1. 1-1 CMPE 259 Sensor Networks Katia Obraczka Winter 2005 Routing

2. 1-2 Announcements

3. 1-3 Transport protocols: summary

4. 1-4 Pump Slow Fetch Quickly PSFQ r For sink-to- source communication (e.g. network reprogramming) r Reliability via retransmissions r Sequence-driven loss detection C.Y. Wan, A.T. Campbell, and L. Krishnamurthy. PSFQ: A Reliable Transport Protocol for Wireless Sensor Networks. WSNA'02, September 28, 2002, Atlanta, Georgia, USA.

5. 1-5 RMST r End-to-end or hop-by-hop repair (the latter is generally better) r Suggests that repair could be done at either MAC layer (ARQ retransmissions) or Transport Layer (requests based on fragment numbers etc.) r Timer-driven loss detection and local data caches r Fits with the Directed Diffusion API F. Stann and J. Heidemann. RMST: Reliable Data Transport in Sensor Networks. IEEE SNPA'03.

6. 1-6 ESRT r Aim for overall quality of service rather than node-to-node reliability Sankarasubramaniam, Y., Akan, O.B., and Akyildiz, I.F., "ESRT: Event-to-Sink Reliable Transport in Wireless Sensor Networks ", In Proc. ACM MobiHoc`03

7. 1-7 CODA Sankarasubramaniam, Y., Akan, O.B., and Akyildiz, I.F., "ESRT: Event-to-Sink Reliable Transport in Wireless Sensor Networks ", In Proc. ACM MobiHoc`03 r Receiver based congestion detection r Open loop hop-by-hop backpressure r Closed-Loop multi-source regulation

8. 1-8 Summarizing Transport Issues r Because of harsh conditions and severe constraints, it may be better to implement reliability in a hop-by-hop rather than end-to-end manner at either the MAC or transport layer r For energy efficiency, it is best to avoid congestion entirely, or have packet losses occur close to the source. Back pressure is a useful technique. r Where possible, scheduled solutions are preferable. s

9. 1-9 Routing

10. 1-10 Issues/challenges r Difficult to pay special attention to any individual node: m Collecting information within the specified region. r Sensors may be inaccessible: m Embedded in physical structures. m Thrown into inhospitable terrain.

11. 1-11 More issues/challenges… r Topological issues: m Arbitrarily large scale. m No fixed infrastructure. m Frequent topology changes Battery exhaustion. Accidents. New nodes are added.

12. 1-12 More issues/challenges… r User and environmental demands also contribute to dynamics: m Nodes move. m Objects move. r Data-centric and application-centric view: m Location. m Time. m Type of sensor. m Range of values…

13. 1-13 More issues/challenges… r Not node-to-node packet switching, but node- to-node data propagation. r High level tasks are needed: At what speed and in what direction was that elephant traveling? Is it the time to order more inventory?

14. 1-14 Challenges r Energy-limited nodes r Computation m Aggregate data m Suppress redundant routing information r Communication m Bandwidth-limited m Energy-intensive Goal: Minimize energy dissipation

15. 1-15 Challenges r Scalability: ad-hoc deployment in large scale m Fully distributed w/o global knowledge. m Large numbers of sources and sinks. r Robustness: unexpected sensor node failures r Dynamics: no a-priori knowledge m Sink mobility. m Target mobility.

16. 1-16 Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat R. Govindan D. Estrin

17. 1-17 Application Example: Remote Surveillance m “Give me periodic reports about animal location in region A every t seconds”. m Tell me in what direction that vehicle in region Y is moving?

18. 1-18 Basic Idea r In-network data processing (e.g., aggregation, caching). r Distributed algorithms using localized interactions. r Application-aware communication primitives. m Expressed in terms of named data.

19. 1-19 Elements of Directed Diffusion r Naming m Data is named using attribute-value pairs. r Interests m A node requests data by sending interests for named data. r Gradients m Gradients is set up within the network designed to “draw” events, i.e. data matching the interest. r Reinforcement m Sink reinforces particular neighbors to draw higher quality ( higher data rate) events.

20. 1-20 Naming r Content based naming. m Tasks are named by a list of attribute – value pairs. m Task description specifies an interest for data matching the attributes. m Animal tracking: Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400] Request Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35Reply

21. 1-21 Interest r The sink periodically broadcasts interest messages to each of its neighbors. r Every node maintains an interest cache. m Each item corresponds to a distinct interest. m No information about the sink. m Interest aggregation : identical type, completely overlap rectangle attributes. r Each entry in the cache has several fields m Timestamp: last received matching interest. m Several gradients: data rate, duration, direction.

22. 1-22 Source Sink Interest = Interrogation Gradient = Who is interested ( data rate, duration, direction ) Setting Up Gradient Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests

23. 1-23 Data Propagation r Sensor node computes the highest requested event rate among all its outgoing gradients. r When a node receives data : m Find a matching interest entry in its cache Examine the gradient list, send out data by rate. m Cache keeps track of recent seen data items (loop prevention). m Data message is unicast individually to the relevant neighbors.

24. 1-24 Source Sink Reinforcing the Best Path Low rate eventReinforcement = Increased interest The neighbor reinforces a path: 1. At least one neighbor 2. Choose the one from whom it first received the latest event (low delay) 3. Choose all neighbors from which new events were recently received

25. 1-25 Local Behavior Choices  For propagating interests  In the example, flood  More sophisticated behaviors possible: e.g. based on cached information, GPS  For setting up gradients  data-rate gradients are set up towards neighbors who send an interest.  Others possible: probabilistic gradients, energy gradients, etc.

26. 1-26 Local Behavior Choices r For data transmission m Multi-path delivery with selective quality along different paths m Probabilistic forwarding m Single-path delivery, etc. r For reinforcement m Reinforce paths based on observed delays m Losses, variances etc.

27. 1-27 Initial simulation study of diffusion r Key metric m Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime diffusion r Compare diffusion to m Flooding omniscient multicast m Centrally computed tree (omniscient multicast)

28. 1-28 Diffusion Simulation Details ns-2 r Simulator: ns-2 r Network Size: 50-250 Nodes r Transmission Range: 40m r Constant Density: 1.95x10 -3 nodes/m 2 (9.8 nodes in radius) r MAC: Modified Contention-based MAC r Energy Model: Mimic a realistic sensor radio [Pottie 2000] m 660 mW in transmission, 395 mW in reception, and 35 mw in idle

29. 1-29 Diffusion Simulation r Surveillance application m 5 sources are randomly selected within a 70m x 70m corner in the field m 5 sinks are randomly selected across the field m High data rate is 2 events/sec m Low data rate is 0.02 events/sec m Event size: 64 bytes m Interest size: 36 bytes m All sources send the same location estimate for base experiments

30. 1-30 Average Dissipated Energy 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Diffusion can outperform flooding and even omniscient multicast. (suppress duplicate location estimates)

31. 1-31 Conclusions  Can leverage data processing/aggregation inside the network.  Achieve desired global behavior through localized interactions.  Empirically adapt to observed environment.

32. 1-32 Energy-efficient multipath routing

33. 1-33 Energy-efficient multipath routing r Based on directed diffusion. r In directed diffusion: m Sink broadcasts interest. m Sensors periodically (low rate) sends back data (e.g., event detection reports). m Sink sends reinforcement on preferred path. m Reverse path is established. m Upon missing reports, sink re-broadcasts interest and sink reinforces.

34. 1-34 Problem? r Periodic flooding of interests and events in the presence of failures. r Solution?

35. 1-35 Solution: multiple paths r Multipath routing: m Load balancing. m Reliable delivery (by sending duplicates). m Robustness.

36. 1-36 Observations r Primary path: “best” path. r Data sent at lower rate on alternate paths. r Upon failure on primary path, reinforcement on alternate path. r If all altremate paths fail, flooding for path re-establishment. r Overhead: alternate path maintenance. r Resilience measured as how often path re- establishment is needed.

37. 1-37 Approach r Disjoint versus “braided” paths. r How to build multiple paths with local information only?

38. 1-38 Localized disjoint multipaths r Sink establishes primary path. r Sink selects “next best” neighbor “A”. r A propagates “alternate path” reinforcement to its “best” neighbor “B”. r If B is already on a path between sink and source, B sends back a “negative reinforcement”. r Access to local information only may lead to longer paths.

39. 1-39 Braided multipath r Partially disjoint. r For each node on primary path, find best path from source to sink that does not contain that node. r Paths in the braid expend equivalent energy. r Reinforcement to “best” node and alternate reinforcement to “next best” node.

40. 1-40 Evaluation r Energy efficiency. m Overhead. r Resilience to failures. m Isolated versus patterned failures.

41. 1-41 Results r Braided multipaths are more energy efficient. m Especially at lower densities. r Disjoint multipaths have better resilience to patterned losses. r Braided multipaths exhibit better resilience to isolated failures.

42. 1-42 Geographic routing r Deliver packets to nodes or regions based on their geographic location. r Typically, nodes know their position and immediate neighbors.

43. 1-43 Basic Geographic Forwarding B. Karp and H.T. Kung. GPSR: Greedy Perimeter stateless Routing for Wireless Networks. MobiCom2000. r Greedy: send packet to neighbor that is closest to destination r Can get stuck in voids. GPSR proposes a perimeter routing mode to avoid this.

44. 1-44 Trajectory Based Forwarding D. Niculescu and B. Nath, Trajectory Based Forwarding and Its Applications. MOBICOM 2003. r Pre-encode arbitrary geographic trajectory; packet goes through nodes closest to this trajectory. r Particularly well suited for large networks with high density.

45. 1-45 Geographic routing without location information (Rao et al.) r Apply geographic routing when (most) nodes do not have position information. r Approach: “virtual coordinates”. m Use local connectivity information.

46. 1-46 Assumptions r Nodes know their own coordinates. r Nodes know coordinates of nodes in the 2- hop neighborhood.

47. 1-47 Routing r Greedy: forward to neighbor closest to destination. r When packet arrived to destination, stop. r If stuck, do expanding ring search until closer node found.

48. 1-48 Coordinate construction r A node’s coordinates is the average of its neighbors’ coordinates. r Finding perimeter nodes’ coordinates. m Beacon nodes flood “Hello” message. m Perimeter nodes discover distance in hops to other perimeter nodes. m Perimeter nodes broadcast their perimeter vector. m Perimeter nodes use triangulation to find coordinates of all perimeter nodes.

49. 1-49 Coordinate construction (cont’d) r Deciding whether a node is on perimeter: m Use distance to beacon nodes. m If node is the farthest away from beacon node compared to all its 2-hop neighbors, then it’s on the perimeter.

50. 1-50 Evaluation r Comparison between greedy routing using real- versus virtual coordinates. r Metrics: m Success rate: number packets reaching destination using purely greedy routing. m Average path length. m Routing load. m Overhead.

51. 1-51 Results r Scalability. m Network size. m Density. r Mobility. r Losses. r Obstacles. r Trade-offs.