1. 1 The Data Dissemination Problem  A region requires event- monitoring (harmful gas, vehicle motion, seismic vibration, temperature, etc.)  Deploy sensors forming a distributed network  On event, sensed and/or processed information delivered to the inquiring destination  Both static and mobile cases  Static: DD & GRAB  Mobile: TTDD Event Sensor sources Sensor sink Data dissemination A sensor field

2. 2 Design Guidelines  application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination  Data centric  In-network data processing: aggregation, filtering, compression, etc.  Leverage the scale of node population to overcome the limitation of individual sensor nodes  New way to achieve robustness  Challenges:  Scalability  Energy efficiency  Robustness / Fault tolerance in outdoor areas  Efficient routing (multiple source destination pairs)

3. 3 General Solution Approach  Gradient based design  A Publish/subscribe approach  Emulates how water flows from a hill to a valley  Establish gradients on the way  Two methods to build gradients  Directed diffusion: explicit vector that points from one node to another node  GRAB: field based that builds a scalar field and uses the derivatives of the field to indicate direction

4. 4 Directed Diffusion  Typical IP based networks  Requires unique host ID addressing  Application is end-to-end, routers unaware  Directed diffusion – uses publish/subscribe  Inquirer expresses an interest, I, using attribute values  Sensor sources that can service I, reply with data

5. 5 Directed Diffusion In-network data processing (e.g., aggregation, caching) Application-aware communication primitives –expressed in terms of named data (not in terms of the nodes generating or requesting data) –Query/reply round Distributed algorithms using localized interactions and measurement based adaptation

6. 6 Data Naming  Expressing an Interest  Using attribute-value pairs  E.g.,  Other interest-expressing schemes possible  E.g., hierarchical (different problem) Type = Wheeled vehicle// detect vehicle location Interval = 20 ms// send events every 20ms Duration = 10 s// Send for next 10 s Field = [x1, y1, x2, y2]// from sensors in this area

7. 7 Basic Directed Diffusion Setting up gradients Source Sink Interest = Interrogation in terms of data attributes Gradient = direction and strength Similar to reverse link forwarding in multicast

8. 8 Basic Directed Diffusion Source Sink Sending data and Reinforcing the “best” path Low rate eventReinforcement = Increased interest

9. 9 Directed Diffusion and Dynamics Recovering from node failure Source Sink Low rate event High rate event Reinforcement

10. 10 Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event

11. 11 More on Path Failure / Recovery  Link failure detected by reduced rate, data loss  Choose next best link (i.e., compare links based on infrequent exploratory downloads)  Negatively reinforce lossy link  Either send i1 with base (exploratory) data rate  Or, allow neighbor’s cache to expire over time Event Sink Src A C B M D Link A-M lossy A reinforces B B reinforces C … D need not A (–) reinforces M M (–) reinforces D

12. 12 Average Dissipated Energy In-network aggregation reduces DD redundancy  Flooding poor because of multiple paths from source to sink flooding Diffusion Multicast

13. 13 Delay DD finds least delay paths, as OM – encouraging  Flooding incurs latency due to high MAC contention, collision flooding Diffusion Multicast

14. 14 Delivery ratio degrades with higher % node failures  Graceful degradation indicates efficient negative reinforcement Event Delivery Ratio under node failures 0 % 10% 20%

15. 15  M gets same data from both D and P, but P always delivers late due to looping  M negatively-reinforces (nr) P, P nr Q, Q nr M  Loop {M  Q  P} eliminated  Conservative nr useful for fault resilience Loop Elimination A QP DM

16. 16 Local Behavior Choices For propagating interests –In the example, flood –More sophisticated behaviors possible: e.g. based on cached information, GPS For data transmission –Multi-path delivery with selective quality along different paths –probabilistic forwarding –single-path delivery, etc. For setting up gradients data-rate gradients are set up towards neighbors who send an interest. data-rate gradients are set up towards neighbors who send an interest. Others possible: probabilistic gradients, energy gradients, etc. For reinforcement reinforce paths, or parts thereof, based on observed delays reinforce paths, or parts thereof, based on observed delays, losses, variances etc. other variants: inhibit certain paths because resource levels are low

17. 17 DD Summary  Application-awareness – a beneficial tradeoff  Data aggregation can improve energy efficiency  Better bandwidth utilization  Network addressing is data centric  Probably correct approach for sensor type applications  Notion of gradient (exploratory and reinforced)  Flexible architecture – enables configuration based on application requirements, tradeoffs  Implementation on Berkley motes  Network API, Filter API

18. 18 GRAB Design Two protocols addressing the two problems –Robust data delivery: MESH (focus for this class) Deliver data to the user in face of node failures and packet losses –Long-lived system: PEAS Extend sensing and data delivery lifetime in proportion to the total number of deployed nodes

19. 19 Design Goal: a forwarding mesh with controllable width Forward each data packet along parallel paths to the sink these paths interleave to form a forwarding mesh The mesh starts at the source, ends at the sink The width of the mesh should be adjusted to achieve certain delivery reliability source sink

20. 20 How to forward data along an adjustable mesh build a cost field that gives each sensor the “implicit direction” towards the sink Directed diffusion uses explicit directino for forwarding to the sink assign each packet certain amount of credit which controls the width of the forwarding mesh

21. 21 How to build a cost field? The sink broadcasts an ADV packet with cost 0 Each node sets its cost as the smaller of –Its own cost (  initially) –The sum of the cost of the sender and the link cost to the sender Then broadcasts its own cost

22. 22 Excessive messages in building the cost field Sink(0) B C 4 1.5 1 sink broadcasts B (1) C (4) C, B broadcasts C (2.5) C broadcasts again the farther a node, the more it broadcasts an example: 1500 nodes, 150mx150m field, the farthest node broadcasts more than 150 times, each node broadcasts 50 times on average

23. 23 A node waits for a time proportional to its cost Sink (0) B C 4 1.5 1 T=0, sink broadcasts. B and C set timers, expiring after 1, 4 seconds B (1) C (4) T=1, B broadcasts, C cancels the first timer and sets another one that expires after 1.5 seconds B C (2.5) T=2.5, C broadcasts when its timer expires

24. 24 How to control the width of the mesh Each packet carries a credit A copy can take any path that requires a cost <= credit + Cost_source Different copies can take different paths, forming a mesh sink source Cost <= credit + Cost_source Cost > credit + Cost_source Cost_source

25. 25 Allocate credit along different hops Calculate how much credit has been used: –alpha_used = P_consumed + C_A – C_source Calculate how much is remaining –R_alpha = (alpha – alpha_used) / alpha Compare to a threshold –R_thresh = (C_A / C_source)^2 sink Cost_source cost_consumed source A Cost_A

26. 26 Key Ideas of GRAB  Cost field to indicate direction  Direction is indicated by “cost-decreasing” pointers  Use credit to build a “mesh of paths”  Multiple paths can increase robustness to node and link failures  Mesh is built on the fly No pre-computed mesh Can change on each packet if the credit included in the packet is different

27. 27 Handling mobility Source Stimulus Sink

28. 28 Mobile Sink Excessive Power Consumption Increased Wireless Transmission Collisions State Maintenance Overhead

29. 29 Challenges Battery powered sensor nodes Communication via wireless links –Bandwidth constraint –Load balancing Ad-hoc deployment in large scale –Fully distributed w/o global knowledge –Large numbers of sources and sinks Unexpected sensor node failures Sink mobility –No a-priori knowledge of sink movement

30. 30 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

31. 31 TTDD Basics Source Dissemination Node Sink Data Announcement Query Data Immediate Dissemination Node

32. 32 TTDD Mobile Sinks Source Dissemination Node Sink Data Announcement Data Immediate Dissemination Node Immediate Dissemination Node Trajectory Forwarding Trajectory Forwarding

33. 33 TTDD Multiple Mobile Sinks Source Dissemination Node Data Announcement Data Immediate Dissemination Node Trajectory Forwarding Source