Contact-Based Mobility Metrics for Delay-Tolerant Ad Hoc Networking
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Transcript of Contact-Based Mobility Metrics for Delay-Tolerant Ad Hoc Networking
Universität Stuttgart
Institute of Parallel and Distributed Systems (IPVS)
Universitätsstraße 38D-70569 Stuttgart
Contact-Based Mobility Metrics for Delay-Tolerant Ad Hoc Networking
A. Khelil, P.J. Marrón, K. Rothermel
MASCOTS, Sept 29 2005
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 2
Outline
• Motivation
• Related Work
• Contact-Based Mobility (CBM) Metrics
• Statistical and Theoretical Analysis for Random Waypoint
• Uses of CBM Metrics
• Conclusion
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 3
dest-2
Motivation
• Mobile ad hoc network (MANET)
• In MANETs mobility can be exploited
◦ to increase the capacity of the network *)
◦ to overcome network partitioning
• New class of protocols and applications
◦ Physical transport of messages (mobility-aided)
◦ Tolerate higher E2E transmission delays (delay-tolerant)
• Delay-tolerant protocols and appl. act on a large time-scale
Investigation of mobility on a large time-scale is crucial
*) M. Grossglauser et al. “Mobility Increases the Capacity of Ad Hoc Networks” Trans. on Netw., 2002.
src
dest-1
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 4
Related Work
• Existing mobility metrics
◦ Velocity-based: e.g. speed, relative speed
◦ Link-based: e.g. link change rate, link duration *)
◦ Route-based: e.g. route change rate, route duration **)
• Metrics defined for (non-delay-tolerant) ad hoc routing
• Metrics model mobility instantaneously and do not support detection of mobility patterns a large time-scale
**) N. Sadagopan et al. “Paths: Analysis of Path Duration Distributions in MANET and their Impact on Routing Protocols” Mobihoc, 2003.
*) J. Boleng et al. “Metrics to Enable Adaptive Protocols for Mobile Ad Hoc Networks” ICWN, 2002.
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 5
Outline
• Motivation
• Related Work
• Contact-Based Mobility (CBM) Metrics
◦ Methodology and Terminology
◦ Metrics Definition
• Statistical and Theoretical Analysis for Random Waypoint
• Uses of CBM Metrics
• Conclusion
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 6
Methodology and Terminology (1)
• Observation: Epidemiology uses contacts to model mobility of individuals
• We use “contacts” between nodes to quantify the mobility on a large time-scale
AB
• Assumption: Nodes are uniquely identified (e.g. MAC addr.)
• Definitions
◦ Encounter between nodes n and m occurs if distance(n,m) <= com. range
enm={n, m, tstart, duration}
◦ Contact:
cnm={enm} AB1st E
2nd E
3rd E
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Research Group
“Distributed Systems” 7
Methodology and Terminology (2)
• Node manages a contact table for the “time of interest T”
• Cn={cnm}:
set of contacts of node n in T
• En={enm}:
set of encounters of node n in T
5
13
9
14
6
con
tact
ee
-ID
0 10 20 30 t
{0, 20}
{7.5, 7.5} {22.5, 12.5}
{22.5, 7.5}
{25, 12.5}
{32.5, 5}
{10, 7.5}
time duration
time (sec)
Contact Table (Node 1)
contact
encounter
time of interest T
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 8
Def. of Contact-Based Mobility (CBM) Metrics
• Node-centric vs. network-wide
• Metrics
◦ Avg. Encounter Frequency = ( = 1.4)
◦ Encounter Rate = (= 7/40 encounters/s)
◦ Contact Rate = (= 5/40 contacts/s)
◦ Avg. Encounter Duration =
◦ Avg. Contact Duration =
T
Cn
T
En
n
Ee nm
C
durennm
.n
Ee nm
E
durennm
.
n
n
C
E
5
13
9
14
6
con
tact
ee
-ID
0 10 20 30 T=40s
{0, 20}
{7.5, 7.5} {22.5, 12.5}
{22.5, 7.5}
{25, 15}
{32.5, 7.5}
{10, 7.5}
time duration
time (sec)
Contact Table (Node 1)
1C1E = 5= 7
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 9
Outline
• Motivation
• Related Work
• Contact-Based Mobility (CBM) Metrics
• Statistical and Theoretical Analysis for Random Waypoint
• Uses of CBM Metrics
• Conclusion
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 10
Simulation Parameters
Area 1000m x 1000m
Number of nodes N
Communication range R = 100 m
Mobility Model Random waypoint
N ∈ [30,300]
Simulation time T = 1800 s
uniform in [0, Vmax ]Vmax ∈ [3,30] m/s
- pause uniform in [0,2] s
+
+
+
+
+
+
+
+
R
Area
+
+
- speed
+
Population closed
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IPVS
Research Group
“Distributed Systems” 11
Average Encounter Frequency
• AEF is independent from node density
• AEF increases with Vmax
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“Distributed Systems” 12
Average Encounter Rate | Average Contact Rate
• Linear increase with node density
• Non linear increase with Vmax
• Linear increase with node density
• linear increase with Vmax
AER / ACR ≈ AEF
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 13
Avg. Contact Duration | Avg. Encounter Duration
• Independent from node density
• Decreases with Vmax
• Independent from node density
• Decreases with Vmax
ACD / AED ≈ AEF
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IPVS
Research Group
“Distributed Systems” 14
Analytical Model for Random Waypoint
AA
avgSpeed * time
2R
areadensityencounters *#
= avgSpeed * time * 2R
= Vmax / 2RVdensity
time
encounterserRateavgEncount
**
#
max
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IPVS
Research Group
“Distributed Systems” 15
Comparison Analytical & Simulation Results
• Results are very comparable
• Differences are due to
- Spatial node distribution is not exactly uniform, since nodes are more likely to locate in the middle of movement area [Bettstetter]
- Average nodal speed decreases over time [Yoon]
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 16
Outline
• Motivation
• Related Work
• Contact-Based Mobility (CBM) Metrics
• Statistical and Theoretical Analysis for Random Waypoint
• Uses of CBM Metrics
◦ CBM Metrics in Network Simulator ns-2
• Conclusion
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 17
Uses of CBM Metrics
• Design and adaptation of delay-tolerant mobility-aided protocols
◦ Detect large time-scale mobility patterns, examples:
▪Node src encounters dest-1 periodically
▪Nodes src and x move in a group
◦ At run-time: HELLO beaconing
Dest-1
X
Contact table of src
src
dest-1
x
• Performance analysis of delay-tolerant mobility-aided protocols
◦ Classification of mobility scenario
◦ Performance evaluation and comparison
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 18
The Network Simulator ns-2
• Ns-2: discrete event simulator for wired & wireless networks
• General Operations Director (GOD): central instance
◦ Stores global state information:
▪#nodes
▪node position
▪number of hops between 2 nodes
▪partitioning information
• GOD simplifies (global view) evaluation of wireless protocols
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Research Group
“Distributed Systems” 19
CBM Metrics in ns-2
Annotation ToolAnnotation Tool
GeneralGeneralOperationsOperations
DirectorDirector(GOD)(GOD)
Delay- Delay- TolerantTolerantProtocolProtocol
EvaluationEvaluation
Query()
CBM metrics
ns-2ns-2
Simulation Simulation tracetrace
Arbitrary ns-2 Arbitrary ns-2 movement tracemovement trace
Before Before simulationsimulation
During During simulationsimulation
Movement trace annotated with Movement trace annotated with CBM informationCBM information
Basic communication Basic communication modelmodel
http://canu.informatik.uni-stuttgart.de/cbmhttp://canu.informatik.uni-stuttgart.de/cbm
A and B communicate if
distance(A,B) <= comm_range
Universität Stuttgart
IPVS
Research Group
“Distributed Systems” 20
Conclusion
• We introduced novel metrics to quantify mobility on a large time- scale
◦ Based on contacts between nodes
◦ Important for evaluation of mobility-aided delay-tolerant networking
• Detailed statistical analysis for random waypoint
• First steps towards an analytical model for random waypoint
• We provide implementation for ns-2
Universität Stuttgart
Institute of Parallel and Distributed Systems (IPVS)
Universitätsstraße 38D-70569 Stuttgart
Thank you for your attention!
http://canu.informatik.uni-stuttgart.de/cbmhttp://canu.informatik.uni-stuttgart.de/cbm
{khelil,marron,rothermel}@informatik.uni-stuttgart.de