1. Paradigm Shift in Protocol Design –May end up with suboptimal performance or failures due to lack of context in the design Design general purpose protocols Evaluate using models (random mobility, traffic, …) Modify to improve performance and failures for specific context Analyze, model deployment context Design ‘application class’-specific parameterized protocols Utilize insights from context analysis to fine-tune protocol parameters Used to: Propose to:

2. Problem Statement How to gain insight into deployment context? How to utilize insight to design future services? Approach Extensive trace-based analysis to identify dominant trends & characteristics Analyze user behavioral patterns –Individual user behavior and mobility –Collective user behavior: grouping, encounters Integrate findings in modeling and protocol design –I. User mobility modeling – II. Behavioral grouping –III. Information dissemination in mobile societies, profile-cast

3. The TRACE framework Trace Analyze Employ (Modeling & Protocol Design) Characterize (Cluster) Represent MobiLib

4. 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 –Tools for trace data mining Use insights to design future context-aware protocols? http://nile.cise.ufl.edu/MobiLib Trace

5. Libraries of Wireless Traces Multi-campus (community-wide) traces: –MobiLib (USC (04-06), now @ UFL) nile.cise.ufl.edu/MobiLib 15+ Traces from: USC, Dartmouth, MIT, UCSD, UCSB, UNC, UMass, GATech, Cambridge, UFL, … Tools for mobility modeling (IMPORTANT, TVC), data mining –CRAWDAD (Dartmouth) Types of traces: –University Campus (mainly WLANs) –Conference AP and encounter traces –Municipal (off-campus) wireless –Bus & vehicular wireless networks –Others … (on going) Trace

6. Wireless Networks and Mobility Measurements In our case studies we use WLAN traces –From University campuses & corporate networks (4 universities, 1 corporate network) –The largest data sets about wireless network users available to date (# users / lengths) –No bias: not “special-purpose”, data from all users in the network We also analyze –Vehicular movement trace (Cab-spotting) –Human encounter trace (at Infocom Conf) Trace

7. Case study I – Individual mobility

8. Case Study I: Goal To understand the mobility/usage pattern of individual wireless network users To observe how environments/user type/trace- collection techniques impact the observations To propose a realistic mobility model based on empirical observations –That is mathematically tractable –That is capable of characterizing multiple classes of mobility scenarios

9. IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis* * 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 - 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 EnvironmentCollection method Analyzed part MIT7/20/02 – 8/17/02 Generic3 corporate buildings PollingWhole trace Dartmouth4/01/01 – 6/30/04 Generic w/ subgroup University campus Event-basedJuly ’03 April ’04 UCSD9/22/02 – 12/8/02 PDA onlyUniversity campus Polling09/22/02- 10/21/02 USC4/20/05 – 3/31/06 GenericUniversity campus Event-based (Bldg) 04/20/05- 05/19/05 Understand changes of user association behavior w.r.t. –Time - Environment - Device type - Trace collection method

10. Metrics for Individual Mobility Analysis What kind of spatial preference do users exhibit? –The percentile of time spent at the most frequently visited locations What kind of temporal repetition do users exhibit? –The probability of re-appearance How often are the nodes present? –Percentage of “online” time Represent

11. 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. Users exhibit “on”/”off” behavior that needs to be modeled. Observations: Visited Access Points (APs) Prob.(coverage > x) Fraction of online time associated with the AP CCDF of coverage of users [percentage of visited APs] Average fraction of time a MN associates with APs

12. 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. Repetitive Behavior

13. Mobility Characteristics from WLANs Simple existing models are very different from the characteristics in WLAN Characterize Prob.(online time fraction > x) On/off activity pattern Skewed location preference Periodic re-appearance

14. Mobility Models Mobility models are of crucial importance for the evaluation of wireless mobile networks [IMP03] Requirements for mobility models –Realism (detailed behavior from traces) –Parameterized, tunable behavior –Mathematical tractability Related work on mobility modeling –Random models (Random walk/waypoint): inadequate for human mobility –Improved synthetic models (pathway model, RPGM, WWP, FWY, MH) – more realistic, difficult to analyze –Trace-based model (T/T++): trace-specific, not general

15. Skewed location visiting preference –Create “communities” to be the preferred area of movement –Each node can have its own community Node moves with two different epoch types – Local or roaming –Each epoch is a random-direction, straight-line movement –Local epochs in the community –Roaming epochs around the whole simulation area 25% 75% Employ Time-variant Community (TVC) Model (W. Hsu, Thyro, K. Psounis, A. Helmy, “Modeling Time-variant User Mobility in Wireless Mobile Networks”, IEEE INFOCOM, 2007, Trans. on Networking 2009)

16. Tiered Time-variant Community (TVC) Model Periodical re-appearance –Create structure in time – Periods –Node moves with different parameters in periods to capture time-dependent mobility –Repetitive structure Finer granularity in space & time –Multi-tier communities –Multiple time periods Employ

17. Using the TVC Model – Reproducing Mobility Characteristics (STEP1) Identify the popular locations; assign communities (STEP2) Assign parameters to the communities according to stats (STEP3) Add user on-off patterns (e.g., in WLAN, users are usually off when moving)

18. Using the TVC Model – Reproducing Mobility Characteristics WLAN trace (example: MIT trace) Skewed location visiting preferencePeriodic re-appearance * Model-simplified: single community per node. Model-complex: multiple communities ** Similar matches achieved for USC and Dartmouth traces

19. Using the TVC Model – Reproducing Mobility Characteristics Vehicular trace (Cab-spotting)

20. Using the TVC Model – Reproducing Mobility Characteristics Human encounter trace at a conference Inter-meeting timeEncounter duration time A encounters B Encounter duration Inter-meeting time

21. Case study II – Groups in WLAN

22. Case Study II: Goal Identify similar users (in terms of long run mobility preferences) from the diverse WLAN user population –Understand the constituents of the population –Identify potential groups for group-aware service In this case study we classify users based on their mobility trends (or location-visiting preferences) –We consider semester-long USC trace (spring 2006, 94days) and quarter-long Dartmouth trace (spring 2004, 61 days)

23. Representation of User Association Patterns We choose to represent summary of user association in each day by a single vector –a = {a j : fraction of online time user i spends at AP j on day d} Summarize the long-run mobility in an “association matrix” Represent -Office, 10AM -12PM -Library, 3PM – 4PM -Class, 6PM – 8PM Association vector: (library, office, class) =(0.2, 0.4, 0.4)

24. Eigen-behavior Eigen-behaviors: The vectors that describe the maximum remaining power in the association matrix (obtained through Singular Value Decompostion) with quantifiable importance Eigen-behavior Distance calculates similarity of users by weighted inner products of eigen-behaviors. – Assoc. patterns can be re-constructed with low rank & error Benefits: Reduced computation and noise

25. Similarity-based User Classification With the distance between users U and V defined as 1-Sim(U,V), we use hierarchical clustering to find similar user groups. USC Dartmouth * AMVD = Average Minimum Vector Distance

26. Validation of User Groups Significance of the groups – users in the same group are indeed much more similar to each other than randomly formed groups (0.93 v.s. 0.46 for USC, 0.91 v.s. 0.42 for Dartmouth) Uniqueness of the groups – the most important group eigen-behavior is important for its own group but not other groups Significance score of top eigen-behavior for USCDartmouth Its own group0.7790.727 Other groups0.0050.004

27. User Groups in WLAN - Observations Identified hundreds of distinct groups of similar users Skewed group size distribution – the largest 10 groups account for more than 30% of population on campus. Power-law distributed group sizes. Most groups can be described by a list of locations with a clear ordering of importance We also observe groups visiting multiple locations with similar importance – taking the most important location for each user is not sufficient

28. Case study III – Encounter Patterns

29. Case Study III: Goal Understand inter-node encounter patterns from a global perspective –How do we represent encounter patterns? –How do the encounter patterns influence network connectivity and communication protocols? Encounter definition: –In WLAN: When two mobile nodes access the same AP at the same time they have an ‘encounter’ –In DTN: When two mobile nodes move within communication range they have an ‘encounter’

30. Prob. (unique encounter fraction > x) Prob. (total encounter events > x) CCDF of unique encounter countCCDF 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 user population (except UCSD) The number of total encounters for the users follows a BiPareto distribution. Observations: Encounters

31. Encounter-Relationship (ER) graph Draw a link to connect a pair of nodes if they ever encounter with each other … Analyze the graph properties? Group of good friends… Cliques with random links to join them Represent

32. Av. Path Length Clustering Coefficient (CC) Small Worlds of Encounters Normalized CC and PL The encounter graph is a Small World graph (high CC, low PL) Even for short time period (1 day) its metrics (CC, PL) almost saturate Encounter graph: nodes as vertices and edges link all vertices that encounter Small World Random graph Regular graph

33. Background: Delay Tolerant Networks (DTN) DTNs are mobile networks with sparse, intermittent nodal connectivity Encounter events provide the communication opportunities among nodes Messages are stored and moved across the network with nodal mobility A B C

34. Information Diffusion in DTNs via Encounters Epidemic routing (spatio-temporal broadcast) achieves almost complete delivery Unreachable ratio (Fig: USC) Robust to selfish nodes (up to ~40%) Trace duration = 15 days Robust to the removal of short encounters

35. Top-ranked friends form cliques and low-ranked friends are key to provide random links (short cuts) to reduce the degree of separation in encounter graph. 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: 0.01, and 0.4

36. Profile-cast W. Hsu, D. Dutta, A. Helmy, ACM Mobicom 2007 Sending messages to others with similar behavior, without knowing their identity –Announcements to users with specific behavior V –Interest-based ads, similarity resource discovery Assuming DTN-like environment A B E C Is B similar to V? Is E similar to V? D ? Is C/D similar to V?

37. Profile-cast Use Cases Mobility-based profile-cast –Targeting group of users who move in a particular pattern (lost-and-found, context-aware messages, moviegoers) –Approach: use “similarity metric” between users Mobility-independent profile-cast –Targeting people with a certain characteristics independent of mobility (classic music lovers) –Approach: use “Small World” encounter patterns

38. Mobility-based Profile-cast Mobility space S D D Scoped message spread in the mobility space S D N N N N Forward??

39. –Singular value decomposition provides a summary of the matrix (A few eigen-behavior vectors are sufficient, e.g. for 99% of users at most 7 vectors describe 90% of power in the association matrix) Profile-cast Operation Profiling user mobility –The mobility of a node is represented by an association matrix S N N N N 1. profiling Each row represents an association vector for a time slot An entry represents the percentage of online time during time slot i at location j Sum. vectors

40. Profile-cast Operation 2. Forwarding decision S N N N N 1. profiling Determining user similarity –S sends Eigen behaviors for the virtual profile to N –N evaluated the similarity by weighted inner products of Eigen-behaviors –Message forwarded if Sim(U,V) is high (the goal is to deliver messages to nodes with similar profile) –Privacy conserving: N and S do not send information about their own behavior

41. Profile-cast Evaluation Epidemic: Near perfect delivery ratio, low delay, high overhead Centralized: Near perfect delivery ratio, low overhead, a bit extra delay Decentral: provides tradeoff between delivery & overhead Random: poor delivery ratio * Results presented as the ratio to epidemic routing Epidemic Decentral Random - Decentralized I-cast achieves: > 50% reduction in overhead of Epidemic >30% increase in delivery of Random

42. Evaluation - Result Success Rate Delay Overhead more overhead 92% 45% Centralized: Excellent success rate with only 3% overhead. Similarity-based: (1) 61% success rate at low overhead, 92% success rate at 45% overhead (2) A flexible success rate – overhead tradeoff RTx with infinite TTL: Much more overhead undersimilar success rate Short RTx with many copies: Good success rate/overhead, but delay is still long

43. S Mobility Profile-cast (intra-group) Goal S Flooding S Flood-sim S Single long random walk S Multiple short random walks

44. Mobility Profile-cast (inter-group) S T.P. S GoalFlooding S T.P. Gradient-ascend S T.P. Single long random walk S T.P. Multiple short random walks S T.P. Flooding_sim

45. Performance Comparison Gradient ascend helps to overcome the difficult case – when the source is far from T.P. Few long RW is better when S is far from T.P. but many short RW is better when S is close to T.P.

46. Performance Comparison Gradient ascend helps to overcome the difficult case – when the source is far from T.P. Few long RW is better when S is close toT.P. but many short RW is better when S is close to T.P. Gradient ascend has some extra delay compared with flooding

47. Profile-cast Initial Results Adjustable overhead/delivery rate tradeoff –61% delivery rate of flooding with 3% overhead –92% delivery rate with 45% overhead Better than single random walk in terms of delay, delivery rate Multiple short random walks also work well in this case

48. Mobility Independent Profile-cast SSSSS GoalFloodingSmallWorld-based Single long random walkMultiple short random walks

49. Future Work Sending to a mobility profile specified by the sender –Gradient ascend followed by similarity comparison (in the mobility space) Mobility independent profile-cast –The encounter pattern provides a network in which most nodes are reachable –We don’t want to flood – How to leverage the Small World encounter pattern to reach the “neighborhood” of most nodes efficiently?

50. Future Work –One-copy-per-clique in the “mobility space” –We expect this to work because similarity in mobility leads to frequent encounters

51. Future Directions (Applications) Detect abnormal user behavior & access patterns based on previous profiles Behavior aware push/caching services (targeted ads, events of interest, announcements) Caching based on behavioral prediction Can/should we extend this paradigm to include social aspects (trust, friendship, …)? Privacy issues and mobile k-anonymity

52. Markov O(2) Predictor Accuracy VoIP User Prediction Accuracy -VoIP users are highly mobile and exhibit dramatic difference in behavior than WLAN users -Prediction accuracy drops from ave ~62% for WLAN users to below 25% for VoIP users On Mobility & Predictability of VoIP & WLAN Users J. Kim, Y. Du, M. Chen, A. Helmy, Crawdad 2007 Work in-progress Motivates -Revisiting mobility modeling -Revisiting mobility prediction

53. visitors Males Females University Campus Fraternity Sorority traces Gender-based feature analysis in Campus-wide WLANs U. Kumar, N. Yadav, A. Helmy, Mobicom 2007, Crawdad 2007 - Able to classify users by gender using knowledge of campus map -Users exhibit distinct on-line behavior, preference of device and mobility based on gender -On-going Work -How much more can we know? -What is the “information-privacy trade-off”?

54. Real world group experiments (structural health monitoring) sensor Instructor WLAN/adhoc sensor-adhoc Multi-party conference Tele-collaboration tools WLAN/adhoc Embedded sensor network The Next Generation (Boundless) Classroom Students -Integration of wired Internet, WLANs, Adhoc Mobile and Sensor Networks -Will this paradigm provide better learning experience for the students? Challenges

55. Emerging Wireless & Multimedia Technologies Protocols, Applications, Services Human Behavior Mobility, Load Dynamics Future Directions: Technology- Human Interaction The Next Generation Classroom

56. Human Behavior Mobility, Load Dynamics Protocols, Applications, Services Emerging Wireless & Multimedia Technologies Human Computer Interaction (HCI) & User Interface Educational/ Learning Experience Education Psycology Cognitive Sciences Mobility Models Traffic Models Protocol Design Context-aware Networking Engineering Application Development Service Provisioning Multi-Disciplinary Research Measurements How to Evaluate? How to Capture? How to Design? Social Sciences

57. sensor Disaster Relief (Self-Configuring) Networks

58. On-going and Future Directions Utilizing mobility –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: self-configuring, mobility-adaptive protocols Socially-aware protocols: security, trust, friendship, associations, small worlds –On-going Projects Next Generation (Boundless) Classroom Disaster Relief Self-configuring Survivable Networks

59. Thank you! Ahmed Helmy helmy@ufl.eduhelmy@ufl.edu URL: www.cise.ufl.edu/~helmy MobiLib: nile.cise.ufl.edu/MobiLib