clt for degrees of random directed geometric networks yilun shang department of mathematics,...
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CLT for Degrees of Random Directed Geometric Networks
Yilun Shang
Department of Mathematics, Shanghai Jiao Tong University May 18, 2008
Context
• Background and Motivation
• Model
• Central limit theorems
• Degree distributions
• Miscellaneous
(Static) sensor network
• Large-scale networks of simple sensors
Static sensor network
• Large-scale networks of simple sensors• Usually deployed randomlyUse broadcast paradigms to communicate
with other sensors
Static sensor network
• Large-scale networks of simple sensors• Usually deployed randomlyUse broadcast paradigms to communicate
with other sensors• Each sensor is autonomous
and adaptive to environment
Static sensor network
• Sensor nodes are densely deployed
Static sensor network
• Sensor nodes are densely deployed
• Cheap
Static sensor network
• Sensor nodes are densely deployed
• Cheap
• Small size
Communication
• Radio Frequency
omnidirectional antenna
directional antenna
Communication
• Radio Frequency omnidirectional antenna directional antenna• Optical laser beam need line of sight for communication
An illustration
Graph Models
Random (directed) geometric network
• Scatter n points on R2 (n large), X1,X2, …,Xn , i.i.d. with density function f
and distribution F
• Given a communication radius rn, two points are connected if they are at distance ≤rn.
Random geometric network
Random geometric network
r
Random geometric network
Random directed geometric network
• Fix angle ∈(0,2]. Xn={X1,..,Xn} i.i.d. points in R2, with density f ,distribution F. Let Yn={Y1,..,Yn} be a sequence of i.u.d. angles, let {rn} be a sequence tends to 0. G(Xn ,Yn ,rn) is a kind of random directed geometric network, where (Xi, Xj ) is an arc iff Xj in S i=S(Xi ,Yi ,rn ).
D.,Petit,Serna, IEEE Trans. Mobi. Comp. 2003
Random directed geometric network
Yi
S i
Xi
rn
Each sensor Xi covers a sector S i, defined by rn and with inclination Yi.
Random directed geometric network
• G( Xn ,Yn ,rn ) is a digraph
• If x5 is not in S1 , to communicate from x1 to x5:
Random directed geometric network
Notations and basic facts• For any fixed k N, define ∈ rn=rn(t) by nrn(t)2=t,
for t>0. Here, t is introduced to accommodate the areas of sectors.
• For A in R2, X is a finite point set in R2 and x R∈ 2, let X(A) be the number of points in X located in A,
and Xx=X {x}.∪ • For >0 , let H be the homogeneous Poisson
point process on R2 with intensity .• For k N and ∈ A is a subset of N, set
(k)=P[Poi()=k] and (A)=P[Poi() A].∈
Notations and basic facts
• Let Zn(t) be the number of vertices of out degrees at least k of G( Xn ,Yn ,rn ) , then
Zn(t)=∑ni=1 I{Xn(S(Xi,Yi,rn(t)))≥ k+1}
• Let Wn(t) be the number of vertices of in degrees at least k of G( Xn ,Yn ,rn ) , then
Wn(t)=∑ni=1 I{ # {Xj ∈ Xn|Xi∈ S(Xj,Yj,rn(t))}≥ k+1}
Central limit theorems
• Theorem
Central limit theorems
• Theorem
Suppose k is fixed. The finite dimensional distributions of the process
n- 1/2[Zn(t) - EZn(t)], t>0
converge to those of a centered Gaussian process (Z∞(t),t>0) with
E[Z∞(t)Z∞(u)]=∫R2 tf(x)/2([k, ∞))f(x)dx +
Central limit theorems
(1/4 2) ∫02 ∫0
2∫R2∫R2 g( z, f(x1), y1, y2 )
f 2(x1 )dz dx1 dy1 dy2 - h(t) h(u),
where g( z, , y1, y2 )=
P[{Hz(S(0,y1,t1/2)) ≥k}∩{H
0(S(z,y2 ,u1/2))≥k}] - P[H(S(0,y1,t1/2))≥ k] P[H(S(z,y2 ,u1/2)) ≥k ],
and h(t)= ∫R2{tf(x)/2(k - 1) tf(x)/2
+tf(x)/2([k, ∞))} f(x)dx.
Central limit theorems
Sketch of the proof
• Compute expectation
• Compute covariance
• Poisson CLT through a dependency graph argument
• Depoissionization
Central limit theorems
• Wn(t)
• k(n) tends to infinity
• Xn−→Pn , where Pn ={X1,..,XNn } is a Poisson process with intensity function n f(x).
Here, Nn is a Poisson variable with mean n.
Corresponding central limit theorems are obtained
Degree distributions
• For k N 0∈ ∪ , let p(k) be the probability of a typical vertex in G(Xn ,Yn ,rn) having out degree k
• Theorem
Degree distributions
• For k N 0∈ ∪ , let p(k) be the probability of a typical vertex in G(Xn ,Yn ,rn) having out degree k
• Theorem
p(k)=∫R2 tf(x)/2(k) f(x)dx ( * )
Degree distributions
• Example 1
f=I[0,1]2 uniform
Degree distributions
• Example 1
f=I[0,1]2 uniform
p(k)=exp( - t tk/k!
The out degree distribution is Poi(t)
Degree distributions
• Example 2
f(x1,x2)=(1/2exp( - (x12+x2
2)/2) normal
Degree distributions
• Example 2
f(x1,x2)=(1/2exp( - (x12+x2
2)/2) normal
p(k)=4t - exp( - t/4) ∑ki=0 (t/4i -
1/i!
a skew distribution
Degree distributions
Degree distributions
• If f is bounded, the degree distribution will never be power law because of fast
decay
Degree distributions
• If f is bounded, the degree distribution will never be power law because of fast
decay
• Given p(k)≥0, ∑∞k=0 p(k)=1, it’s very
hard to solve equation ( * ) for getting a f(x)
Miscellaneous
• High dimension
• Angles not uniformly at random
• Dynamic model
(Brownian, Random direction, Random waypoint, Voronoi, etc.)