1. 1-1 Routing

2. 1-2 Data-Centric Routing r Paradigm shift from accessing data from individual nodes to accessing “relevant” data. m Data within certain region, m Data on events, m Collective data processing, e.g., “What’s the average temperature of a region?”, “How many animals cross this path?”, “Is there an intruder in the area?”.

3. 1-3 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

4. 1-4 Challenges r Scalability: arbitrarily large scale ad-hoc deployment. m Fully distributed w/o global knowledge. m Large numbers of sources and sinks. r Robustness: unexpected sensor node failures. r Dynamics: m Topology changes (e.g., mobility, failures, etc.) m Target mobility.

5. 1-5 Directed Diffusion r Intanagonwiwat et al., ACM Mobicom 2000. r One of the first data centric routing paradigms.

6. 1-6 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?

7. 1-7 Basic Idea r Simple attribute-based naming as fundamental building block. r Requests for information (interests) and relevant data (reports) are described as sets of value-attribute pairs.

8. 1-8 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

9. 1-9 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 are set up within the network designed toward the sink to “draw” events, i.e. data matching interest. r Reinforcement m Sink reinforces particular neighbors to draw higher quality ( higher data rate) events.

10. 1-10 Basic Algorithm r Sink floods interest. (interest may be periodically repeated).  Every node caches interest while valid, and creates local gradient towards neighboring nodes from which it heard interest.  Sources with relevant data starts sending it according to local gradients. r When sink starts receiving data, it reinforces one or some of the paths, pruning the rest. r Negative reinforcements can be used for adjusting to changing consitions.

11. 1-11 Source Sink Interest = Interrogation Gradient = Who is interested ( data rate, duration, direction ) Example Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests

12. 1-12 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.

13. 1-13 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

14. 1-14 Local Behavior Choices  For propagating interests:  In the example, flood.  More sophisticated behaviors possible: e.g. based on GPS.  For setting up gradients:  Data-rate gradients are set up towards neighbors who send an interest.  Others possible: probabilistic gradients, energy gradients, etc.

15. 1-15 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.

16. 1-16 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)

17. 1-17 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

18. 1-18 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

19. 1-19 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)

20. 1-20 Directed Diffusion Variants r Original mechanism: 2-phase pull, i.e., interests and reinforcements. r 1-phase pull variant: eliminates reinforcements as a separate phase. m Sink floods interest. m Data source selects best reverse path. m Assumes links are bidirectional. r Push-diffusion: m Initiative from sources, i.e., they advertise their data along multiple paths; sink, if interested, reinforces one or some of the paths.

21. 1-21 Pull versus Push Diffusion r Overall performance is application dependent. r “Pull” is more energy-efficient in terms of route setup in the case of many active sources. r “Push” is more efficient when there are fewer sources and more sinks.

22. 1-22 Multipath Routing r Robustness/resilience to failures. r Multipath versus alternate path routing. r Totally- or partially disjoint paths.

23. 1-23 Directed Diffusion Resilience r Periodic flooding of interests and events to circumvent failures. r Problem?

24. 1-24 Braided Multipath Routing r Ganesan et al., MC2R 2002. r Alternate path routing. r Braided path: node/link disjointedness between the multiple paths is not required. Braided paths: For each node in the main path, find path that does not include that node.

25. 1-25 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 alternate paths fail, flooding for path re-establishment. r Overhead: alternate path maintenance. r Resilience measured as how often path re- establishment is needed.

26. 1-26 Approach r Disjoint versus “braided” paths. r How to build multiple paths with local information only?

27. 1-27 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.

28. 1-28 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.

29. 1-29 Evaluation r Energy efficiency. m Overhead. r Resilience to failures. m Isolated versus patterned failures.

30. 1-30 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.

31. 1-31 Gradient Cost Routing (GRAd) r Poor et al., ACM Queue 2003. r All nodes keep estimated cost to destinations (sinks); e.g., number of hops. r When packet is sent, it includes cost so far (i.e., number of hops traversed) and TTL. r Node receiving packet whose cost is smaller than packet TTL, forwards packet. r Increments packet cost by one; decrements TTL by one. r GRAd = limited flood for robustness at expense of overhead.

32. 1-32 Gradient Broadcast (GRAB) r Ye et al., IPSN 2003. r Enhances GRAd with “credits” decremented at each hop. m Earlier hops receive greater credit and thus higher spreading initially. m Ensures diverse paths converge to sink. S D

33. 1-33 Energy-Efficient Routing r Maximize network lifetime. r Techniques range from: m Use of suitable shortest-path metric. m Derive energy-efficient routes using global optimization. m Traffic spreading for load balancing.

34. 1-34 Power-Aware Routing for MANETs r Singh et al., ACM Mobicom 98. r Pick nodes with longer remaining battery lifetime as intermediate relays. r If R i is remaining energy of node i, then link metric is C=1/R i.  Shortest-path algorithm finds route that minimizes  i 1/R i.

35. 1-35 Traffic Spreading r Load balance across multiple paths.

36. 1-36 Traffic spreading approaches r Stochastic: node picks next-hop randomly (chosen from neighbors with equal gradient). r Energy-based: node increases its “height” when its energy falls below a certain threshold. All nodes need to adjust their height accordingly. r Stream-based: divert streams from nodes that are part of paths used b other streams.

37. 1-37 Geographic Routing r Useful for location-specific interests/queries. r Deliver packets to nodes or regions based on their geographic location. r Typically, nodes know their position and immediate neighbors.

38. 1-38 Geographic Forwarding r Simplest form of geographic-based forwarding. m Finn, ISI Tech Report, 1987. m Greedy approach. m Forwards packet to neighbor closest to destination.

39. 1-39 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.

40. 1-40 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.

41. 1-41 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.

42. 1-42 Assumptions r Nodes know their own coordinates. r Nodes know coordinates of nodes in the 2- hop neighborhood.

43. 1-43 Data Forwarding 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.

44. 1-44 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.

45. 1-45 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.

46. 1-46 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.

47. 1-47 Results r Scalability. m Network size. m Density. r Mobility. r Losses. r Obstacles. r Trade-offs.

48. 1-48 Routing with Mobile Nodes r Significant previous work on routing for MANETs where potentially all nodes can move. r Sensor networks are assumed to be predominantly static. However, a few nodes (e.g., the sinks) can be mobile. m E.g., robots, humans roaming in the area, etc. r Advantages of mobility: m Enable collecting information in a timely manner. m Provide network connectivity.

49. 1-49 Data MULEs

50. 1-50 Target deployments. r Sparse networks. r Multi-tiered deployments. m Sensors. m Wired access points. m Mules.

51. 1-51 Approach r Mobile agents. r MULEs: mobile ubiquitous LAN extensions. m Mobility. m Communication (short range). UWB radios? [low power and ability to handle bursts]. m Buffering.

52. 1-52 Pros and cons

53. 1-53 Pros and cons r Pros: m Energy efficiency ? Listen for the mule. m Intermittent connectivity. r Cons: m Increased latency.

54. 1-54 3-tier architecture r Wired APs. r Mules. r Sensors.

55. 1-55 Considerations r APs have no limitations. r Mules: m Storage, mobility, ability to communicate with sensors and APs. m Unpredictable movement patterns. m Can talk to other mules. Benefits? r Robustness. r Reliability.

56. 1-56 More considerations… r No routing overhead. r Mules can transport data for multiple applications. r High latency. m Delay bounds? r Mobility limitations.

57. 1-57 Main results r Buffer requirements at sensors inversely proportional to ratio of number of mules to grid size. r Buffer requirement at mule inversely proportional to ratio of number of mules to grid size and ratio of APs to grid size. r Relationship between buffer capacity, number of mules, and reliability.