COMMUNITY: Comm unication in Heterogeneo u s N etworks Prone to Ep i sodic Connectivi ty

1. COMMUNITY: Communication in Heterogeneous Networks Prone to Episodic Connectivity Katia Obraczka University of California, Santa Cruz katia@soe.ucsc.edu http://inrg.cse.ucsc.edu/

2. The Vision: The Internet of the Future

3. Current Research • Routing in Heterogeneous Delay-Tolerant Networks. • Message Delivery in Heterogeneous Networks with Episodic Connectivity. • Mobility Inference for Efficient DTN Routing. • Traffic Forecasting for Efficient Scheduled-Access Medium Access in Self-Organizing Wireless Networks.

4. Routing in Heterogeneous Delay-Tolerant Networks • Joint work with Akis Spyropoulos (ETH) and Thierry Turletti (INRIA). • T. Spyropoulos, T. Turletti, and K. Obraczka "Routing in Delay-Tolerant Networks Comprising Heterogeneous Node Populations”, to appear in the IEEE Transactions on Mobile Computing (TMC), 2008. • T. Spyropoulos, T. Turletti, and K. Obraczka, ``Utility-based Message Replication for Intermittently Connected Heterogeneous Networks'', in the Proceedings of the 1st. IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC) 2007.

5. Starting Point: Opportunistic Routing Generality and simplicity: no knowledge assumed. • Opportunistic routing paradigm: • At every hop, node decides whether to: • Replicate, forward and/or • Store-and-carry. • Store-carry-and-forward.

6. Opportunistic Routing • Epidemic Routing [Vahdat et al. ’00] Uncontrolled replication. At every encounter, give message copy to peer. (+) Fast (for low traffic loads!). (-)Too many resources! • Controlled Replication[Small et al. ’05, Spyropoulos et al. ’05] E.g., give copy to only L nodes. (+) Control transmissions/energy/buffer space per message! (+) Few copies may yield good performance but not always. (-) Greedy replication: blindly choose relays!

7. Covered by Relay 1 Greedy Replication: A Good Scenario Covered by relay 2 2 copies 1 12 D 13 S 14 2 16 11 15 3 7 8 5 10 4 9 Relays are highly mobile Relays routes are uncorrelated Nodes have homogeneous behavior 6

8. Heterogeneous Networks Base Stations or Static Sensors Roam around network (infrequent) stay inside community Community (local) Nodes Fast/Mobile Nodes

9. Roaming Node Good Relay! No more copies! Move “locally” Src Greedy Replication in Heterogeneous Environments 3 copies • If p% of the nodes are “local”: • Compared to homogeneous case, delay increase ≈ c1 + (c2/(1-p)), where c1, c2 constants, andc1 +c2 = 1. Dst

10. How to Discover “Best” Relays Maintain utility function:What does good relay mean? Approach: Give L copies tobest relays: (+) Control overhead (e.g., scarce resources like memory, battery power, etc). • 2 types of utility function: • Destination-Dependent (DD) Utility. • Destination-Independent (DI) Utility.

11. DD Utility “Node X is good relay for destination D”. • Example: Last Seen First (LSF). • A.k.a, “age of last encounter”. • If UX(Y) is the utility of node X for destination Y, then under LSF: • UX(Y) = 1/(tX(Y)+1),wheretX(Y) denotes last time X saw Y. • If X’s is smaller, then X is a better relay for that destination.

12. DI Utility “Node X is good relay for all destinations”. • Examples: • Most Mobile First (MMF). • Most Social First (MSF). • Utility can be pre-assigned or computed on-line.

13. Vehicle Vehicle Pedestrian Pedestrian Base Station Sensor Most Mobile First (MMF) • Utility of node X = Label of X. • Pre-defined preference order (): • E.g., Label1 Label2…  LabelN. Community

14. Most Social First (MSF) • Label assignment not always possible and/or utility varies over time. • Estimate utility online. • tn = [(n-1)T, nT], nth time window with duration T. • Ni(n): set of nodes seen by node i during tn. • Sociability of i during tn is Si(n) = |Ni(n)|/T. • Utility of node i (for any destination j) is:

15. Evaluation • Simulation experiments using custom discrete-event simulator (available from http://people.ee.eth.ch/spyropot/dtnsim.html). • Synthetic mobility and real mobility traces. • E.g., DieselNet with 24 buses running in Amherst.

16. Selected Results LSF Replication

17. Other Contributions • Developed fluid-based model to analyze performance of opportunistic routing strategies in heterogeneous networks. • E.g., epidemic, controlled replication (greedy and utility-based). • Good matching between analytical and simulation results.

18. Summary • Utility-based opportunistic routing (replication) in heterogeneous DTNs. • Considerable gains in performance. • Different utility functions. • DD. • DI. • Fluid-based models to analyze opportunistic routing in heterogeneous DTNs.

19. Message Delivery in Heterogeneous Networks with Episodic Connectivity • Joint work with Naveed Rais (PhD candidate, INRIA) and Thierry Turletti (INRIA). • N.B. Rais, T. Turletti, and K. Obraczka, ``Coping with Episodic Connectivity in Heterogeneous Networks'', to appear in the ACM Annual International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) 2008.

20. Motivation • To-date, no comprehensive message delivery solution for heterogeneous environments prone to disconnections. Example of a heterogeneous internetwork.

21. Current Solutions • Typically address subsets of the problem by: • Extending MANETs to handle episodic connectivity. • Using special-purpose gateways to connect MANETs to the Internet, • Or using special-purpose nodes to provide address the intermittent connectivity problem. • Handling heterogeneity at higher layers (e.g., Bundle architecture).

22. Our Solution: MeDeHa • Message Delivery in Heterogeneous, Disruption-Prone Networks. • Features: • Message relaying by any node. • Buffering (at layer 2). • Topology and content information exchange. • Traffic differentiation and QoS through message tags.

23. MeDeHa Operation

24. MeDeHa: Current Implementation • Message delivery in internets consisting of infrastructure-based wireless networks whose nodes can get disconnected.

25. Performance Evaluation • Simulations using OMNET++. • Simulator needed to be extended to support explicit disassociation, handoff, and buffering.

26. Selected Results Non-uniform deployment of 9 APs

27. Mobility Inference for Efficient DTN Routing Joint work with Matt Bromage (PhD student, UCSC)

28. Main Idea • Capture mobility patterns in node movement. • Use resulting information to make better decisions in DTN routing. • E.g., finding good relays.

29. Mobility Inference Tool • Tool works in both Qualnet and stand-alone (Linux OS). • Finds and maps “structure” in mobility when present. • Structure is any routinely followed path • Builds library of commonly followed paths over time. • Path information used to make routing decisions. • How to define a path? • Series of sampled locations from source to destination.

30. Path 1 Path 2 Path 3 … MIE Flowchart Current Location • Node location sampled periodically. • MIE determines if node is building a new path or following an existing one. • Each node maintains a table of paths and info about each path (e.g., distance, time, etc). MIE Path Library

31. Path Library Path 1 Path 2 Path 3 … Applications to DTN Routing Node Encounter • Mobility patterns used to determine if a node is a good relay. • Based on path information one can determine: • How close you can get to the destination. • What the probability is and how long it will take to get there. • Example: greedy forwarding. Destination Routing Decision Forward Replicate Nothing

32. Traffic Forecasting for Efficient Scheduled-Access Medium Access in Self-Organizing Wireless Networks • Joint work with Vladi Petkov (PhD candidate, UCSC)

33. Why Scheduled Access? • Random-access MACs have known problems: • At high loads, inefficient use of channel due to collisions. • Resource wastefule (e.g., energy). • Scheduled-access MACs: • Collision-freedom: efficient channel use. • Energy efficiency.

34. But… • Scheduled-access MACs suffer from inherent delay. • They are reactive! Can we make them reactive???

35. Approach: Traffic Forecasting • Is network traffic forecastable? • Can we use forecasting to address schedule-based MACs inherent latency?

36. Expected Contributions Pioneering the use of traffic forecasting in scheduled-access MACs. • Analysis of performance benefits of prediction for scheduled-access MACs.on • Comparison of a number of different approaches to traffic forecasting.

37. How can forecasting help? • Traffic adaptive scheduled-access MACs must exchange traffic information. • With traffic forecasting, nodes can exchange traffic information ahead of time!

38. How much can it help? Considerable improvements at lower loads! Comparable to “traditional” scheduled-access approaches at higher loads.

39. Impact on Energy Consumption Slight energy consumption reduction. More significant at higher loads.

40. Is Network Traffic Predictable?

41. Is Network Traffic Predictable? Real-time audio (skype): packet size trace

42. Is Network Traffic Predictable? Real-time audio (skype): packet inter-arrival time trace

43. Proposed Approach • Machine-learning based experts framework. • Experts assigned to each output. • Weights assigned to experts and reduced depending on expert’s output. • Input is packet inter-arrival time. • Output is slot period within scheduling period.

44. Forecaster Output

45. Delay Improvements

46. Efficiency

47. Future Work • Further investigation into benefits of forecasting. • What if your forecast is not perfect? • When does it not make sense to do it? • More interesting/realistic traffic patterns. • Aggregates. • What applications will benefit? • Traffic (pattern, persistence). • Topology.