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Tutorial Mobility Modeling for Design and Analysis of Ad Hoc Wireless Networks

This tutorial explores various modeling techniques for designing and analyzing ad hoc wireless networks, including geographic services, robust geographic routing, and behavior modeling. It also discusses the impact of mobility on network performance.

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Tutorial Mobility Modeling for Design and Analysis of Ad Hoc Wireless Networks

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  1. TutorialMobility 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 theNOMADS research group

  2. 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

  3. 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.

  4. Motivation Geographic Restriction • Randomized models (e.g., random waypoint) do not capture • (I) Existence of geographic restriction (obstacles) • (II) Temporal dependence of node movement (correlation over history) • (III) Spatial dependence (correlation) of movement among nodes • 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? Mobility Space Spatial Correlation Temporal Correlation

  5. Connectivity Graph Random Waypoint Group Mobility Freeway Mobility Manhattan Mobility Contraction/Expansion Hybrid Trace-driven DSR AODV DSDV GPSR GLS ZRP Building Block Analysis Performance Metrics Connectivity Metrics Mobility Metrics Flooding Caching Error Detection Error Notification Error Handling Throughput Overhead Success rate Wasted Bandwidth Relative Speed Spatial Dependence Temporal Dependence Node Degree/Clustering Link Duration Path Duration Encounter Ratio The IMPORTANT Framework Overview Routing Protocol Performance Mobility Models

  6. Random Way Point (RWP) Group Mobility (RPGM) Freeway (FW) Manhattan (MH) member member Leader IMPORTANT Mobility Models

  7. Rich Coverage of Mobility & Connectivity Dimensions High High Med Med Low Low Relative Speed Link Duration High High Med Med Low Low Spatial Dependence PATH Duration

  8. Whether Mobility Matters? 43% DSR Throughput across Mobility DSR Overhead across Mobility Manhattan : AODV is best Random Waypoint : DSR is best x10

  9. Relative Velocity Putting the Pieces Together Link Duration Throughput Spatial Dependence Path Duration Overhead Why Mobility Matters?

  10. 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.

  11. Nodes in different groups RPGM w/ 4 groups Vmax=5m/s R=250m Nodes moving in opposite directions FW model Vmax=5m/s R=250m 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

  12. RW RPGM (4 groups) Vmax=30m/s R=250m FW Link Duration at high speeds > 10m/s Not Exponential !!

  13. RW RPGM4 h=2 h=4 100 Vmax=30m/s R=250m FW h=4 Path Duration (PD) distribution for long paths ( 2 hops) at high speeds (> 10m/s) Exponential !!

  14. Conclusions • Mobility patterns are very IMPORTANTin 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

  15. 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.

  16. A C D B E F S Time: TA(D)=t1 Location: LA(D)=x1,y1 Time: TE(D)=t3 Location: LE(D)=x3,y3 Time: TF(D)=t4 Location: LF(D)=x4,y4 Time: TC(D)=t2 Location: LC(D)=x2,y2 Basic Operation of MAID: Encounter history, search and age gradient tree

  17. 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 S S A1 TA(D): The Age for A’s last encounter with D A2 A3 A4 A5 A6 A7 D D

  18. Transient State MAID protocol phases and metrics • 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

  19. Warm Up Phase The Warm Up Time depends heavily on the Mobility model and the Velocity

  20. 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.

  21. Spatio-Temporal Correlations in the AGT 400 nodes 3000mx3000m area Radio range 250m RWK RWP S V=10m/s A B C D MH RPGM (80grps)

  22. RWK RWP V=30m/s MH RPGM (80grps)

  23. RWK RWP V=50m/s MH RPGM (80grps)

  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 • 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. Observations: Visited Access Points (APs) Fraction of online time associated with the AP Prob.(coverage > x) 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. Observations: Encounters Prob. (unique encounter fraction > x) Prob. (total encounter events > x) 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 averageof 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 Random Graph - Low path length, - Low clustering - 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. Clustering Coefficient Small Worlds of Encounters • ‘Encounter’: When 2 nodes access the same AP at the same time • Encounter graph: nodes as vertices and edges link all vertices that encounter Normalized CC and PL 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 • Tracers trap MAC addresses accessing the WLAN - Building level granularity Wireless Network Coverage Map at USC - main campus

  41. USC MAP WITH OBSERVATION LOCATIONS Statistics about recorded mobility traces used in this study Partial Recorded Data and example

  42. Distributions 1000 800 #of people 600 observed at 400 200 S4 0 time slots LVL S1 JEP KOH OHE PED TOMMY Series1 CARLS JR. Series2 Observation Location Series3 Series4 # of access Observations vs. WLAN 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 WLAN access traces Observation traces

  43. classroom Library Off-campus cafeteria Other areaon campus Survey based: Weighted Way Point (WWP) Model

  44. Vision:Community-wide Wireless/Mobility Library • Library of • measurements from Universities, vehicular networks • realisticmodels 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 Traffic , Application Model Cooperation , Trust , Security Model Cooperation , Trust , Security Model Wireless Channel Model Wireless Channel Model Device On/Off Model Device On/Off Model IMPACT Technology Penetration,Deployment Model Technology Penetration,Deployment Model Mobility 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. IMPORTANT Manhattan Freeway Group RWP

  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

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