Routing and Data Dissemination

1. Routing and Data Dissemination Presented by: Li, Huan Liu, Junning

2. Outline • Motivation and Challenges • Basic Idea of Three Routing and Data Dissemination schemes in Sensor Networks • Some Thoughts on Comparison of the Data dissemination schemes

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. 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. 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. 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. Why Different and Difficult? • A sensor node is not 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. 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. 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. Challenges • Energy-limited nodes • Computation • Aggregate data • Suppress redundant routing information • Communication • Bandwidth-limited • Energy-intensive Goal: Minimize energy dissipation

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. 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., rada • Moving sensors, e.g., robotics

13. SPIN: The Goal Broadcast with minimum energy W.R.Heinzelman, J.Kulik, H.Balakrishnan

14. B D G C A E F Conventional Approach • Flooding • Send to all neighbors • E.g., routing table updates

15. (a) (a) A • Implosion • Data overlap B A C B (a) (a) D C r q s (r,s) (q,r) Resource Inefficiencies • Resource blindness

16. B D G C A E F What is the optimum protocol? • “Ideal” • Shortest-path routes • Avoids overlap • Minimum energy • Need global topology information

17. 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

18. SPIN Family Sensor Protocol for Information via Negotiation • 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 ADV A B REQ A B DATA A B

19. REQ DATA DATA DATA DATA REQ ADV REQ ADV ADV ADV DATA REQ ADV DATA ADV ADV REQ REQ SPIN-PP Example: A B

20. 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

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

22. Unlimited Energy Simulations • Flooding converges first • No queuing delays • SPIN-PP • Reduces energy by 70% • No redundant DATA messages --SPIN-PP --Ideal --Flooding

23. Limited Energy Simulations -- Ideal --SPIN-EC --SPIN-PP --Flooding • SPIN-EC distributes additional 20% data

24. 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.

25. 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.

26. 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.

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

28. 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?

29. 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

30. 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

31. Reply Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35 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: Request Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400]

32. 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

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

34. 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

35. Reinforcing the Best Path Source 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 Sink Low rate event Reinforcement = Increased interest

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

37. 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.

38. Initial simulation study of diffusion • Key metric • Average Dissipated Energy per event delivered • indicates energy efficiency and network lifetime • Compare diffusion to • flooding • centrally computed tree (omniscient multicast)

39. Diffusion Simulation Details • Simulator: ns-2 • Network Size: 50-250 Nodes • Transmission Range: 40m • Constant Density: 1.95x10-3 nodes/m2 (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

40. 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

41. Average Dissipated Energy 0.018 0.016 Flooding 0.014 0.012 0.01 0.008 Omniscient Multicast (Joules/Node/Received Event) Average Dissipated Energy 0.006 Diffusion 0.004 0.002 0 0 50 100 150 200 250 300 Network Size Diffusion can outperform flooding and even omniscient multicast. (suppress duplicate location estimates)

42. Conclusions • Can leverage data processing/aggregation inside the network • Achieve desired global behavior through localized interactions • Empirically adapt to observed environment

43. Comments • Primary concern is energy • Simulations only • Only use five sources and five sinks • How to exam scalability? • ???

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

45. 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

46. Sink Sink Sensor Network Model Stimulus Source

47. Mobile Sink Excessive Power Consumption Increased Wireless Transmission Collisions State Maintenance Overhead

48. 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

49. 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…

50. Source Sink TTDD Basics Dissemination Node Data Announcement Data Query Immediate Dissemination Node