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Routing in Disruption-Tolerant Networks. Katia Obraczka University of California, Santa Cruz katia@soe.ucsc.edu http://inrg.cse.ucsc.edu/. What are Disruption-Tolerant Networks?. Disruption-tolerant networks or DTNs. A.k.a, Delay-tolerant, Episodically-connected,
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Routing in Disruption-Tolerant Networks Katia Obraczka University of California, Santa Cruz katia@soe.ucsc.edu http://inrg.cse.ucsc.edu/
What are Disruption-Tolerant Networks? • Disruption-tolerant networks or DTNs. • A.k.a, • Delay-tolerant, • Episodically-connected, • Intermittently-connected networks. Networks where end-to-end connectivity is NOT guaranteed.
DTN Applications:Environmental Monitoring CARNIVORES Project at UCSC: Monitoring coyotes in the Santa Cruz mountains.
DTN Applications:Connecting Remote Communities • Rural “kiosks”: • Shared among locals. • Selling/buying agricultural products. • Banking and other transactions.
X X X path disruption! X path disruption! node link DTNs Regular Network DTN End-to-end path S D Connectivity disruption due to: • Wireless propagation (fading, shadowing, etc.) • Duty cycling for power conservation. • Mobility.
What’s the big deal? • Routing protocols have always assumed end-to-end connectivity. • Table-driven (proactive) protocols (e.g., Internet routing) can recover from infrequent topology changes. • On-demand (reactive) protocols (e.g., MANET routing) can recover from frequent but short-lived outages. • But what if “outages” are frequent and long-lived? • “Traditional” routing simply drops packets!
DTN’s Routing Paradigm Shift • Before DTNs: • Space dependency. • Network routing: given graph G(V,E), find shortest path between source-destination. • Store-and-forward routing. • After DTNs: • Space and time dependency. • Network as a time-varying graph G(V,E(t)). • Links are a function of time. • Links as “contacts”. • Store-carry-and-forward routing.
Types of Contacts • Scheduled contacts • E.g. satellite links, message ferry. • All info known. • Probabilistic contacts • Statistics about contacts known. • E.g., bus, sensors with random wake-up schedule. • Opportunistic contacts • Not known a priori. • E.g., tourist car that happens to drive by.
Designing DTN Routing/Forwarding Protocols • What information is available? • Oracles. • Contacts, contact statistics, queuing, traffic, buffer capacity, etc. • None. • How much information is known? • No knowledge. • Partial knowledge. • Complete knowledge. • Trade-offs?
Routing/Forwarding under Intermittent Connectivity Scheduled/, (partially) known contacts (e.g., buses). Enforced contacts with specialized nodes (e.g., ferries). What about unknown contacts? • Contacts not known in advance. • No specialized nodes. i.e., only mobility of the nodes themselves is available. Opportunistic (mobility-assisted) routing
Our Focus: Opportunistic Routing Generality and simplicity: no knowledge assumed. • Opportunistic routing paradigm: • At every hop, node decides whether to: • Forward and/or • Store-and-carry. • Store-carry-and-forward.
Current Research • Two thrusts: • Utility-based controlled replication in heterogeneous DTNs. • Steward-Assisted Routing (StAR) • Routing framework that works wellin both connected networks and networks prone to frequent, long-lived disconnections.
Utility-Based Replication in Heterogeneous DTNs • In collaboration with Akis Spyropoulos and Thierry Turletti, INRIA Sophia-Antipolis. • 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. • T. Spyropoulos, T. Turletti, and K. Obraczka "Routing in Delay-Tolerant Networks Comprising Heterogeneous Node Populations”, in preparation.
Opportunistic Routing • Epidemic Routing: everyone gets a copy (Vahdat et al. ‘00). (+) Fast (for low traffic loads!). (-)Too many resources! • Controlled Replication: give a copy to only L nodes (Small et al. ’05, Spyropoulos et al. ’05). (+) Control transmissions/energy/buffer space per message! (+) few copies (<10% of nodes) give good performance (not always!) (-) Replication is greedy: blindly choose relays!
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
Heterogeneous Networks Base Stations or Static Sensors Roam around network (infrequent) stay inside community Community (local) Nodes Fast/Mobile Nodes
Roaming Node Good Relay! No more copies! Move “locally” Src Greedy Replication in Heterogeneous Environments 3 copies • If p% of the nodes are “useless” (local): • Delay increase ≈ c1 + c2/(1-p) (c1, c2 constants, andc1 +c2 = 1) compared to homogeneous case. Dst
Goal: Discover “Best” Relays How? Maintain some utility function. • What does “better” mean? • 2 types of utility function: • Destination-Dependent (DD) Utility: “Node X is a good next hop for destination D”. • E.g., friendship with D, proximity to D, etc. • Destination-Independent (DI) Utility: “Node X is a good next hop for all destinations”. • E.g., moves more frequently/faster around the network, is a “social hub”, has higher resources (e.g. vehicle with no energy limitations), etc.
What to Do With Utility? Approach: Give L copies to best relays: (+) Control resources: IMPORTANT! (e.g., scarce resources like battery power)
D D D S D A B Utility-based (“Smart”) Replication • Message from S to D. • S starts with 1 message copy and L “fwd tokens”. • UX(Y): Utility of node X for destination Y. n>1tokens n/2 n/2 Ltokens Option 1) If UB(D) > UA(D) (relative utility) Option 2) If UB(D) > Uth (absolute utility)
Last Seen First (LSF) Replication • Age-of-last-encounter timer: “I last saw node D less than 10 minutes ago”. • tX(Y): last time X saw Y. • UX(Y) = 1/(tX(Y)+1) • If my timer is smaller, then I’m a better relay for that destination. • DD utility.
more copies => less costly mistakes Simulation Results (LSF) • Community-based Mobility (Infocom’07) • Four types of nodes (100 total) • Community nodes (40%), Local nodes (40%) • Roaming nodes (10%), Static nodes (10%)
Vehicle Vehicle Pedestrian Pedestrian Base Station Sensor Most Mobile First (MMF) Replication • Utility of X = Label of node X. • DI. • Preference Order (): Label1 Label2… LabelN. • E.g. based on statistical properties or characteristics. • Order may also depend on destination’s label. Community
Simulation Results –MMF (1) • Same scenario as before • 4 types of nodes • Labels: “roam” “community” ”local” “static” • MMF1: give only to {“roam” || “community”} • MMF2: {“roam” || “community”} && {Prob{roam} > 0.15}
few “good” options => mistakes cost! many “good” options => fewer mistakes by “greedy” Simulation Results –MMF (2) • 2 types of nodes: “mobile” and “static” • Algorithm: give only to “mobile” • Nodes = 100
Most Social First (MSF) Algorithm • Mobility statistics not always available or dynamic (e.g. node in car, then in office) • Estimate node “sociability” online • tn = [(n-1)T, nT] (nth time window – duration T) • Ni(n): set of nodes seen by node i during tn • Si(n) = |Ni(n)|/T: sociability of i during tn • Running average: • Utility of node i: Ui =
Tuning MSF’s Parameters • Can past predict future? • How do we set T (window) and a (weight of new sample)? • Depends on (i) mobility/interaction patterns, (ii) horizon of prediction • time-homogeneous: past reliable predictor! • periodic in T*: set T to T* • shorter scale prediction: smaller T, larger a, higher moments • Number of copies: find lower bound given target delivery ratio and message TTL.
Simulation Results – MSF • 2 types of nodes: “mobile”, “static” • MMF: knows “mobile” labels beforehand • MSF: identifies “mobile” nodes online. Same performance! • T = 1000, a = 0.8, L = 10
Conclusion • Utility-based vs. Greedy Replication in Heterogeneous Networks: up to 4-5x improvement • Few “good” options => bigger gains. • Small budget of copies => bigger gains. • MSF: Generic, adaptive algorithm to discover “social” nodes. • Can be a building block for more complex schemes.
Ongoing and Future Directions • Modeling encounter-based (epidemic) protocols in heterogeneous environments. • Multiple classes of nodes. • Different mixing characteristics. • Fluid model approximation.
Ongoing and Future Directions (Cont’d) • Trace-based evaluation of Smart Replication. • E.g., National University of Singapore class schedules. • Hybrid DI/DD utility functions; more sophisticated sociability estimators.
Steward-Assisted Routing (StAR) • Routing framework that performs wellin both connected networks as well as networks prone to frequent, long-lived disconnections.
StAR • In collaboration with Jay Boice (UCSC MSc, May 2007) and J.J. Garcia-Luna. • J. Boice, J.J. Garcia-Luna Aceves, K. Obraczka, ``Disruption-Tolerant Routing with Scoped Propagation of Control Information'', in Proceedings of the IEEE International Conference on Communications (ICC) 2007. • J. Boice, J.J. Garcia-Luna Aceves, and K. Obraczka, ``On-Demand Routing in Disrupted Environments'', BEST PAPER AWARD, in Proceedings of the IFIP/TC6 Networking 2007. • J.J Garcia-Luna Aceves, K. Obraczka, and J. Boice, “An On-Demand Routing Framework for Disruption-Tolerant Networks”, under submission.
StAR Highlights • Combines on-demand (intra-partition) with opportunistic (inter-partition) routing. • Use of relays, or stewards, to deliver data to partitioned destinations. • No a-priori topological knowledge. • Use past connectivity information to predict future communication opportunities. • Scopes temporal and spatial dissemination of routing information.
SCIP • Scoped Contact and Interest Propagation. • Limits scope of routing information. • Nodes get routing info for destinations of interest. • Nodes only keep info for d if they are on the path from s to d. Example: s1 and s2 interested in d.
Steward Selection • Steward selected for given destination. • Use sequence numbers and number of hops to select local steward for destination d. • Steward has most recent sequence number. • If sequence numbers are equal, choose node with lowest number of hops to destination. Example: one steward per destination per partition.
Well-Connected Topologies • 100 nodes in 3600x500m area with full connectivity. • Static and random waypoint mobility. • Comparison against AODV and OLSR. StAR performs well with full connectivity and under short-lived disconnections.
As Connectivity Decreases… PDR Epidemic StAR • Gridded mobility. • Decrease connectivity by increasing grid dimension. AODV Decreasing connectivity
StAR Experiments: Special Operation Scenarios • Real experiments with StAR on testbed with static nodes and mobile robots. • Collaboration with Prof. Weitzenfeld’s robotics lab at ITAM, Mexico. • K. Obraczka, J. Boice, L. Martínez-Gómez, J. P. Francois, A. Levin-Pick, A. Weitzenfeld, “StAR: Ad-Hoc Wireless Networking for Autonomous Multi-Robot Coordination”, to appear in the Proceedings of the 1st. IEEE/ACM Robocom, October 2007.
StAR Testbed Eagle Knights modified robot with local camera and 802.11 communication capabilities. The original robot architecture is extended with: (1) the Crossbow Stargate managing wireless communication and local vision and (2) a webcam for sensing.
Results TABLE II Performance of AODV and StAR in Topoloy 2 Image Deliveries DeliveryRatio AODV 48 51.11% StAR 90 100.00% Topology 2: Static sensors with mobile intermediate node. Sensor 4 sends images to sink node 7 through intermediate mobile node 2 and static node 1.
Summary • StAR as routing framework that operates well in both well-connected networks as well as networks prone to episodic connectivity. • No a-priori knowledge, e.g., node schedules, location, etc. • Combines on-demand (intra-partition) with opportunistic (inter-partition) routing. • Use of relays, or stewards, to deliver data to partitioned destinations.
Future Work • Use other sources of information to improve performance. • Full/partial node schedules, GPS, etc. • Investigate other metrics for steward selection. • Explore different message replication strategies. • Integrate with work on utility-based opportunistic routing.
Energy-Efficient Medium Access (Task 4) • Novel efficient and flexible medium access framework form MANETs. • With J.J. Garcia-Luna and Venkatesh Rajendran (UCSC PHD, May 2007). • V. Rajendran, K. Obraczka and J.J. Garcia-Luna, ``A DYNAmic Multi-channel Medium Access Framework for Wireless Ad Hoc Networks'', BEST PAPER AWARD in the Proceedings of the 4th. IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) 2007. • V. Rajendran, Medium Access Control Protocols for MANETs, PhD Dissertation, UCSC, 2007. • V. Rajendran, K. Obraczka and J.J. Garcia-Luna, Application-Aware Medium Access for Sensor Networks, 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), November 2005.
Energy Consumption Modeling and Prediction (Task 4) • Models for energy consumption and network lifetime prediction. • Collaboration with Prof. R. Manduchi, UCSC CE. • C. Margi, Energy Consumption Trade-Offs in Power-Constrained Networks, PhD Dissertation, UCSC, 2006. • C. Margi, K. Obraczka, R. Manduchi, Characterizing System Level Energy Consumption in Mobile Computing Platforms, IEEE WirelessCom 2005, June 13-16, 2005 • C. Margi, V. Petkov, K. Obraczka, R. Manduchi Characterizing Energy Consumption in a Visual Sensor Network Testbed, IEEE/Create-Net TridentCom 2006, March 1-3, 2006. • Energy Consumption Trade-offs in Visual Sensor Networks”. C. B. Margi, R. Manduchi , K. Obraczka. SBRC 2006, May 29 - June 02, 2006.
Mobility Models for Wireless Networks (Task 1) • First Statistical-Equivalent Model (SEM) to characterize random waypoint mobility. • Collaboration with Profs. B. Sanso and A. Kottas, UCSC Applied Math, and K. Viswanath (NTT Labs). • K. Viswanath, A. Kottas, B. Sanso, and K. Obraczka, ``Statistical Equivalent Models for Computer Simulators'', in press to appear in the special issue of Simulation: Transactions of the Society for Modeling and Simulation International on ''Advances in Performance Evaluation of Computer and Telecommunication Systems”. • K. Viswanath, K. Obraczka, A. Kottas, B. Sanso, A Statistical Equivalent Model for Random Waypoint Mobility: A Case Study, IEEE SMC SPECTS 2006. • K. Viswanath and K. Obraczka, Modeling the Performance of Flooding in MANETs (Extended Version), Computer Communications Journal (CCJ) 2005.
Robust Routing for Network Fault-Tolerance and Security (Task 6) • Novel game-theoretic stochastic routing framework as proactive alternative to today's reactive approaches to route repair. • Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA. • S. Bohacek, J.P. Hespanha, C. Lim, and K. Obraczka, ``Game Theoretic Stochastic Routing for Fault Tolerance on Computer Networks'', in press to appear in the IEEE Transactions Parallel and Distributed Systems. • C. Lim, Scalable Multi-path Routing for Robust Communication, PhD Dissertation, USC, 2006. • G. Huang, Robust and Secure Routing in MANETs, MSc Theis, UCSC, 2006. • C. Lim, S. Bohacek, J. Hespanha and K. Obraczka, Hierarchical Max-Flow Routing, IEEE Globecom 2005. • S. Bohacek, J. Hespanha, J. Lee, C. Lim and K. Obraczka, A New TCP for Persistent Packet Reordering, IEEE/ACM Transactions on Networking, Vol. 14, No.2, April 2006. • R. Guru, G. Huang and K. Obraczka, An Integrated and Flexible Approach to Robust and Secure Routing in MANETs, IEEE IC3N, August 2005.