1 / 36

Using Complex Networks for Mobility Modeling and Opportunistic Networking

Using Complex Networks for Mobility Modeling and Opportunistic Networking. Thrasyvoulos ( Akis ) Spyropoulos EURECOM. Traffic Regulation. Economy. Architecture/ Urban Planning. Gaming. Mobility : Many domains. Network Design. Public Transport Design. user on the phone.

weston
Download Presentation

Using Complex Networks for Mobility Modeling and Opportunistic Networking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Using Complex Networks for Mobility Modeling and Opportunistic Networking Thrasyvoulos (Akis) Spyropoulos EURECOM

  2. Traffic Regulation Economy Architecture/ Urban Planning Gaming Mobility: Manydomains Network Design Public Transport Design user on the phone

  3. Rationale for Mobility Models • Mobility Models are required for • Performance evaluation • Analytical • A system dynamics must be tractable in order to derive characteristics of interest • Simulations • Often used as an alternative when models are too complex (no analytical derivation) • But still complementary to the analytical approach • Trace-replaying and experiments • Solution design • Networking solutions should be designed according to their in situ environment (i.e., mobility context and characteristics)

  4. Mobile Switching Center VLR HLR Evaluation of Cellular Networks • Aim at providing integrated communications (i.e., voice, video, and data) between nomadic subscribers in a seamless fashion • Input • Arrival rate of new calls (traffic) • Arrival rate of handoffs (mobility) • Output • HLR load, probability of call rejection Mobile user will generate handoffs Keeps current User location Mobile user Generates new call

  5. ? Physical limits to capacity! The Mobile Internet ? Cost? • Exponential growth of demand in bandwidth • New technologies (LTE, other 4G) • Smaller cells (Femtocells) Cisco: Global Mobile Data Traffic 1018 Flash crowds Sparsely populated regions Natural disasters Connectivity anywhere, anytime is infeasible!

  6. Device-to-Device Communication (e.g. Bluetooth or WiFi Direct)

  7. D D D D D A C B D E F Data/Malware Spreading Over Opp. Nets • Contact Process: Due to node mobility • Q: How long until X% of nodes “infected”?

  8. Opportunistic networks • Connect devices to each other • Bluetooth, WiFi direct • It’s all about Contacts! • Opportunities to exchange data • How do we route messages (unicast/multicast)? • Whom do we trust? • Where do we place services?

  9. Understanding mobility is complex

  10. Outline • Motivation • Introduction to Mobility Modeling • Complex Network Analysis for Opportunistic Routing • Complex Network Properties of Human Mobility • Mobility Modeling using Complex Networks • Performance Analysis for Opportunistic Networks

  11. Classification of Mobility Models • Scale • Microscopic • accurately describes the motion of mobile individuals • Macroscopic • considers the displacement of mobile entities (e.g., pedestrians, vehicles, animals) at a coarse grain, for example in the context of large geographic areas such as adjacent regions or cells • Inputs • Standard Parameters: speed, direction, … • Additional Inputs: map, topology, preferred/popular locations… • Behavioral: intention, social relations, time-of-day schedule,… • Inherent Randomness: stochastic models (Markov, ODEs, Queuing)

  12. I. Random Waypoint (RWP) Model • A node chooses a random destination anywhere in the network field • The node moves towards that destination with a velocity chosen randomly from [0, Vmax] • After reaching the destination, the node stops for a duration defined by the “pause time” parameter. • 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, ...

  13. Random Way Point: Basics

  14. 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

  15. 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 (-)

  16. Random Walk

  17. Library C3 Office C2 Community-based Mobility (Spatial Preference) • Multiple Communities (house, office, library, cafeteria) • Time-dependency Rest of the network p23(i) p12(i) p32(i) p21(i) p11(i) House (C1) Community (e.g. house, campus)

  18. Microscopic Models Quickly Get VERY complicated!

  19. A Macroscopic Mob. Model for Cell. Networks • A simple handover model • cell -> state • need to find transition probabilities • depend on road structure, user profile, statistics 0.8 0.2 user on the phone 0.6 0.5 0.3 0.4 0.4 0.2 0.7 0.15 0.2 Cellular Network Markov Chain

  20. Opportunistic networks: Macroscopic View • Connect devices to each other • Bluetooth, WiFi direct • It’s all about Contacts! • Opportunities to exchange data • How do we route messages (unicast/multicast)? • Whom do we trust? • Where do we place services? Contacts are driven by our mobility in a social context!

  21. Outline • Motivation • Introduction to Mobility Modeling • Complex Network Analysis for Opportunistic Routing • Complex Network Properties of Human Mobility • Mobility Modeling using Complex Networks • Performance Analysis for Opportunistic Networks

  22. Opportunistic Routing: Contact Prediction • Human mobility not fully random: Patterns, Recurrence • e.g. recent Barabasi’s Nature papers • Human mobility is heterogeneous • Different neighbors, different numbers of neighbors • Infer Contact Pattern => Predict Future Contacts => Forward to node with Highest Delivery Probability • HOW??? • (maybe?) recent contact with X => high prob. of future contact with X [Lindgren et al. ’03, Dubois-Ferriere et al., ‘03] • (maybe?) frequent contact with X => high prob. of future contact with X [Burgess et al ’06] • (maybe?) many total contacts (with anyone) => high prob of future contact with any X [Spyropoulos et al. ’07, Erramilli et al. ‘08]

  23. The Contact Graph Routing? Protocolperformance? Trust? Content Placement?

  24. RoutingusingSocialNetwork Analysis (SNA) • SimBet [MobiHoc ‘07], Bubble Rap [MobiHoc ’08] • Represent Mobility using a Social/Complex Graph • Physics, Sociologydiscipline • studyof large graphs • scale-free, small-world, navigation, etc, • strong tiebetweennodes • social(friends) • geographic(familiarstrangers) aggregate ConceptualGraph Actual Graph (over time) time

  25. D SNA-based Forwarding (SimBet, BubbleRap) Look at graph; Forward IFF • relay in same community as D • ORrelay hashigher centrality D ? SNA-based Forwarding shows promising performance!

  26. Time-based Aggregation • (usedbySimBet, BubbleRap) • „Growing Time Window“: Edgesfor all contacts in [0, T] • „Sliding Time Window“: Edgesfor all contacts in [T-ΔT, T] Example: ETH trace, 20 nodes on onefloor ✗ ✔ ✗ ΔT = 2h ΔT = 72h ΔT = 1h ! ! Different networksneeddifferent time windows

  27. Density-basedAggregation • Density N: # nodes, E: edgesincluded • Easiertocomparebetweenscenarios (0 ≤ d ≤ 1) • How to „fill“ thesocialgraphtothisdensity? • „Most recent“: Create an edgeforthe x mostrecentcontacts • „Most frequent“: An edgeforthe x mostfrequentcontacts • Whatistherightdensity??? Aggregate to a certaindensityofthegraph

  28. Sensitivityof SNA-based Routing Performance ? ? • Sensitivityofroutingtographdensity • Goodgraphs => goodroutingperformance • Simulation usingSimBetand Bubble Rap • Syntheticcontactprocesses • Small-world, cavemen • Contacttraces • ETH (20 nodes, studentsandstaffworking on 1 floor) • INFO (41 Infocom 2005 participants) • MIT (97 studentsandstaff) How toevaluatethesocialgraphs

  29. Contribution 1 - Sensitivity of Routing Performance Bad performance in „extreme“ cases! There is an optimal density range!

  30. Contribution 2 - Online Algorithm Similarity(u,v) = Number of common neighbors of u and v • Assumption: Twotypesofcontacts • Regular, withnodesof same community -> highsimilarity • Random, withnodesof different communities -> lowsimiliarity • Wewant all regular links but norandom links => Predictive! ✔ ✗ ✗ ✔ ✗ Regular: low similarity Random: low similarity Regular: high similarity Random: low similarity Regular: high similarity Random: high similarity

  31. DoingtheMath • Based on cavemengraph model • Maximizeavgsimilarityof Regular links • Minimizeavgsimilarityof Random links MaximizeDifference

  32. Maximizing Modularity (I) d = 0.5 d = 0.01 d = 0.1 • Clustering to distinguish Random and Regular links • Synthetic models: 2-means clustering • Density with maximal cluster distance is optimal • Real world requires more robust solution cavemen model Histogram of similarity values between all pairs of nodes MIT

  33. Maximizing Modularity (II): Spectral Analysis • Arrange observed similarity values (si) into a matrix W • Spectral Graph Theory • Calculate Laplacian L of W • D: diagonal normalization matrix • Eigenvalue decomposition of L: • 1 =0 ≤2 ≤…≤n • 2 = 0 if two clusters are perfectly seperable (2 connected components) • 2(Algebraic Connectivity):smallforhighly modular data • Minimizing λ2 -> max. “distance“ between Regular and Random

  34. Maximizing Modularity (III) Minimum correlates with optimal density

  35. Performance of Online Algorithm (II) • Delivery ratio relative to Direct Transmission using • optimal fixed density / online algorithm Online Algorithm performance is close to optimal

  36. Opportunistic Routing Using SNA: Summary • Contribution 1- Sensitivityanalysis • Choosingtherightaggregationdensitymatters! • More thanspecificroutingalgorithm! • Contribution 2– Optimal densityalgorithm • Maximizemodularityofobservedsimilarityvalues • Spectral Graph Theorytechniques • Performance closetothatof optimal density Contacts Structure Mobility Predict ! ! General Applicability (not just routing)

More Related