1. 1 Sensor Network Routing – II Data-Centric Routing

2. 2 Taxonomy Sensor Network Protocols Geo-Routing LAR, GPSR, GEDIR Network Encoding VGR, LCR, BVR, GEM Data Centric DDSPT, AODV, DSR ID-Based Data Centric Routing requires application level semantics, while others are application-independent SkipTodayThird Lecture

3. 3 C. Intanagonwiwat, R. Govindan, D. Estrin, et. al. IEEE/ACM Transactions on Networking, Feb 2003 Adapted from Romit Roy Choudhury’s slides Directed Diffusion for Wireless Sensor Networking

4. 4 The 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 Event Sensor sources Sensor sink Directed Diffusion A sensor field

5. 5 The solution Proposes an application-specific paradigm to facilitate efficient delivery of sensed data to inquiring destination Challenges: Scalability Energy efficiency Robustness / Fault tolerance in outdoor areas Efficient routing (multiple source destination pairs)

6. 6 Outline Directed Diffusion Overhead Analysis Performance Simulation Implementation Conclusion Paper critique

7. 7 Directed Diffusion Typical ID-based networks Requires unique host ID addressing The nodes route data independently without looking at the data content. Directed diffusion – uses publish/subscribe Inquirer expresses an interest, I, using attribute values Sensor sources that can service I, reply with data

8. 8 Key Assumption in Directed Diffusion Application data is visible Pros and Cons

9. 9 Basics of Directed Diffusion 1.A sink node expresses interests in a particular data and inject them as queries in the network 2.Sensor nodes reply to one or several interests 1. Naming 2. Gathering 2. Disseminate 4. Maintenance We need to address four key functions?

10. 10 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 I. Data Naming Expressing an Interest Using attribute-value pairs E.g., Other interest-expressing schemes possible E.g., hierarchical (different problem)

11. 11 II. How to disseminate interest …….. 1.(limited) Flooding 2.Tree-based forwarding 3.Geographic routing ( filtering out the interests on basis of the coordinate specification) 4.Using cached data to find out which neighbor had previously responded to similar interest Any other solution to reliably/efficiently propagate interests?

12. 12 Establishing Gradients….. Done between every pair of nodes Consists of a pair E.g. the gradient from A to neighbor B rate : the inverse of the value of the Interval in the interest sent by B direction : The link to B ( A might have many neighbors – a local naming is required ) They are used for sending back data to the sink – the path with the highest gradient is generally preferred

13. 13 Simplified view….

14. 14 The Algorithm……. Initially the sink sends an exploratory interest ( with a low data rate i.e. high interval ) The sensors store it in an Interest cache and forwards it. Subsequent interests having same type,interval,rect values are suppressed – thus selective forwarding Gradients set up between neighbors

15. 15 Algorithm (cont……) A sensor whose sensed value matches with the type in an interest samples the readings based on the stored interval and sends it to all the neighbors with which it has a gradient. The intermediate sensors route the data based on the gradient in that direction. Eventually the sink receives the sampled information through some neighboring node.

16. 16 Directed Diffusion…. Interest Sink Source Gradient Directional Flooding

17. 17 Directed Diffusion…. Interest Sink Source Gradient

18. 18 Directed Diffusion…. Interest Sink Source Gradient

19. 19 Directed Diffusion…. Sink Source Gradient

20. 20 Data Caching……. Helps in suppressing similar interests from different sinks Helps in suppressing similar event information from different sources and helps in data aggregation

21. 21 Data propagation….. The sources send back the data along the paths which were set up Query/interest: 1.Type=four-legged animal 2.Interval=20ms (event data rate) 3.Duration=10 seconds (time to cache) 4.Rect=[-100, 100, 200, 400] Reply: 1.Type=four-legged animal 2.Instance = elephant 3.Location = [125, 220] 4.Intensity = 0.6 5.Confidence = 0.85 6.Timestamp = 01:20:40

22. 22 Reinforcement ….. The sink chooses a high quality( optimal path ) by choosing the appropriate neighbor (using greedy strategy) and reinforces it by sending an interest packet with a lower interval to that link negatively reinforce non-optimal links The reinforced interest is forwarded by each sensor node till it reaches the source

23. 23 Negative reinforcement….. The exploratory gradients exist which helps the network to be robust in case of node failures. Send interest packets with higher interval to faulty links or links with higher delay. A measure to reduce redundant communication after finding out the optimal path

24. 24 Example of reinforcement…. Original interest Reinforced interest Type=four-legged animal Interval=1s (event data rate) Duration=10 seconds (time to cache) Rect=[-100, 100, 200, 400] Timestamp = 01:22:35 ExpireAt = 01:30:40 Type=four-legged animal Interval=20ms (event data rate) Duration=10 seconds (time to cache) Rect=[-100, 100, 200, 400] Timestamp = 01:28:00 ExpireAt = 01:35:00

25. 25 Directed Diffusion…. Interest Sink Source Gradient Directional Flooding

26. 26 Directed Diffusion…. Interest Sink Source Gradient

27. 27 Directed Diffusion…. Interest Sink Source Gradient

28. 28 Directed Diffusion…. Sink Source Gradient

29. 29 Directed Diffusion…. Sink Source Gradient Reinforcement

30. 30 Directed Diffusion…. Sink Source Gradient Reinforcement

31. 31 Directed Diffusion…. Sink Source Gradient Reinforcement

32. 32 Directed Diffusion…. Sink Source Gradient Data

33. 33 Directed Diffusion…. Sink Source Gradient Data

34. 34 Directed Diffusion robustness…. Sink Source Gradient Data

35. 35 Directed Diffusion…. Sink Source Gradient Data Reinforcement

36. 36 Directed Diffusion…. Sink Source Gradient Data Reinforcement

37. 37 Directed Diffusion…. Sink Source Gradient Data Reinforcement

38. 38 Design considerations……

39. 39 Multiple sources…… sink source Data aggregation…

40. 40 Multiple sinks…… sink source sink

41. 41 This Talk Related Work Directed Diffusion Overhead Analysis Performance Simulation Implementation Conclusion Paper critique

42. 42 Evaluation Metrics Discuss on evaluation metrics MAC vs. Networking

43. 43 Metrics MAC Delivery Ratio Delay Energy Consumption Code Size Control Overhead Throughput Good-put Efficiency # collision # Retransmission Fairness else ??? Routing Delivery Ratio Delay Energy Consumption Code Size Control Overhead Throughput Good-put Efficiency Path length Memory Requirement Data Redundancy else ???

44. 44 Simulation Setup & Metrics ns2, 50 nodes in 160x160 sqm., range 40m Node density maintained, 802.11 MAC Random 5 sources in 70x70, random 5 sinks Average Dissipated Energy Per node energy dissipation / # events seen by sinks Average Delay Latency of event transmission to reception at sink Distinct event delivery ratio Ratio of # events sent to # events received by sink

45. 45 Average Dissipated Energy In-network aggregation reduces Directed Diffusion redundancy Flooding poor because of multiple paths from source to sink flooding Diffusion Multicast

46. 46 Delay DD finds least delay paths, as OM – encouraging Flooding incurs latency due to high MAC contention, collision flooding Diffusion Multicast

47. 47 Delivery ratio degrades with higher % node failures Graceful degradation indicates efficient negative reinforcement Event Delivery Ratio under node failures 0 % 10% 20%

48. 48 Conclusion Directed diffusion, a paradigm proposed for event monitoring sensor networks Energy efficiency achievable Diffusion mechanism resilient to fault tolerance Conservative negative reinforcements proves useful A careful MAC protocol, designed for such specifics, can yield further performance gains

49. 49 Contribution 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

50. 50 Limitation of Directed Diffusion Discussion

51. 51 Critique Choice of path does not maximize aggregation Least delay path does not  max aggregation Exploratory paths improve fault tolerance But at the cost of additional msg./energy overhead Overhead analysis omits the exploratory paths Disjoint sinks can cause low aggregation Not discussed in simulations Scalability/Overhead analysis over-simplified Omniscient multicast trivialized

52. 52 Critique Idle energy = 10% of receive, 5% of transmit Explains the poor energy performance of flooding Not realistic numbers – optimistic assumption Traffic very low, and idle energy low (128 B/s) Heavier traffic, and higher idle energy may reduce performance benefits