity of second update for a particular node.

7. The method as claimed in claim 1, wherein said smaller neighborhood range SR is given by

E[SR]=(1−

p)R₁

where p is probability that any node moves into the transmission range of node S, R₁ is radius of node S.

8. The method as claimed in claim 7, wherein

$R$

( V →

2 , V →

1 , a , b ) = { 1 

;

a ≤



V →

2 - V →

1 

≤

b 0 

;

otherwise .

where v is a relative speed, {right arrow over (V)} is the random relative velocity vector proposed in previous section and s is the maximum speed for any node.

9. The method as claimed in claim 8, wherein

$\begin{array}{c}{f}_{}\end{array}$

V →





( t ) ≈



F 

V →





( δ

t ) - F 

V →





( t ) δ

t = 

P 

( t ≤



V →



≤

t + δ

t ) δ

t = 

∮

( 0 , 0 ) ( 2 

π

, s ) 

∮

( 0 , 0 ) ( 2 

π

, s ) 

R 

( V →

2 , V →

1 , t , t + δ

t ) ( 2 

π





s ) 2 

δ

t ·



V →

2 



V →

1 ,

where f_□

{right arrow over (V)}<sub>□

(t)</sub> is the distribution function, δ

_t is a small positive value, and

$p={\int <br><br>}_0^{2<br><br>s}<br><br>f_{<br><br>\stackrel{\to <br><br>}{V}<br><br>}<br><br>\left(v\right)<br><br>p<br><br>\left(\mathrm{fv}\right)<br><br><br><br>v$