1 HEINZ NIXDORF INSTITUTE University of Paderborn Algorithms and Complexity Christian Schindelhauer...
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Transcript of 1 HEINZ NIXDORF INSTITUTE University of Paderborn Algorithms and Complexity Christian Schindelhauer...
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HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Algorithms for Radio NetworksWinter Term 2005/2006
25 Jan 200613th Lecture
Stefan Rührup
Algorithms for Radio Networks 2
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Modeling Worst Case Mobility
• Problem:
– Mobile users
– positions are known at the moment (t=0), but not in the future (t=∆).
– How to adjust the transmission range?
• Reasonable restriction for the worst case
– velocity bound pedestrian model
– acceleration bound vehicular model
transmission range
Algorithms for Radio Networks 3
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Modeling Worst Case Mobility
V: Pedestrian Model ↔ Maximum velocity ≤ vmax
A: Vehicular Model ↔ Maximum accelaration ≤ amax
Algorithms for Radio Networks 4
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Modeling Worst Case Mobility
V: Pedestrian Model ↔ Maximum velocity ≤ vmax
A: Vehicular Model ↔ Maximum acceleration ≤ amax
Algorithms for Radio Networks 5
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
The Mobile Ad Hoc Network
• Basic idea: Maintain the network for a time interval Δ
• As a start: synchronous round model
• In every round of duration Δ
– Determine positions (speed vectors) of possible
communication partners
– Establish (stable) communication links
– Update routing information
– Do the job, i.e. packet delivery, video streams, telephone,…
Algorithms for Radio Networks 6
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
The Mobility Model
• Velocity bounded mobility model Pedestrians move with a (known) bounded velocityFor all i{1,…,n}:
• Acceleration bounded mobility model Cars move with a (known) bounded accelerationFor all i{1,…,n}:
• Technical assumptions:– polynomial distances and speeds– complete knowledge of position
Algorithms for Radio Networks 7
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Taming Mobility in the Link Layer
• General Concept
– Increase transmission distance
to guarantee a certain life span Δ for each link
– Apply the mobility model (i.e. amax or vmax is known)
transmission range
start position (t0)
end position (t0+Δ)
Algorithms for Radio Networks 8
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
vx
x
Example
• Trains moving in opposite directions
• Transmission range only sufficient in the static case
• If we take the velocity into account:
• We need a modified distance measure
Algorithms for Radio Networks 9
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Given the positions u,w and the velocity bound vmax
• Maximum distance after the time interval ∆:
Velocity bounded (pedestrian) model
uncertainty
u w
Algorithms for Radio Networks 10
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Transmission range for the velocity bounded (pedestrian) model:
end
Walking range
start startend
Sa-tisfies
triangleinequality
Pedestrian Model
transmission rangefor node u and t=0
Algorithms for Radio Networks 11
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Acceleration bound amax
• Positions u,v and speed vectors u’,v’ known
• Maximum distance after time interval ∆:
• With given distances andwe can approximate by a constant factor.
Vehicular Model
uncertainty due to acceleration
u w
velocity
Algorithms for Radio Networks 12
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
end
start start
end
Sa-tisfies
triangleinequality
Vehicular Model
• Transmission range for the acceleration bounded (vehicular) model:
Algorithms for Radio Networks 13
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Communication links (edges) can interfere
– edges interfere, if one node is located within the transmission radius of a node of the other edge
• Velocity bounded model:
interference, if
• Acceleration bounded model:
interference, if and
interference
pq e
e’
Interferences
Algorithms for Radio Networks 14
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• An edge g interferes with edge e in the
1. pedestrian (v) model
2. vehicular (a) model
No interference Interference
gg
e e e e
g
e
g
p
q
qp
Interferences more formally
Algorithms for Radio Networks 15
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Interference number of a network
• What is the maximum number of interfering edges in a network?
• Interference number is influenced by the transmission rangeand the positions of the nodes
• How many nodes can meet in one place and form a crowd?
Definition of crowdedness
• Crowdedness gives a lower bound for interferences
• In both mobility models we observe for all connected graphs G(V,E): Int(G) crowd(S) - 1
Algorithms for Radio Networks 16
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Crowdedness of node set – natural lower bound on network parameters (like diversity)
• Pedestrian (v) model:– Maximum number of nodes that can collide with a given node
in time span [0,Δ]
• Vehicular (a) model:– Maximum number of nodes that may move to node u meeting
it with zero relative speed in time span [0,Δ]
• crowd(S) := maxuS crowd(u)
Crowdedness more formally
Algorithms for Radio Networks 17
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Transmission ranges defined for two mobility models
• Basis for
– the definition of interferences
– construction of a network topology
– analyisis: interference number, congestion
Mobility Models
Algorithms for Radio Networks 18
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
The Hierarchical Grid Graph(pedestrian model)
• Start with grid of box size Δ vmax
• For O(log n) rounds do– Determine a cluster head per
box– Build up star-connections
from all nodes to their cluster heads
– Erase all non cluster heads– Connect neighbored cluster
heads– Increase box size by factor 2
od
Algorithms for Radio Networks 19
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Interference number of the network G: Int(G) := maxeE(G) |Int(e)|
• Load and path system
– Set of all message paths defines path system P
– l(e) := # number of packets sent over edge e according to P
• Congestion of an edge (with respect to a path system P )
– Congestion of a network G:
• Mobile spanner
– A network is a mobile spanner, if for all u,w exists a path
connecting u and v with bounded length
Load, Congestion and Spanners
Algorithms for Radio Networks 20
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• A graph G is called a mobile spanner, if for all nodes there is a path in G with
• Congestion of a path system:
• A mobile spanner G approximates an optimal path system
Mobile Spanner
Algorithms for Radio Networks 21
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Results
Lemma
In both mobility models α{v,a} every mobile spanner is also a mobile power spanner, i.e. for some ß≥1 for all u,w S there exists a path (u=p0,p1,…,pk=w) in G such that:
Algorithms for Radio Networks 22
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Results
Theorem
Given a mobile spanner G for any of our mobility models then
– for every path system P in a complete network C
– there exists a path system P‘ in G such that
Theorem
The Hierarchical Grid Graph constitutes a mobile spanner with at most O(crowd(V) + log n) interferences (for both mobility models).
The Hierarchical Grid Graph can be built up in O(crowd(V) + log n) parallel steps using radio communication
Algorithms for Radio Networks 23
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
The Hierarchical Grid Graph(vehicular model)
• Algorithm:
– Consider coordinates (x(si),y(si),x(s‘i),y(s‘i))
– Start with four-dimensional grid with rectangular boxes of size (6Δ²amax, 6Δ²amax,2Δvmax,2Δvmax)
– Use the same algorithm as before
x
vx t=0
x
vx t=Δ
x
vx t=2Δ
Algorithms for Radio Networks 24
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Stable Basic Networks under Worst Case Mobility
CorollaryThere exist distributed algorithms that construct a mobile network G for velocity bounded and acceleration bounded model with the following properties:
1. G allows routing approximating the optimal congestion by O(log² n)
2. Energy-optimal routing can be approximated by a factor of O(1)
3. G approximates the minimal interference number by O(log n)4. The degree is O(crowd(S)+ log n)5. The diameter is O(log n)
• Still no routing can satisfy small congestion and energy at the same time!
Algorithms for Radio Networks 25
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Applications of mobility models
• Various protocols for mobile networks
• more or less influenced by mobility (esp. routing)
• How to evaluate and compare protocols?
– simulation of mobile networks
– mobility models as benchmarks
• traces or synthetic models?
Algorithms for Radio Networks 26
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Requirements
• imitation of realistic motion
• mobility model should not be too complex
⇒ simplifications (depends on the network model)
e.g., cellular network:
– random walk on network cells
– speed and direction vectors can be neglected
• many application-specific mobility models
Algorithms for Radio Networks 27
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Parameters
• Bounds on velocity, acceleration or the change of direction
• Is the motion independent of other mobile hosts?
– entity mobility models
– group mobility models
• Degree of randomness
– sharp turns possible or smoothed motion?
• Granularity
– macroscopic view (→ cellular networks)
– microscopic view (→ ad hoc networks)
Algorithms for Radio Networks 28
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• Brownian Motion (microscopic view)
– speed an direction are chosen randomly in each time step (uniformly from [vmin,vmax] and [0,])
• Random Walk
– macroscopic view
– memoryless
– e.g., for cellular networks
– movement from cell to cell
– choose the next cell randomly
– residual probability
[Camp et al. 2002]
Brownian Motion, Random Walk
Algorithms for Radio Networks 29
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
[Camp et al. 2002]
[Johnson, Maltz 1996]Random Waypoint Mobility Model
• move directly to a randomly chosen destination
• choose speed uniformly from [vmin,vmax]
• stay at the destination for a predefined pause time
Algorithms for Radio Networks 30
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
• adjustable degree of randomness
• velocity:
• direction:
Gauss-Markov Mobility Model[Liang, Haas 1999]
mean random variablegaussian distribution
tuning factor
[Camp et al. 2002]
α=0.75
Algorithms for Radio Networks 31
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Concept map of mobility models[Bettstetter 2001]
32
HEINZ NIXDORF INSTITUTEUniversity of Paderborn
Algorithms and ComplexityChristian Schindelhauer
Thanks for your attention!End of 13th lectureNext lecture: We 1 Feb 2006, 4pm, F1.110Next exercise class: Th 26 Jan 2006, 1.15 pm, F2.211 or Tu 31 Jan 2006, 1.15 pm, F1.110Next mini exam Mo 13 Feb 2006, 2pm, FU.511