1. Routing and Dissemination in Wireless Sensor Networks Sandeep Gupta Based on Slides by Huan and Junning U. Mass.

2. CSE534- Advanced Computer Networks 2 Outline Motivation and Challenges Basic Idea of Some Routing and Data Dissemination schemes in Sensor Networks

3. CSE534- Advanced Computer Networks 3 Differences with Current Networks Difficult to pay special attention to any individual node: –Collecting information within the specified region –Collaboration between neighbors Sensors may be inaccessible: –embedded in physical structures. –thrown into inhospitable terrain.

4. CSE534- Advanced Computer Networks 4 Differences with Current Networks Sensor networks deployed in very large ad hoc manner –No static infrastructure They will suffer substantial changes as nodes fail: – battery exhaustion – accidents – new nodes are added.

5. CSE534- Advanced Computer Networks 5 Differences with Current Networks User and environmental demands also contribute to dynamics: –Nodes move –Objects move Data-centric and application-centric –Location aware –Time aware

6. CSE534- Advanced Computer Networks 6 Overall Design of Sensor Networks One possible solution? –Internet technology coupled with ad-hoc routing mechanism  Each node has one IP address  Each node can run applications and services  Nodes establish an ad-hoc network amongst themselves when deployed  Application instances running on each node can communicate with each other

7. CSE534- Advanced Computer Networks 7 Why Different and Difficult? A sensor node does not have an identity (address)  Content based and data centric Where are nodes whose temperatures will exceed more than 10 degrees for next 10 minutes? Tell me the location of the object ( with interest specification) every 100ms for 2 minutes.

8. CSE534- Advanced Computer Networks 8 Why Different and Difficult? Multiple sensors collaborate to achieve one goal. Intermediate nodes can perform data aggregation and caching in addition to routing. where, when, how?

9. CSE534- Advanced Computer Networks 9 Why Different and Difficult? Not node-to-node packet switching, but node-to-node data propagation. High level tasks are needed: At what speed and in what direction was that elephant traveling? Is it the time to order more inventory?

10. CSE534- Advanced Computer Networks 10 Challenges Energy-limited nodes Computation –Aggregate data –Suppress redundant routing information Communication –Bandwidth-limited –Energy-intensive Goal: Minimize energy dissipation

11. CSE534- Advanced Computer Networks 11 Challenges Scalability: ad-hoc deployment in large scale –Fully distributed w/o global knowledge –Large numbers of sources and sinks Robustness: unexpected sensor node failures Dynamically Change: no a-priori knowledge –sink mobility –target moving

12. CSE534- Advanced Computer Networks 12 Challenges Topology or geographically issue Time : out-of-date data is not valuable Value of data is a function of time, location, and its real sensor data. Is there a need for some general techniques for different sensor applications? –Small-chip based sensor nodes –Large sensors, e.g., radar –Moving sensors, e.g., robotics

13. CSE534- Advanced Computer Networks 13 Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat R. Govindan D. Estrin

14. CSE534- Advanced Computer Networks 14 Application Example: Remote Surveillance –e.g., “Give me periodic reports about animal location in region A every t seconds” –Tell me in what direction that vehicle in region Y is moving?

15. CSE534- Advanced Computer Networks 15 Basic Idea In-network data processing (e.g., aggregation, caching) Distributed algorithms using localized interactions Application-aware communication primitives –expressed in terms of named data

16. CSE534- Advanced Computer Networks 16 Elements of Directed Diffusion Naming –Data is named using attribute-value pairs Interests –A node requests data by sending interests for named data Gradients – Gradients is set up within the network designed to “draw” events, i.e. data matching the interest. Reinforcement –Sink reinforces particular neighbors to draw higher quality ( higher data rate) events

17. CSE534- Advanced Computer Networks 17 Naming Content based naming –Tasks are named by a list of attribute – value pairs –Task description specifies an interest for data matching the attributes –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

18. CSE534- Advanced Computer Networks 18 Interest The sink periodically broadcasts interest messages to each of its neighbors Every node maintains an interest cache –Each item corresponds to a distinct interest –No information about the sink –Interest aggregation : identical type, completely overlap rectangle attributes Each entry in the cache has several fields –Timestamp: last received matching interest –Several gradients: data rate, duration, direction

19. CSE534- Advanced Computer Networks 19 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

20. CSE534- Advanced Computer Networks 20 Data Propagation Sensor node computes the highest requested event rate among all its outgoing gradients When a node receives a data : –Find a matching interest entry in its cache Examine the gradient list, send out data by rate –Cache keeps track of recent seen data items (loop prevention) –Data message is unicast individually to the relevant neighbors

21. CSE534- Advanced Computer Networks 21 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

22. CSE534- Advanced Computer Networks 22 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.

23. CSE534- Advanced Computer Networks 23 Local Behavior Choices For data transmission –Multi-path delivery with selective quality along different paths –probabilistic forwarding –single-path delivery, etc. For reinforcement –reinforce paths based on observed delays –losses, variances etc.

24. CSE534- Advanced Computer Networks 24 Initial simulation study of diffusion Key metric –Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime diffusion Compare diffusion to –flooding omniscient multicast –centrally computed tree (omniscient multicast)

25. CSE534- Advanced Computer Networks 25 Diffusion Simulation Details ns-2 Simulator: ns-2 Network Size: 50-250 Nodes Transmission Range: 40m Constant Density: 1.95x10 -3 nodes/m 2 (9.8 nodes in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2000] –660 mW in transmission, 395 mW in reception, and 35 mw in idle

26. CSE534- Advanced Computer Networks 26 Diffusion Simulation Surveillance application –5 sources are randomly selected within a 70m x 70m corner in the field –5 sinks are randomly selected across the field –High data rate is 2 events/sec –Low data rate is 0.02 events/sec –Event size: 64 bytes –Interest size: 36 bytes –All sources send the same location estimate for base experiments

27. CSE534- Advanced Computer Networks 27 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)

28. CSE534- Advanced Computer Networks 28 Conclusions Conclusions  Can leverage data processing/aggregation inside the network  Achieve desired global behavior through localized interactions  Empirically adapt to observed environment

29. SPIN: Sensor Protocols for Information via Negotiation Paper: Negotiation-Based Protocols for Disseminating Information in Wireless Sensor Networks – Mobicom 99. W.R.Heinzelman, J.Kulik, H.Balakrishnan

30. CSE534- Advanced Computer Networks 30 Conventional Approach B D E F G C A Flooding –Send to all neighbors –E.g., routing table updates

31. CSE534- Advanced Computer Networks 31 Resource Inefficiencies Implosion A B C D (a) A B C (r,s) (q,r) qs r Data overlap Resource blindness

32. CSE534- Advanced Computer Networks 32 What is the optimum protocol? B D E F G C A “Ideal” –Shortest-path routes –Avoids overlap –Minimum energy –Need global topology information

33. CSE534- Advanced Computer Networks 33 Two basic ideas Two basic ideas Exchanging sensor data may be expensive, but exchanging data about sensor data may not be. Nodes need to monitor and adapt to changes in their own energy resources

34. CSE534- Advanced Computer Networks 34 SPIN Family Data negotiation –Meta-data (data naming) –Application-level control –Model “ideal” data paths SPIN messages –ADV- advertise data –REQ- request specific data –DATA- requested data Resource management A B A B A B ADV REQ DATA Sensor Protocol for Information via Negotiation

35. CSE534- Advanced Computer Networks 35 B A ADV REQ DATA SPIN-PP Example: ADV REQ DATA

36. CSE534- Advanced Computer Networks 36 SPIN on Point-to-Point Networks SPIN-PP –3-stage handshake protocol –Advantages Simple Minimal start-up cost SPIN-EC –SPIN-PP + low-energy threshold –Modifies behavior based on current energy resources

37. CSE534- Advanced Computer Networks 37 Test Network 25 Nodes Antenna reach = 10 meters Average degree = 4.7 neighbors 59 Edges Network diameter = 8 hops Data 500 bytes 16 bytes Meta-Data

38. CSE534- Advanced Computer Networks 38 Unlimited Energy Simulations -- SPIN-PP -- Ideal -- Flooding Flooding converges first –No queuing delays SPIN-PP –Reduces energy by 70% –No redundant DATA messages

39. CSE534- Advanced Computer Networks 39 Limited Energy Simulations SPIN-EC distributes additional 20% data -- Ideal -- SPIN-EC -- SPIN-PP -- Flooding

40. CSE534- Advanced Computer Networks 40 Conclusions Successfully use meta-data negotiation to solve the implosion, overlap problem of simple flooding and gossiping. Resource-adaptive enhancements Simple scheme, small communication overhead, but a performance close to the ideal situation.

41. CSE534- Advanced Computer Networks 41 Future work Consider the cost of not only communicating data, but also synthesizing data, make it more realistic resource- adaptation protocols. Queuing delay, loss-prone nature of wireless channels can be incorporated and experimented.

42. CSE534- Advanced Computer Networks 42 Limitations The SPIN EC(Energy Constrained) version’s strategy may be too simple. There should be a topology dependant strategy, e.g. a narrow bridge connecting two connected component should be more energy conservative. The ideal criteria used to compare with SPIN is ideal in terms of data dissemination rate, so really not ‘ideal’ anymore when energy or other resources are limited, need a new goal function.

43. TTDD: A Two-tier Data Dissemination Model for Large-scale Wireless Sensor Networks (Mobicom 2002) Haiyun Luo Fan Ye, Jerry Cheng Songwu Lu, Lixia Zhang UCLA CS Dept.

44. CSE534- Advanced Computer Networks 44 Assumptions Fixed source and sensor nodes, mobile or stationary sinks nodes densely applied in large field Position-aware nodes, sinks not necessarily Once a stimulus appears, sensors surrounding it collectively process signal, one becomes the source to generate the data report

45. CSE534- Advanced Computer Networks 45 Sensor Network Model Source Stimulus Sink

46. CSE534- Advanced Computer Networks 46 Mobile Sink Excessive Power Consumption Increased Wireless Transmission Collisions State Maintenance Overhead

47. CSE534- Advanced Computer Networks 47 Goal, Idea Efficient and scalable data dissemination from multiple sources to multiple, mobile sinks Two-tier forwarding model –Source proactively builds a grid structure –Localize impact of sink mobility on data forwarding –A small set of sensor node maintains forwarding state

48. CSE534- Advanced Computer Networks 48 Grid setup Source proactively divide the plane into αXα square cells, with itself at one of the crossing point of the grid. The source calculates the locations of its four neighboring dissemination points The source sends a data-announcement message to reach these neighbors using greedy geographical forwarding The node serving the point called dissemination node This continues…

49. CSE534- Advanced Computer Networks 49 TTDD Basics Source Dissemination Node Sink Data Announcement Query Data Immediate Dissemination Node

50. CSE534- Advanced Computer Networks 50 TTDD Mobile Sinks Source Dissemination Node Sink Data Announcement Data Immediate Dissemination Node Immediate Dissemination Node Trajectory Forwarding Trajectory Forwarding

51. CSE534- Advanced Computer Networks 51 TTDD Multiple Mobile Sinks Source Dissemination Node Data Announcement Data Immediate Dissemination Node Trajectory Forwarding Source

52. CSE534- Advanced Computer Networks 52 Grid Maintenance Issues: –Efficiency –Handle unexpected dissemination node failures Solutions: –Source sets the Grid Lifetime in Data Announcement –DN replication: each DN recruits several sensor nodes from its one-hop neighbor, replicates the location of the upstream DN –DN failure detected and replaced on- demand by on-going query and data flows

53. CSE534- Advanced Computer Networks 53 Grid Maintenance Source Dissemination Node Data Immediate Dissemination Node X

54. CSE534- Advanced Computer Networks 54 Grid Maintenance (cont’d) Source Dissemination Node Data Immediate Dissemination Node X

55. CSE534- Advanced Computer Networks 55 Ns-2 Simulation Metrics –Energy consumption, delay, success rate Impacts of –Cell size –Number of sources and sinks –Sink mobility –Node failure rates

56. CSE534- Advanced Computer Networks 56 Conclusions TTDD: two-tier data dissemination Model –Exploit sensor nodes being stationary and location-aware –Construct & maintain a grid structure with low overhead First Infrastructure-approach in semi- stationary sensor networks –Efficiency & effectiveness in supporting mobile sinks Proactive sources –Localize sink mobility impact

57. CSE534- Advanced Computer Networks 57 Limitations and Future work Knowledge of cell size Greedy geographical routing failures, it is not clear how the greedy geographical routing works in terms of the neighbor’s range, which may lead to a problem of finding two dissemination node for one Mobile stimulus Mobile sensor node Sink mobility speed: limited speed Data aggregation