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Tutorial Mobility Modeling for Design and Analysis of Ad Hoc Wireless Networks
Ahmed Helmy
Electrical Engineering Department University of Southern California helmy@usc.edu Webpage: http://ceng.usc.edu/~helmy Wireless Networking Lab: http://nile.usc.edu
UNIVERSITY OF SOUTHERN CALIFORNIA
Outline
•
– –
Ad Hoc Networks & Mobility Classification (15 min.)
Synthetic and Trace-based Mobility Models The Need for Systematic Mobility Framework
•
–
Survey of the Major Mobility Models (30 min.)
Random models - Group mobility models – Vehicular (Manhattan/Freeway) models - Obstacle models
•
– –
Characterizing Mobility (30 min.)
Mobility Dimensions (spatial and temporal dependency, geographic restrictions) Mobility Metrics (spatio-temporal correlations, path and link duration)
Ahmed Helmy - USC
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Outline (contd.)
•
– –
Mobility-centric framework to analyze ad hoc networks (40 min.)
The IMPORTANT mobility framework Case Studies: BRICS, PATHS, MAID
•
– –
Trace-based mobility modeling (30 min.)
Analyzing wireless network measurements and traces Survey-based and observation techniques
•
– –
Mobility simulation and analysis tools (20 min.)
Available software packages and tools Resources and related projects
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Wireless Mobile Ad hoc Networks (MANETs) • A Mobile Ad hoc Network (MANET) is a collection of mobile devices forming a multi-hop wireless network with minimal (or no) infrastructure • To evaluate/study adhoc networks mobility and traffic patterns are two significant factors affecting protocol performance. • Wireless network performance evaluation uses: – Mobility Patterns: usually, uniformly and randomly chosen destinations (random waypoint model) – Traffic Patterns: usually, uniformly and randomly chosen communicating nodes with long-lived connections • Impact of mobility on wireless networks and ad hoc routing protocols is significant
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Example Ad hoc Networks
Mobile devices (laptop, PDAs)
Vehicular Networks on Highways
Hybrid urban ad hoc network (vehicular, pedestrian, hot spots,…)
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Classification of Mobility and Mobility Models
Static (e.g., sensor networks) Uncontrolled Mobility Mobility Mobile Hybrid Controlled Mobility Unpredictable Mobility Predictable Mobility Hybrid
I- Based on Controllability
Hybrid
Synthetic
II- Based on Model Construction
Model Trace-based
Usage pattern Movement Pattern Hybrid Hybrid Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA Mobility Dimensions & Classification of Synthetic Uncontrolled Mobility Models
* F. Bai, A. Helmy, "A Survey of Mobility Modeling and Analysis in Wireles Adhoc Networks", Book Chapter in the book "Wireless Ad Hoc and Sensor Networks”, Kluwer Academic Publishers, June 2004. Ahmed Helmy - USC
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I. Random Waypoint (RWP) Model
1. A node chooses a random destination anywhere in the network field 2. The node moves towards that destination with a velocity chosen randomly from [0, Vmax] 3. After reaching the destination, the node stops for a duration defined by the “pause time” parameter. 4. This procedure is repeated until the simulation ends – Parameters: Pause time T, max velocity Vmax – Comments:
• • Speed decay problem, non-uniform node distribution Variants: random walk, random direction, smooth random, ...
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Random Way Point: Basics
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Random Way Point: Example
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-1- RWP leads to non-uniform distribution of nodes due to bias towards the center of the area, due to non-uniform direction selection. To remedy this the “random direction” mobility model can be chosen. -2- Average speed decays over time due to nodes getting „stuck‟ at low speeds
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II. Random (RWK) Walk Model
• Similar to RWP but
– – – – Nodes change their speed/direction every time slot New direction is chosen randomly between (0,2] New speed chosen from uniform (or Gaussian) distribution When node reaches boundary it bounces back with (-)
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Random Walk
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III. Reference Point Group Mobility (RPGM)
• • • • Nodes are divided into groups Each group has a leader The leader‟s mobility follows random way point The members of the group follow the leader‟s mobility closely, with some deviation • Examples:
– Group tours, conferences, museum visits – Emergency crews, rescue teams – Military divisions/platoons
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Group Mobility: Single Group
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Group Mobility: Multiple Groups
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IV. Obstacle/Pathway Model
• Obstacles/bldgs map • Nodes move on pathways between obstacles • Nodes may enter/exit buildings • Pathways constructed by computing Voronoi graph (i.e., pathways equidistant to nearby buildings) • Obstacles affect communication
– Nodes on opposite sides (or in/outside) of a building cannot communicate
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V. Related Real-world Mobility Scenarios
• Pedestrian Mobility
– University or business campuses – Usually mixes group and RWP models, with obstacles and pathways
• Vehicular Mobility
– Urban streets (Manhattan-like) – Freeways – Restricted to streets, involves driving rules
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Urban Street
Streets - Manhattan
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Freeway Map
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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
Temporal Correlation Geographic Restriction
Spatial 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?
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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.
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Framework Goals (Questions to Answer)
• Whether mobility matters? and How much does it matter?
– Rich set of mobility models that capture characteristics of different types of movement – Protocol independent metrics such as mobility metrics and connectivity graph metrics to capture the above characteristics
• Why?
– Analysis process to relate performance with a specific characteristic of mobility via connectivity metrics
• How?
– Systematic process to study the performance of protocol mechanistic building blocks (BRICS) across various mobility characteristics
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The IMPORTANT Framework Overview
Mobility Models
Connectivity Graph
Building Block Analysis
Routing Protocol Performance
DSR AODV DSDV GPSR GLS ZRP
Random Waypoint Group Mobility Freeway Mobility Manhattan Mobility Contraction/Expansion Hybrid Trace-driven
Mobility Metrics
Relative Speed Spatial Dependence Temporal Dependence Node Degree/Clustering
Connectivity Metrics
Link Duration Path Duration Encounter Ratio
Performance Metrics
Flooding Caching Error Detection Error Notification Error Handling
Throughput Overhead Success rate Wasted Bandwidth
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Mobility Metrics • Relative Speed (mobility metric I)
– The magnitude of relative speed of two nodes, average over all neighborhood pairs and all time
1 T N N R S | v (i, t ) v ( j , t ) | P t 0 i 1 j 1
j i
if dist(( xi , yi ), ( x j , y j )) 2 R
• Spatial Dependence (mobility metric II)
– The value of extent of similarity of the velocities/dir of two nodes that are not too far apart, average over all neighborhood pairs and all time
1 T N N min( v (i, t ), v ( j, t )) v (i, t ) v ( j, t ) Dspatial P t 0 i 1 j 1 max( v (i, t ), v ( j, t )) | v (i, t ) || v ( j, t ) |
j i
if dist(( xi , yi ), ( x j , y j )) 2 R
For example, RWP model, Vmax=30m/s, RS=12.6m/s, Dspatial=0.03
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Connectivity Graph Metrics • Average link duration (connectivity metric I)
– The value of link duration, average over all nodes pairs
1 N N L D LD(i, j ) if there is a link between i and j P i 1 j 1
j i
– Link/Path duration distributions (PATHS study)
Protocol Performance Metrics
• Throughput: delivery ratio • Overhead: number of routing control packets sent
Ahmed Helmy - USC
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Mobility Models Summary
Application Random Waypoint Model Group Mobility Model Freeway Mobility Model Manhattan Mobility Model Spatial Dependence Geographic Restriction
General (uncorrelated straight lines)
No
No
Conventions, Campus Metropolitan Traffic/Vehicular Urban Traffic/Vehicular
Yes
No
Yes
Yes
No
Yes
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Parameterized Mobility Models
• Random Waypoint Model (RWP)
– Each node chooses a random destination and moves towards it with a random velocity chosen from [0, Vmax]. After reaching the destination, the node stops for a duration defined by the “pause time” parameter. This procedure is repeated until simulation ends Parameters: Pause time T, max velocity Vmax Each group has a logical center (group leader) that determines the group’s motion behavior Each nodes within group has a speed and direction that is derived by randomly deviating from that of the group leader
– – –
• Reference Point Group Model (RPGM)
member member Leader
– –
| Vm em ber(t ) | | Vleader(t ) | random() SDR Vmax
m em ber(t ) leader(t ) random() ADR max
Parameters: Angle Deviation Ratio(ADR) and Speed Deviation Ratio(SDR), number of groups, max velocity Vmax. In our study, ADR=SDR=0.1 In our study, we use two scenarios: Single Group (SG) and Multiple Group (MG)
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Parameterized Mobility Models
• Freeway Model (FW)
– Each mobile node is restricted to its lane on the freeway – The velocity of mobile node is temporally dependent on its previous velocity – If two mobile nodes on the same freeway lane are within the Safety Distance (SD), the velocity of the following node cannot exceed the velocity of preceding node – Parameter: Map layout, Vmax
Map for FW
• Manhattan Model (MH)
– Similar to Freeway model, but it allows node to make turns at each corner of street – Parameter: Map layout, Vmax
Map for MH
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Experiment I: Analysis of mobility characteristics
• IMPORTANT mobility tool
– integrated with NS-2 (released Jan ‟04, Aug „05) – http://nile.usc.edu/important
• Simulation done using our mobility generator and analyzer
• • • • • • Number of nodes(N) = 40, Simulation Time(T) = 900 sec Area = 1000m x 1000m Vmax set to 1,5,10,20,30,40,50,60 m/sec across simulations RWP, pause time T=0 SG/MG, ADR=0.1, SDR=0.1 FW/MH, map layout in the previous slide
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Mobility metrics
• Objective:
– validate whether proposed mobility models span the mobility space we explore
• Relative speed
– For same Vmax, MH/FW is higher than RWP, which is higher than SG/MG
Relative Speed
• Spatial dependence
– For SG/MG, strong degree of spatial dependence – For RWP/FW/MH, no obvious spatial dependence is observed
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Spatial Dependence
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Connectivity Graph Metrics
• Link duration
– For same Vmax, SG/MG is higher than RWP, which is higher than FW, which is higher than MH
Link duration
• Summary
– Freeway and Manhattan model exhibits a high relative speed – Spatial Dependence for group mobility is high, while it is low for random waypoint and other models – Link Duration for group mobility is higher than Freeway, Manhattan and random waypoint
Path duration
- Similar observations for Path duration
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Experiment II: Protocol Performance across Mobility Models
Simulations done in ns-2:
• Routing protocols: DSR, AODV, DSDV • Same set of mobility trace files used in experiment1 • Traffic pattern consists of source-destination pairs chosen at random • 20 source, 30 connections, CBR traffic • Data rate is 4packets/sec (low data rate to avoid congestion) • For each mobility trace file, we vary traffic patterns and run the simulation for 3 times
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Results and Observations
• Performance of routing protocols may vary drastically across mobility patterns (Example for DSR)
Throughput
Routing Overhead
• There is a difference of 40% for throughput and an order of magnitude difference for routing overhead across mobility models!
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Which Protocol Has the Highest Throughput ?
• We observe that using different mobility models may alter the ranking of protocols in terms of the throughput!
Random Waypoint : DSR
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Manhattan : AODV !
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Which Protocol Has the Lowest Overhead ?
• We observe that using different mobility models may alter the ranking of protocols in terms of the routing overhead!
RPGM(single group) : DSR
Manhattan : DSDV
• Recall: Whether mobility impacts protocol performance? • Conclusion: Mobility DOES matter, significantly, in evaluation of protocol
performance and in comparison of various protocols!
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Putting the Pieces Together
• Why does mobility affect protocol performance? • We observe a very clear trend between mobility metric, connectivity and performance
– With similar average spatial dependency
• Relative Speed increases Link Duration decreases Routing Overhead increases and throughput decreases
– With similar average relative speed
• Spatial Dependence increase Link Duration increasesThroughput increases and routing overhead decreases
• Conclusion: Mobility Metrics influence Connectivity Metrics which
in turn influence protocol performance metrics !
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Relative Velocity
Putting the Pieces Together
Link Duration Throughput
Spatial Dependence
Path Duration
Overhead
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Mechanistic Building Blocks (BRICS)
• How does mobility affect the protocol performance? • Approach:
*
– The protocol is decomposed into its constituent mechanistic, parameterized building block, each implements a well-defined functionality – Various protocols choose different parameter settings for the same building block. For a specific mobility scenario, the building block with different parameters behaves differently, affecting the performance of the protocol
• We are interested in the contribution of building blocks to the overall performance in the face of mobility • Case study:
– Reactive protocols (e.g., DSR and AODV)
* F. Bai, N. Sadagopan, A. Helmy, "BRICS: A Building-block approach for analyzing RoutIng protoCols in Ad Hoc Networks - A Case Study of Reactive Routing Protocols", IEEE International Conference on Communications (ICC), June 2004.
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Building Block Diagram for reactive protocols
DSR
Local Inquiry & Global Flooding Link Monitoring Error Notification Cache Management
AODV
Expanding Ring Search & Global Flooding Link Monitoring Error Broadcast
Cache Management
Salvaging
(a)
Generalization of Flooding
(b)
Generalization of Flooding
Localized Rediscovery
Generalization of Error Handling
Route Setup
Route Request
Flooding
Add Route Cache
Caching
Route Reply
Range of Flooding
Caching Style Expiration Timer
Localized/Non-localized method
Route Maintenance
Error Detection
Link Breaks
Route Invalidate
Error Handling
Notify
Error Notification
Notify
(c)
Detection Method
Handling Mode Ahmed Helmy - USC
Recipient
UNIVERSITY OF SOUTHERN CALIFORNIA
How useful is caching?
DSR
AODV
• •
In RW, FW and MH model, most of route replies come from the cache, rather than destination (>80% for DSR, >60% for AODV in most cases) The difference in the route replies coming from cache between DSR and AODV is greater than 20% for all mobility models, maybe because of caching mode
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Is aggressive caching always good?
DSR
•
•
The invalid cached routes increase from RPGM to RW to FW to MH mobility models
Aggressive Caching may have adverse effect at high mobility scenarios!
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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.
• Observation
– In the previous study only „average‟ link duration was considered. – Are we missing something by looking only at averages? – Next: We conduct the PATHS study to investigate statistics and distribution of link and path duration.
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PATHS: Analysis of PATH Duration Statistics and their Impact on Reactive MANET Routing Protocols
Fan Bai, Narayanan Sadagopan, Bhaskar Krishnamachari, Ahmed 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), Special Issue on Quality of Service in Variable Topology Networks, 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.
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Motivation and Goal
• Mobility affects connectivity (i.e., links), and in turn protocol mechanisms and performance • It is essential to understanding effects of mobility on Protocol Mechanisms link and path characteristics Performance Mobility Connectivity (Throughput, • In this study: Overhead)
– Closer look at the mobility effects on connectivity metrics (statistics of link duration (LD) and path duration (PD)) – Develop approximate expressions for LD & PD distributions (Is it really exponential? When is it exponential?) – Develop first order models for Tput & Overhead as f(PD)
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Connectivity Metrics
• Link Duration (LD):
– For nodes i,j, the duration of link i-j is the longest interval in which i & j are directly connected – LD(i,j,t1)=t2-t1
• iff t, t1 t t2, > 0 : X(i,j,t)=1,X(i,j,t1-)=0, X(i,j,t2+)=0
• Path Duration (PD):
– Duration of path P={n1,n2,…,nk} is the longest interval in which all k-1 links exist
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Simulation Scenarios in NS-2
• Path duration computed for the shortest path, at the graph and protocol levels, until it breaks. • Used the IMPORTANT mobility tool:
– nile.usc.edu/important
• Mobility Parameters
– Vmax = 1,5,10,20,30,40,50,60 m/s, – RPGM: 4 groups (RPGM4), Speed/Angle Deviation Ratio=0.1
• 40 nodes, in 1000mx1000m area • Radio range (R)=50,100,150,200,250m • Simulation time 900sec
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Link Duration (LD) PDFs
• At low speeds (Vmax < 10m/s) link duration has multi-modal distribution for FW and RPGM4
– In FW due to geographic restriction of the map
• Nodes moving in same direction have high link duration • Nodes moving in opposite directions have low link duration
– In RPGM4 due to correlated node movement
• Nodes in same group have high link duration • Nodes in different groups have low link duration
• At higher speeds (Vmax > 10m/s) link duration does not exhibit multi-modal distribution • Link duration distribution is NOT exponential
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Nodes moving in opposite directions
FW model Vmax=5m/s R=250m
RPGM w/ 4 groups Vmax=5m/s R=250m
Nodes in different groups Nodes in the same group
Nodes moving in the same direction/lane
Multi-modal Distribution of Link Duration for Freeway model at low speeds
Multi-modal Distribution of Link Duration for RPGM4 model at low speeds
Link Duration (LD) distribution at low speeds < 10m/s
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RPGM (4 groups)
FW
Vmax=30m/s R=250m
Link Duration at high speeds
> 10m/s
Not Exponential !!
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Path Duration (PD) PDFs
• At low speeds (Vmax < 10m/s) and for short paths (h2) path duration has multi-modal for FW and RPGM4 • At higher speeds (Vmax > 10m/s) and longer path length (h2) path duration can be reasonably approximated using exponential distribution for RW, FW, MH, RPGM4.
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Nodes moving in opposite directions
Nodes in different groups RPGM4 Vmax=5m/s h=2 hops R=250m
FW Vmax=5m/s h=1 hop R=250m Nodes moving in the same direction
Nodes in the same group
Multi-modal Distribution of Path Duration Multi-modal Distribution of Path Duration for Freeway model at low speeds, low hops for RPGM4 model at low speeds, low hops
Path Duration (PD) distribution for short paths at low speeds < 10m/s
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RW
h=2
RPGM4
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)
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Exponential Model for Path Duration (PD)
• Let path be the parameter for exponential PD distribution:
– PD PDF f(x)= path e- path x – As path increases average PD decreases (and vice versa)
• Intuitive qualitative analysis:
– – – – PD=f(V,h,R); V is relative velocity, h is path hops & R is radio range As V increases, average PD decreases, i.e., path increases As h increases, average PD decreases, i.e., path increases As R increases, average PD increases, i.e., path decreases
• Validate intuition through simulations
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Exponential Model for PD
But, PD PDF f(x)= path e- path x
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0.5
0.1
Probability
Exponential PD
Probability
RW h=2
Exponential 0.4 0.3 0.2 0.1 PD
FW h=4
0.05
0 0 10 20 30 40 50 Path Duration (sec)
0 0 10
Path Duration (sec)
20
- Correlation: 94.1-99.8% - Goodness-of-fit Test
RW FW RPGM K-S test 0.04-0.065 0.045-0.085 0.09-0.12
Probability
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 2
Exponential D= 0.048 PD
Vmax=30m/s R=250m
FW h=4
4
6
8
10 12 14 16 18 20 22 24 26 28 30
Cumulative Distribution Function (CDF)
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Effect of Path Duration (PD) on Performance: Case Study for DSR
• PD observed to have significant effect on performance • (I) Throughput: First order model
– T: simulation time, D: data transferred, Tflow: data transfer time, Trepair: total path repair time, trepair: av. path repair time, f: path break frequency
Throughput
D T
1 T T flow Trepair T flow trepair. f .T T flow trepair. .T PD
trepair D Throughput (1 ) (1 ).rate PD T flow PD trepair
T
T flow trepair (1 ) PD
1 ) PD
Throughput (
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Effect of PD on Performance (contd.)
• (II) Overhead: First order model
– Number of DSR route requests= PD – p: non-propagating cache hit ratio, N: number of nodes
T
Overhead
1 PD
• Evaluation through NS-2 simulations for DSR
Throughput Overhead Random Waypoint (RW) Freeway (FW) -0.9165 -0.9597 0.9753 0.9812 Manhattan (MH) -0.9132 0.9978
Pearson coefficient of correlation () with 1
PD
– RPGM exhibits low , due to relatively low path changes/route requests
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Conclusions
• Detailed statistical analysis of link and path duration for multiple mobility models (RW,FW,MH,RPGM4):
– Link Duration: multi-modal FW and RPGM4 at low speeds – Path Duration PDF:
• Multi-modal FW and RPGM4 at low speeds and hop count • Exponential-like at high speeds & med/high hop count for all models
• Developed parametrized exponential model for PD PDF, as function of relative velocity V, hop count h and radio range R • Proposed simple analytical models for throughput & overhead that show strong correlation with reciprocal of average PD • Open Issues:
– Can we prove this mathematically? Yes – Is it general for random and correlated mobility? Yes
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Case Studies Utilizing Mobility Modeling
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Case Study on Effects of Mobility on the Grid Location Service (GLS)
• Group mobility: - prolongs protocol convergence - incurs max overhead - incurs max query failure rate * Subtle Coupling between
– (1) Mobility – (2) The Grid Topology – (3) Protocol Mechanisms
100 90 80 70 60 50 40 30 20 10 0
100 90
Percentage Failed Queries
Percentage Overhead
Manhattan Freeway Group Mobility RWP
Models
Manhattan Freeway Group Mobility RWP
80 70 60 50 40 30 20 10 0
Model
* C. Shete, S. Sawhney, S. Herwadka, V. Mehandru, A. Helmy, "Analysis of the Effects of Mobility on the Grid Location Service in Ad Hoc Networks", IEEE ICC, June 2004.
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Case Study on Geo-routing across Mobility Models
• Depending on beacon frequency location info may be out of date • Nodes chosen by geographic routing may move out of range before next beacon update. • Increasing beacon updates does not always help! • Using simple mobility prediction achieved up to 37% saving in wasted bandwidth, 27% delivery rate
700 w/o MP w/o NLP w/ MP(NLP+DLP)
1
GPSR
D e liv e ry R a te (% )
0.9 0.8 0.7 0.6 0.5 0.4 0.3
GPSR with prediction
600
N u mb e r o f p a cke t d ro p s
500
400
300
200
GPSR with prediction
1 1.5 3 Beacon Interval (sec) 6
0.2 0.1 0 10
w/o MP w/o NLP w/ MP(NLP+DLP)
GPSR
20 30 Max Node Speed (m/sec) 40 50
100
0 0.250.5
(FWY) * D. Son, A. Helmy, B. Krishnamachari, "The Effect of Mobility-induced Location Errors on Geographic Routing in Ad Hoc Networks: Analysis and Improvement using Mobility Prediction", IEEE WCNC, March 2004, and IEEE Transactions on Mobile Computing, Special Issue on Mobile Sensor Networks, 3rd quarter 2004 (to appear).
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Contraction, Expansion and Hybrid Models
• May be useful for sensor networks • Contraction models show „improved‟ performance (e.g., Tput, link duration) with increased velocity
Expansion Contraction Hybrid * Y. Lu, H. Lin, Y. Gu, A. Helmy, "Towards Mobility-Rich Performance Analysis of Routing Protocols in Ad Hoc Networks: Using Contraction, Expansion and Hybrid Models", IEEE ICC, June 2004.
UNIVERSITY OF SOUTHERN CALIFORNIA
MAID Case Study: Utilizing Mobility
• MAID: Mobility Assisted Information Diffusion • May be used for: resource discovery, routing, node location applications • MAID uses „encounter‟ history to create time (or age) gradients towards the target/destination • MAID uses (and depends on) mobility to diffuse information, hence its performance may be quite sensitive to mobility degree and patterns • Unlike conventional adhoc routing, link/path duration may not be the proper metrics to analyze • The „Age gradient tree‟ and its characteristics determine MAID‟s performance
* F. Bai, A. Helmy, "Impact of Mobility on Mobility-Assisted Information Diffusion (MAID) Protocols", USC Technical Rreport, July 2005. [Submitted for review]
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Time: t1 Location: x1,y1
S
A
Time: t3 Location: x3,y3
E D B C
Time: t2 Location: x2,y2
Time: t4 Location: x4,y4
F
Basic Operation of MAID: Encounter history, search and age gradient tree
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
MAID protocol phases and metrics
• Cold cache (initial, transient, phase):
– Encounter cache is empty – More encounters „warm up‟ the cache by increasing the entries
• Warm cache (steady state phase) :
– Average encounter ratio reaches ~30% of network nodes – Age gradient trees are established
• Metrics:
– Warm up time – Average path length to a destination – Cost of search to establish the route to the destination
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Warm Up Phase
The Warm Up Time depends heavily on the Mobility model and the Velocity
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Spatio-Temporal Correlations in the AGT
RWK
400 nodes 3000mx3000m area Radio range 250m V=10m/s
RWP
RPGM (80grps)
Ahmed Helmy - USC
MH
UNIVERSITY OF SOUTHERN CALIFORNIA
RWK
RWP
V=30m/s
RPGM (80grps)
Ahmed Helmy - USC
MH
UNIVERSITY OF SOUTHERN CALIFORNIA
RWK
RWP
V=50m/s
RPGM (80grps)
Ahmed Helmy - USC
MH
UNIVERSITY OF SOUTHERN CALIFORNIA
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
On-going and Future Work
• Extend the IMPORTANT mobility tool:
– URL: http://nile.usc.edu/important
• Trace-based mobility models
– Pedestrians on campus
• Usage pattern (WLAN traces)
– USC, MIT, UCSD, Dartmouth,…
nile.usc.edu/MobiLib
• 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
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Trace-based Mobility Modeling
Total Population: ~ 25,000 students Wireless Users: ~6000 students Access Points: ~400
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis* • Classes of future wireless ad hoc and sensor 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 associations and friendship
• Study of relationships and user behavior based on measurements of various University WLANs
* W. Hsu, A. Helmy, “IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis”, USC TR, July „05 (Under Submission)
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Statistics of Studied Traces
- Four major campuses - Month long traces studied - Total users in the study: over 12,000 users - Total Access Points in the study: over 1,300
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Observations: On-line Time
On-off behavior is very common for wireless users. This seems especially true for small handheld devices. There are clear categories of heavy and light users, the distribution of which is skewed and heavily depends on the campus.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Observations: Visited Access Points (APs)
[percentage of visited APs]
•Individual users access only a very small portion of APs in the network, less than 35% in all campuses. The long-term mobility of users is highly skewed in terms of time associated with each AP. On average a user spends more than 95% of time at its top five most visited APs.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Observations: Visited APs
•The majority of users experience low mobility while using the network. This is even true for portable devices such as PDAs. The actual handoff statistics depend heavily on the environment.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Observations: Similarity Index
•We observe 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.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Observations: Encounters
•In all the traces, the MNs encounter a relatively small fraction of the user population; below 40% in most cases and never reaching above 60% in any case. Except for UCSD trace, on average a MN only encounters 1.88%-5.94% of the whole population. The number of total encounters for the users follows a BiPareto distribution, the parameters of which depends on the campus.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Regular Graph
- High path length - High clustering
1 0.8 0.6 0.4 0.2
Small World Graph: Low path length, High clustering
Random Graph
- Low path length, - Low clustering
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)
UNIVERSITY OF SOUTHERN CALIFORNIA
Encounter-graphs and Friendship
• Encounters link most of the MNs together in a connected graph:
– Albeit each MN encounters only with small portion of the population. – The encounter graph is a SmallWorld graph – Even for short time period (1 day) its clustering coefficent, average path length, and connectivity are all close to those for longer traces.
• Friendship between MNs is highly asymmetric.
– The distribution for the friendship index is exponential for all the traces, regardless of the friendship definition (based on time, encouner, or location). – Among all node pairs there are less than 5% with friendship index larger than 0.01, and less than 1% with friendship index larger than 0.4.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Encounter-graphs using Friends
•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.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Encounter-based Information Diffusion
•Encounters patterns are rich enough to support information diffusion. Specifically, information can be delivered to more than 94% of users within two days. The reachability and average delay do not decrease significantly until at least ~40% of nodes are selfish.
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Vision: Building Community-wide Wireless/Mobility Library
• Library of measurements from WLANs, mobility and associations from potential wireless societies (e.g., universities, vehicular nets) • Library of realistic models of user behavior (e.g., mobility, traffic, friendship, encounter models, … ) • Library of benchmarks and guidelines for simulation and evaluation • How much insight can we get by analyzing the traces? • Can we use the insight to „design‟ protocols of the future (not only for evaluation)? • Currently 20+ major universities willing to share their traces • …. more to come: http://nile.usc.edu/MobiLib • If you have traces: helmy@usc.edu !
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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 C. Jr.
7
TOMMY
4
Tracers trap MAC addresses accessing the WLAN - Building level granularity
Wireless Network Coverage Map at USC - main campus Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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
6
KOH
1
JEP
2
LVL
3
PED
5
OHE TOMMY C. Jr.
7 4
At1 Bt1 Ct1 At2
10:00-10:15
Bt2 Ct2
10:45-11:00
10:15-10:30
Statistics about recorded mobility traces used in this study
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
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Distributions
1000 #of people 800 observed at 600 400 200 LVL JEP KOH OHE 0 TOMMY PED
Observations vs. WLAN traces
# of access S4 time slots S1 Series1 Series2 Series3 Series4
Observation Location
CARLS JR.
Observation traces • •
WLAN access traces
Observation traces exhibit drastically different trends than WLAN traces The two traces include different parts of the student population
– – – 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
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Survey based: Weighted Way Point (WWP) Model
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
Classroom
classroom Off-campus Other area 61-120 121-240 on campus pause time (m)
probability
Library cafeteria 0-30 31-60
> 240
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0-30 31-60 61-120
Other
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
Library
probability
121-240
> 240
probability
0-30
31-60
61-120 pause time (m)
121-240
> 240
pause time (m)
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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)
– nile.usc.edu/important
• The Obstacle Mobility simulator (UCSB)
– moment.cs.ucsb.edu/mobility
• The CORSIM Simulator • OPNET (commercial)
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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.usc.edu/important
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Manhattan
IMPORTANT
Freeway
Group
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
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
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
CORSIM
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
– Protocol design for controlled mobility scenarios
– Examples: DakNet, Message Ferries, Info Station!
On-going and Future Directions Utilizing mobility
– Mobility-Assisted protocols
– A. Helmy, "Mobility-Assisted Resolution of Queries in Large-Scale Mobile Sensor Networks (MARQ)", Computer Networks Journal, Vol. 43, Issue 4, pp. 437-458, Nov03
– Context-aware Networks
• Mobility-aware protocols: self-configuring, mobility-adaptive protocols • Socially-aware networks: security, trust, friendship, associations, small worlds
contact
1 R 2 7
contact contact
3
C R 5
4
R
– Experiments:
• Boundless Classroom [Next generation education paradigm?!] • E-buddy system for campus security/safety
Ahmed Helmy - USC
6 R
Route
UNIVERSITY OF SOUTHERN CALIFORNIA
Related Links and Resources
• Delay Tolerant Networks (DTNs)
– Research group: www.dtnrg.org
• Vehicular/Transportation Networks
– PATH project/center: www.path.berkeley.edu – 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.usc.edu/MobiLib)
Ahmed Helmy - USC
UNIVERSITY OF SOUTHERN CALIFORNIA
Thank You !
• Ahmed Helmy • Webpage: ceng.usc.edu/~helmy • Lab: nile.usc.edu • IMPORTANT Mobility tool: nile.usc.edu/important • Wireless/Mobile Network Library of Traces: nile.usc.edu/MobiLib
Ahmed Helmy - USC