Probabilistic Routing Schemes for Ad-Hoc Opportunistic Networks

1. Routing in Opportunistic Networks Chapter 8: Probabilistic Routing Schemes for Ad-Hoc Opportunistic Networks 1Vangelis Angelakis, 2Elias Tragos, 3George Perantinos, and 1Di Yuan 1 Linköping University, Sweden 2 Foundation for Research and Technology –Hellas 3 Forthnet S.A.

2. Wireless proliferation • Wireless RF Proliferation in the past decades • Bluetooth, 802.11a,b/g, 3/4G • Computing paradigms based on Wireless • Wireless Cloud • Internet of Things • Machine-to-Machine (ad-hoc) communication • Wireless medium backlashes • Range issues • Interference / Communication reliability

3. Relaying and forwarding • Transmission range limitations -> need for relays • Key decisions in forwarding packets: 1. Whatto send (my packet or a relayed packet ?) 2.Towhom(to a relay or the destination ?) 3.Whento do so ( will I suffer collisions, cause interference ?) • Routing deals with 1,2 • Scheduling takes care of 3 once 1 and 2 have been decided • Relaying typically assumes: • Some topology knowledge • Collaborating nodes (limited/no selfishness) • Routing needs to work towards these assumptions

4. Routing in Opportunistic Networks • The role of mobility 1. Buffering taking advantage of transitive transmission 2. Delay\Disruption -Tolerant Networking • Problems arising from opportunistic communication: • Topology is becoming too variable • Selfishness can arise to conserve resources • Opportunistic Networks’ routing needs to cope with these two

5. Probabilistic Routing • Work-around: Probabilistic routing • Model and take into account the environment (too complex), or • Randomize on • Whom to send to and • When to send • Cross-layer routing approach, taking input from: • Physical layer • Access layer • Trade-off: performance / simplicity-effectivness

6. Probabilistic Routing • Work-around: Probabilistic routing • Model and take into account the environment (too complex), or • Randomize on • Whom to send to and • When to send • Cross-layer routing approach, taking input from: • Physical layer • Access layer • Trade-off: performance / simplicity-effectivness

7. Schemes Overview • Epidemic routing (Vahdat & Becker, 2000) • PROPHET (Lindgren, et al. 2003) • MAXPROP (Burgess, et al. 2006) • Parametric Probabilistic Routing (Barret, et al. 2005) • PROPICMAN (Nguyen, et al. 2007)

8. Epidemic Routing 1/2 • Bio-inspired: packets are considered to infect nodes (Vahdat & Becker, 2000) • Assumes • Nodes are randomly mobile & have ordered identifiers • Resources sufficiency (battery / buffers) • Forwarding Decision: fixed – flooding • Buffers: FIFO • Buffer (hashed) “index”: Summary Vector (SV) • Reliability: ack’s

9. Epidemic Routing 2/2 • Meeting a newly identified neighbor node • Exchange SVs • Exchange unknown messages For protocol sake the process is initiated by the node with the smaller identifier • Per-host queuing • New messages given preference over old ones in terms of buffer availability 1 A B SVA 2 Request: (SVA+SVB’) 3 Messages unknown to B

10. PRoPHET (1/2) • PRoPHET: Probabilistic Routing Protocol using History of Encounters and Transitivity (Lindgren, et al. 2003) • Users move in a “not so random”, predictable fashion • Forwarding decision: by Delivery PredictabilityP(M,D) set up at every node M for each known destination D. • Epidemic Routing SV’s are used here too to exchange • Delivery Predictability values to updated own P(M,D) as follows:

11. PRoPHET (2/2) • When the node M encounters another node N, the predictability for N  increases as: P(M, N)new = P(M, N)old + (1 - P(M,N)old) x Lenc, Lencis an initialization constant • The predictabilities for all destinations D other than N suffer ageing: P(M, D)new = P(M, D)old x γK, γis an aging constant K is a time factor • Transitive property updates the predictability of destinationD for which N has a P(N, D) value: P(M,D)new = P(M,D)old + (1 - P(M,D)old) x P(M,E) x P(E,D) x β βis ascaling factor • The assumption here is that M is likely to meet N again.

12. MaxProp (1/2) • Motivated by pedestrian mobility and city vehicles (busses) (Burgess, et al. 2006) • Addressed resources issues considering vehicles • Bulky equipment • energy • Maintains ordered destination based queues • Addresses on top of PRoPHET • QoS • Stale data • Assumes • Unlimited buffer for own messages per node • Fixed size buffer for relaying messages • No topology knowledge/control

13. MaxProp (2/2) • Communication steps (flooding-based!): 1. Neighbor Discovery (no knowledge of when the next opportunity to communicate will be) 2. Data Transfer • Transfer packets destined for neighbor peer, • Transfer routing information, • Acknowledge any delivered data, • prioritize “young” relayed packets, • Send un-transmitted packets by estimated delivery likelihood, • ensure only new packets are sent. 3. Storage Management (expunge packets to accommodate the relay buffers)

14. PARAMETRIC PROBABILISTIC ROUTING (1/2) • Developed for Sensor Networks (Barret, et al. 2005) • Based on controlled flooding: • Packet forwarding decision by probability function • Probability function is based on: • distance to destination, • distance from original source to destination, • number of copies already received, … • Variations: 1. The Destination Attractor • Source-Destination distance and Current Relay-Destination distance 2. Directed transmission • uses also the number of hops packet has already traveled.

15. PARAMETRIC PROBABILISTIC ROUTING (2/2) • Estimating distances to Destination: • Each sensor includes its current estimate of distance to D • receiving such information, each sensor updates its distance information • A sensor chooses as S-D distance the minimum of the currently received information from neighbors. • Potentially this leads to misinformation • Exponential scheme relaxes the problem, but enables wider flooding

16. Propicman • Fully context-aware routing protocol (Nguyen, et al. 2007) • Node Profile: nodes exchanging data must have some information about each other. • Selection of best forwarders: • delivery probability based on the profile of the neighbors • For every neighbor a sender calculates 2-hop route delivery probability • Forwards only if own delivery probability is less than a potential relay • Security considerations • Assumptions for “community level” security (e.g. authentication, signatures) • Messages’ content is secure although the “evidences” of the node profile can be recovered.

17. A Framework for Probabilistic Routing • Simulation framework for lower layer parameters inverstigation (Gazoni, et al. 2010) • Forwarding decision: • Probability function based on modular metric • Distance • ETX • Linear or piece wise • selection of shape and slope affects on the number of “certain forwarders” • can be varied upon execution to adapt to losses • Time to send • Back-off based scheme implemented (with variable or fixed window size) • Highly probable forwarders get to transmit early. • Passive acknowledgements via overhearing

18. References • A. Vahdat and D. Becker. Epidemic Routing for Partially-connected Ad Hoc Networks. Technical Report: CS-200006, Duke University, April 2000. • A. Lindgren, A. Doria, and O. Schelén. Probabilistic Routing in Intermittently Connected Networks. In proc. of the 2003 ACM MobiHoc. • J. Burgess, et al. MaxProp: Routing for vehicle-based disruption-tolerant networks. In proc. of 2006 IEEE INFOCOM. • C. L. Barrett et al. Parametric Probabilistic Routing in Sensor Networks, Mobile Networks and Applications 10:4, pp 529-544, 2005. • H. A. Nguyen, et al. Probabilistic Routing Protocol for Intermittently Connected Mobile Ad Hoc Networks (PROPICMAN). In proc. of the 2007 IEEE WoWMoM. • Niki Gazoni, et al. A framework for opportunistic routing in multi-hop wireless networks. In proc. of the 2010 ACM PE-WASUN.

19. Thanks for your attention!