Tutorial
Mobility Modeling for Design and Analysis of
Ad Hoc Wireless Networks
Ahmed Helmy
Computer and Information Science and Engineering (CISE)
College of Engineering
University of Florida
helmy@ufl.edu , http://www.cise.ufl.edu/~helmy
Founder and Director:
Wireless Mobile Networking Lab http://nile.cise.ufl.edu
Founder of the NOMADS research group
Outline
• Geographic Services in Wireless Networks
– Robust Geographic Routing
– Robut Geocast
– Geographic Rendezvous for Mobile Peer-to-Peer Networks (R2D2)
• Towards Behavioral Modeling and Context-Aware Protocols
–
–
–
–
Mobility and Connectivity Modeling (IMPORTANT & PATHS)
Mobility-Assisted Information Diffusion (MAID)
Trace-based Modeling of Behavior (IMPACT & MobiLib)
The Next Generation Classroom & Context-Aware Protocols
2
IMPORTANT: A framework to systematically
analyze the "Impact of Mobility on
Performance Of RouTing in Ad-hoc NeTworks"
Fan Bai, Narayanan Sadagopan, Ahmed Helmy
{fbai, nsadagop, helmy}@usc.edu
website “http://nile.usc.edu/important/”
* F. Bai, N. Sadagopan, A. Helmy, "IMPORTANT: A framework to systematically analyze the
Impact of Mobility on Performance of RouTing protocols for Adhoc NeTworks", IEEE
INFOCOM, pp. 825-835, April 2003.
* F. Bai, N. Sadagopan, A. Helmy, “The IMPORTANT Framework for Analyzing the Impact of
Mobility on Performance of Routing for Ad Hoc Networks”, AdHoc Networks Journal Elsevier Science, Vol. 1, Issue 4, pp. 383-403, November 2003.
* F. Bai, A. Helmy, "The IMPORTANT Framework for Analyzing and Modeling the Impact of
Mobility in Wireless Adhoc Networks", Book Chapter in the book "Wireless Ad Hoc and
Sensor Networks”, Kluwer Academic Publishers, June 2004.
Motivation
• Randomized models (e.g., random waypoint) do not capture
– (I) Existence of geographic restriction (obstacles)
– (II) Temporal dependence of node movement
Mobility
(correlation over history)
Space
– (III) Spatial dependence (correlation)
of movement among nodes
Geographic
Restriction
Spatial
Correlation
Temporal Correlation
• A systematic framework is needed to investigate the impact of various
mobility models on the performance of different routing protocols for
MANETs
• This study attempts to answer
–
–
–
–
What are key characteristics of the mobility space?
Which metrics can compare mobility models in a meaningful way?
Whether mobility matters? To what degree?
If the answer is yes, why? How?
4
The IMPORTANT Framework Overview
Mobility
Models
Connectivity
Graph
Random Waypoint
Group Mobility
Freeway Mobility
Manhattan Mobility
Contraction/Expansion
Hybrid
Trace-driven
Mobility
Metrics
Relative Speed
Spatial Dependence
Temporal Dependence
Node Degree/Clustering
Routing
Protocol
Performance
DSR
AODV
DSDV
GPSR
GLS
ZRP
Building
Block
Analysis
Connectivity
Metrics
Link Duration
Path Duration
Encounter Ratio
Performance
Metrics
Flooding
Caching
Error Detection
Error Notification
Error Handling
Throughput
Overhead
Success rate
Wasted Bandwidth
5
IMPORTANT Mobility Models
• Random Way Point (RWP)
• Freeway (FW)
• Group Mobility (RPGM)
• Manhattan (MH)
member
member
Leader
6
Rich Coverage of Mobility & Connectivity Dimensions
High
High
Med
Med
Low
Low
Relative Speed
Link Duration
High
High
Med
Med
Low
Low
PATH Duration
Spatial Dependence
7
Whether Mobility Matters?
43%
DSR Throughput across Mobility
Random Waypoint : DSR is best
x10
DSR Overhead across Mobility
Manhattan : AODV is best
8
Relative Velocity
Putting the Pieces Together
Link Duration
Throughput
Spatial Dependence
Path Duration
Overhead
Why Mobility Matters?
9
On the Connectivity of Mobile Networks
PATHS: Analysis of PATH Duration Statistics and
their Impact on Reactive MANET Routing Protocols
F. Bai, N. Sadagopan, B. Krishnamachari, A. Helmy
{fbai, nsadagop, brksihna, helmy}@usc.edu
* F. Bai, N. Sadagopan, B. Krishnamachari, A. Helmy, "Modeling Path Duration Distributions in
MANETs and their Impact on Routing Performance", IEEE Journal on Selected Areas in
Communications (JSAC), Vol. 22, No. 7, pp. 1357-1373, Sept 2004.
•N. Sadagopan, F. Bai, B. Krishnamachari, A. Helmy, "PATHS: analysis of PATH duration Statistics and
their impact on reactive MANET routing protocols", ACM MobiHoc, pp. 245-256, June 2003.
Nodes moving in opposite directions
FW model
Vmax=5m/s
R=250m
Nodes moving in
the same direction/lane
Multi-modal Distribution of Link Duration for Freeway
model at low speeds
RPGM w/ 4 groups
Vmax=5m/s
R=250m
Nodes in different groups
Nodes in the same group
Multi-modal Distribution of Link Duration
for RPGM4 model at low speeds
Link Duration (LD) distribution at low speeds < 10m/s
11
RW
RPGM (4 groups)
FW
Vmax=30m/s
R=250m
Link Duration
at high speeds
> 10m/s
Not Exponential !!
12
RW
RPGM4
h=2
h=4
100
FW
h=4
Vmax=30m/s
R=250m
Path Duration (PD) distribution
for long paths ( 2 hops)
at high speeds (> 10m/s)
Exponential !!
13
Conclusions
• Mobility patterns are very IMPORTANT in evaluating
performance of ad hoc networks
• A rich set of mobility models is needed for a good evaluation
framework.
• Richness of those models should be evaluated using quantitative
mobility metrics.
• Mobile Network Connectivity:
– Link and Path duration distributions are bi/multi-modal at low
speeds for group and freeway mobility
– Link duration distribution is NOT exponential at high speeds
– Path duration distribution is exponential at high speeds
14
Mobility-Assisted Information Diffusion (MAID)
• Used for resource discovery, routing, node location, …
• Uses ‘encounter’ history to create age gradients towards target
• Utilizes (and depends on) mobility to diffuse information. Hence, is
expected to be sensitive to mobility degree and patterns
• The ‘Age gradient tree’ (AGT) determine MAID’s performance
• Unlike conventional adhoc routing, link/path duration may not be
the appropriate metrics to analyze
* Fan Bai, Ahmed Helmy, “Poster: Impact of Mobility on Mobility-Assisted Information Diffusion
(MAID) Protocols”, IEEE INFOCOM, March 2005.
15
Time: TA(D)=t1
Location: LA(D)=x1,y1
A
S
Time: TE(D)=t3
Location: LE(D)=x3,y3
E
D
Time: TF(D)=t4
Location: LF(D)=x4,y4
F
B
C
Time: TC(D)=t2
Location: LC(D)=x2,y2
Basic Operation of MAID: Encounter history, search and age gradient tree
16
Age-based Search Algorithm
-set C = S (current node)
-While C != D (not found yet)
- Search for a node A with TA(D)<TC(D)
(use expanding ring search)
- set C = A
TA(D): The Age for A’s last encounter with D
S
A1
A2
A3
A4
A5
A6
A7
D
17
MAID protocol phases and metrics
Transient State
• Cold cache (transient warm-up phase)
– More encounters ‘warm up’ the cache by increasing the entries
• Warm cache (steady state phase)
– Average encounter ratio reaches 30-40%,
– Age gradient trees are established
• Metrics
– Warm up time, Av path length, Cost of search to destination
18
Warm Up Phase
The Warm Up Time depends heavily on the Mobility model and the Velocity
19
Steady State Phase
Steady State Performance depends only on the Mobility model but not on the Velocity
- These metrics reflect the structure of the age-gradient trees (AGTs).
- Hence, MAID leads to stable characteristics of the AGTs.
20
Spatio-Temporal Correlations in the AGT
400 nodes
3000mx3000m area
Radio range 250m
RWK
S
RWP
V=10m/s
A
B
C
D
RPGM (80grps)
MH
21
RWK
RWP
V=30m/s
RPGM (80grps)
MH
22
RWK
RWP
V=50m/s
RPGM (80grps)
MH
23
24
On-going Work: Trace-based Mobility Modeling
• Extend the IMPORTANT mobility tool:
– URL: http://nile.cise.ufl.edu/important
• Trace-based mobility models
nile.cise.ufl.edu/MobiLib
– Pedestrians on campus
• Usage pattern (WLAN traces)
– UFL, USC, MIT, UCSD, Dartmouth,…
• Student tracing (survey, observe)
– Vehicular mobility
• Transportation literature
– Parametrized hybrid models
• Integrate Weighted Group mobility with Pathway/Obstacle Model
• Derive the parameters based on the traces
25
MobiLib Traces (since May ’05)
•
•
•
•
•
•
•
•
•
•
•
•
•
University of Florida (UFL) [in progress]
University of Southern California (USC) [3 traces]
Dartmouth [2 traces]
MIT [3 traces]
UCSD [2 traces]
UCSB
U-Mass Amherst
U Washington
U Cambridge (UK)
Georgia Tech (promised)
UNC (promised)
Ohio OSU (promised)
12 Univs (9 + 3 promised) , 14 traces + 3 promised
26
Trace-based Mobility Modeling: USC Case Study
Univ. Southern California (USC)
- Total Population: ~ 25,000 students
- Wireless Users: ~6000 students
- Access Points: ~400
27
IMPACT: Investigation of Mobile-user Patterns Across
University Campuses using WLAN Trace Analysis*
• Classes of future wireless networks will be attached to humans
• What kinds of correlations exist between wireless users?
• Analyze measurements of wireless networks
– Understand Wireless Users Behavior (individual and group)
– Develop models to understand user associations
• Study of user behavior based on traces of University WLANs
* W. Hsu, A. Helmy, “IMPACT: Investigation of Mobile-user Patterns Across University Campuses using
WLAN Trace Analysis”, two papers at IEEE Wireless Networks Measurements (WiNMee), April 2006
28
Statistics of Studied Traces
- 4 major campuses – 30 day traces studied from 2+ years of traces
- Total users > 12,000 users - Total Access Points > 1,300
Trace source
Trace duration
User
type
Environment
Collection
method
Analyzed
part
MIT
7/20/02 –
8/17/02
Generic
3 corporate
buildings
Polling
Whole
trace
Dartmouth
4/01/01 –
6/30/04
Generic
w/ subgroup
University
campus
Event-based
July ’03
April ’04
UCSD
9/22/02 –
12/8/02
PDA only
University
campus
Polling
09/22/0210/21/02
USC
4/20/05 –
3/31/06
Generic
University
campus
Event-based
04/20/0505/19/05
• Try to understand the changes of user association behavior w.r.t.
– Time - Environment - Device type - Trace collection method
• Analyze
– Individual association patterns and repetitive behavior
– Group and friendship behavior (via encounters)
29
Fraction of online time
associated with the AP
Prob.(coverage > x)
Observations: Visited Access Points (APs)
CCDF of coverage of users
[percentage of visited APs]
Average fraction of time a MN associates with APs
•Individual users access only a very small portion of APs in the network.
•On average a user spends more than 95% of time at its top 5 most visited APs.
•Long-term mobility is highly skewed in terms of time associated with each AP.
30
Observations: On-line Time
-On-off behavior is very common for wireless users.
-This is especially true for small handheld devices (at UCSD).
-There are clear categories of heavy and light users, the distribution of which is
skewed and heavily depends on the campus.
31
Observations: Similarity Index
•Clear repetitive patterns of association in wireless network users.
•Typically, user association patterns show the strongest repetitive pattern at time
gap of one day/one week.
32
0
Fraction of user population (x)
0.4
0.6
0.2
0.8
1
1
Cambridge
0.1
UCSD
MIT
USC
0.01
Dart-04
0.001
0.0001
Prob. (total encounter events > x)
Prob. (unique encounter fraction > x)
Observations: Encounters
Dart-03
CCDF of unique encounter count
CCDF of total encounter count
•In all the traces, the MNs encounter a small fraction of the user population.
• A user encounters 1.8%-6% on average of the whole population (except UCSD)
•The number of total encounters for the users follows a BiPareto distribution.
33
Inter-encounter time distribution is power law!
34
Encounter-graphs
• Definition
– When 2 nodes access the same AP at the same time we
call this an ‘encounter’
– The encounter graph has all the mobile nodes as vertices
and its edges link all those vertices that encounter each
other
35
Graphs , Path Length and Clustering
Small World Graph: Low path length, High clustering
Regular Graph
- High path length
- High clustering
1
Random Graph
- Low path length,
- Low clustering
0.8
0.6
0.4
0.2
Clustering
Path Length
0
0.0001
0.001
0.01
0.1
1
probability of re-wiring (p)
- In Small Worlds, a few short cuts contract the diameter (i.e., path length) of a regular graph to
resemble diameter of a random graph without affecting the graph structure (i.e., clustering)
36
Small Worlds of Encounters
Normalized CC and PL
• ‘Encounter’: When 2 nodes access the same AP at the same time
• Encounter graph: nodes as vertices and edges link all vertices that encounter
Clustering Coefficient
Av. Path Length
Trace period [days]
• The encounter graph is a Small World graph
• Even for short time period (1 day) its metrics (CC, PL) almost saturate
37
Encounter-graphs using Friends
• Distribution for friendship index FI is exponential for all the traces
• Friendship between MNs is highly asymmetric
• Among all node pairs: < 5% with FI > 0.01, and <1% with FI > 0.4
•Top-ranked friends tend to form cliques and low-ranked friends are the key to
provide random links and reduce the degree of separation in encounter graph.
38
Encounter-based Information Diffusion
•Encounters patterns are rich enough to support information diffusion.
•Information can be delivered to more than 94% of users in <2 days.
•Reachability & Av delay do not decrease significantly until 40+% of nodes
become selfish.
39
Initial Findings of IMPACT
• Individual Behavior
–
–
–
–
A user spends > 95% of on-line time with top 5 most visited APs
Clear On/Off behavior with distinct groups of heavy and light users
Most users have low mobility while on-line (even PDAs)
Clear repetitive access patterns (esp. for 1, 7 day periods)
• Group/Encounter Behavior
–
–
–
–
Average encounter rate is only ~2-6% of user population
Encounter graph is Small World. Metrics converge in < 1 day (of 30)
Low-ranked friends key to reduce degrees of separation
Encounter delivery reaches > 94% users in 2 days (99% in 6 days)
40
Network Usage vs. Mobility
Wireless Network (WLAN) Usage Traces
•
•
•
Collect measurements of network access patterns for WLAN users at various
locations/buildings on campus
Draw map and join the buildings via shortest pathways to approximate user
movement routes
Estimate transition probability from one location to another at a given time
slot
KOH
1
Number of Access Points (AP)
Number of Buildings with APs
Number of Registered Users
Number of Users in Trace
200
44
5250
4576
JEP
2
LVL
3
6
5
PED
OHE
TOMMY
C. Jr.
4
7
•
Tracers trap MAC addresses accessing the
WLAN - Building level granularity
Wireless Network Coverage Map at USC - main campus
41
USC MAP WITH OBSERVATION LOCATIONS
Observation Location (OL) Type
(1) KOH
Computational/Residential
(2) JEP
Residential/Library route
(3) LVL
Library
(4) TOMMY
Center of Campus
(5) PED
Classes
(6) OHE
Classes
(7) Carl's Jr.
Cafeteria
KOH
1
JEP
2
LVL
3
PED
6
5
OHE
TOMMY
C. Jr.
4
7
At1
Bt1
Ct1
At2
10:00-10:15
Bt2
10:15-10:30
Ct2
Statistics about recorded mobility traces used in this study
10:45-11:00
10:30-10:45
Trace Period
Feb 25 - April 25
Number of Persons in the trace
Number of Observers
60 Number of Groups Observed
Total Observation Hours
220 Number of Subgroups
6389
1758
2382
Partial Recorded Data and example
Observation Location (OL) Date Time
Group Size Direction of Group Subgroup Size(s) Direction of Subgroup
Olin Hall (OHE) OL6
12-Mar 10:03 AM
3 SE
2 SE
1 NE
42
Observations vs. WLAN traces
Distributions
1000
#of people 800
observed at 600
# of
access
400
TOMMY
Observation Location
CARLS JR.
PED
0
LVL
JEP
KOH
OHE
200
S4
time slots
S1
Observation traces
•
•
Series2
Series3
WLAN access traces
Series4
Observation traces exhibit drastically different trends than WLAN traces
The two traces include different parts of the student population
–
–
–
•
Series1
WLAN users tend to cluster around base stations
WLAN users exhibit on-off behavior (sit-down, turn on laptop, access wireless network, turn
off, then move). Seldom did users access the WLAN when mobile
Observation traces trace actual mobility instead of network access patterns
Mobility models based on network access traces may not reflect actual mobility of the
users
43
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Other
probability
probability
probability
Survey based: Weighted Way Point (WWP) Model
0-30
31-60
61-120
pause time (m)
121-240
> 240
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Classroom
classroom
Library
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Off-campus
cafeteria
0-30
31-60
Other area
61-120
121-240
on
campus
pause time (m)
> 240
Library
0-30
31-60
61-120
121-240
> 240
pause time (m)
44
Vision: Community-wide Wireless/Mobility Library
• Library of
– measurements from Universities, vehicular networks
– realistic models of behavior (mobility, traffic, friendship,
encounters)
– benchmarks for simulation and evaluation
– trace data mining tools
• Can we use the insight to design protocols of the future
(not only for evaluation)?
• … Interest-based search and routing !
45
Effective Layered Mobile Networking Model
Traffic , Application Model
Cooperation , Trust , Security Model
Wireless Channel Model
Device On/Off Model IMPACT
Technology Penetration,Deployment Model
Mobility Model IMPORTANT
Actual connectivity graph results from the interaction of all these layers
46
Mobility Simulation Tools
• The Network Simulator (NS-2) (USC/ISI, UCB, Xerox Parc)
[wireless extensions CMU/Rice]
– www.isi.edu/nsnam
• The GloMoSim Simulator (UCLA)/QualNet (Commercial)
• The IMPORTANT Mobility Tool (USC/UFL)
– nile.cise.ufl.edu/important
• The Obstacle Mobility simulator (UCSB)
– moment.cs.ucsb.edu/mobility
• The CORSIM Simulator
• OPNET (commercial)
47
IMPORTANT
• Includes:
– Mobility generator tools for FWY, MH, RPGM, RWP,
RWK (future release), City Section (future rel.)
– Acts as a pre-processing phase for simulations, currently
supports NS-2 formats (can extend to other formats)
– Analysis tools for mobility metrics (link duration, path
duration) and protocol performance [future rel.]
(throughput, overhead, age gradient tree chars)
– Acts as post-processing phase of simulations
– nile.cise.ufl.edu/important
48
Manhattan
Group
IMPORTANT
Freeway
49
CORSIM (Corridor Traffic Simulator)
• Simulates vehicles on highways/streets
• Micro-level traffic simulator
– Simulates intersections, traffic lights, turns, etc.
– Simulates various types of cars (trucks, regular)
– Used mainly in transportation literature (and recently
for vehicular networks)
– Does not incorporate communication or protocols
– Developed through FHWA (federal highway
administration) http://ops.fhwa.dot.gov
– Need to buy license
50
CORSIM
51
On-going and Future Directions for Mobile Networking
– Controlled mobility scenarios
• DakNet, Message Ferries, Info Station
– Mobility-Assisted protocols
• Mobility-assisted information diffusion:
EASE, FRESH, DTN, $100 laptop
– Context-aware Networking
• Mobility-aware protocols: selfconfiguring, mobility-adaptive protocols
• Socially-aware protocols: security, trust,
friendship, associations, small worlds
– On-going Projects
• The Next Generation Classroom
• Disaster Relief Networks
52
The Next Generation (Boundless) Classroom
Students
sensor
sensor
sensor
sensor
sensor
sensor-adhoc
Embedded sensor network
WLAN/adhoc
WLAN/adhoc
sensor
sensor
sensor
Multi-party conference
Tele-collaboration tools
sensor
sensor
sensor-adhoc
Instructor
WLAN/adhoc
Challenges
sensor
sensor
sensor
sensor
sensor-adhoc
-Integration of wired Internet, WLANs, Adhoc
Mobile and Sensor Networks
-Will this paradigm provide better learning
experience for the students?
Real world group experiments (structural health monitoring)
53
Disaster Relief (Self-Configuring) Networks
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
54
Future Directions: Technology-Human Interaction
The Next Generation Classroom
Emerging Wireless &
Multimedia Technologies
Protocols,
Applications,
Services
Human
Behavior
Mobility,
Load
Dynamics
55
Engineering
Multi-Disciplinary Research
Human Computer Interaction (HCI)
& User Interface
Social Sciences
Cognitive
Sciences
Education
Psycology
Application Development
Service Provisioning
Emerging Wireless &
Multimedia Technologies
How to Capture?
Protocols,
Applications,
Services
Human
Behavior
Educational/
Learning
Experience
Protocol Design
How to Evaluate?
Measurements
Mobility Models
Context-aware
Networking
How to Design?
Traffic Models
Mobility,
Load
Dynamics
56
Related Links and Resources
• Delay Tolerant Networks (DTNs)
– Research group: www.dtnrg.org
• Vehicular/Transportation Networks
– PATH project/center: www.path.berkeley.edu (UC-Berkeley)
– METRANS center: www.metrans.org (USC, CSULB)
• Mesh Networks
– Microsoft research http://research.microsoft.com/mesh/
– MIT RoofNet http://pdos.csail.mit.edu/roofnet
– GATech Message Ferries
http://www.cc.gatech.edu/fac/Mostafa.Ammar/ferrying.html
– UIUC, Rice, …. and others (chk nile.cise.ufl.edu/MobiLib)
57
Thank You !
• Ahmed Helmy
• Webpage: www.cise.ufl.edu/~helmy
• Lab: nile.cise.ufl.edu
• Mobility tools and Simulations:
IMPORTANT
• Wireless/Mobile Network Library of Traces:
MobiLib
58