1.  SNU INC Lab MOBICOM 2002 Directed Diffusion for Wireless Sensor Networking C. Intanagonwiwat, R. Govindan, D. Estrin, John Heidemann, and Fabio Silva

2.  SNU INC Lab Contents  Introduction  Directed Diffusion □Interest and Data Naming □Interest Propagation and Gradients Set-up □Data Propagation □Reinforcement  Simulations  Conclusion

3.  SNU INC Lab Introduction  Problem: How can we get data from the sensors?  Sensor network : □Frequent Node Failure □Energy-Constraint  Request Driven □ Task: sink->sensors (query dissemination) □ Event: sensor source->sink  Data Centric □ Communication is for named data  Diffusion closely resembles some ad-hoc routing Event Sensor sources Sensor sink Directed Diffusion A sensor field

4.  SNU INC Lab Interest and Data Naming  Interest/Query 1.Type = tank 2.Interval = 10ms (event data rate, 100 events per second) 3.Rect = [-100, 100, 200, 400] 4.Timestamp = 01 : 20 : 40 5.ExpiresAt = 01 : 30 : 40  Data/Reply 1.Type = tank 2.Instance = [150, 220] 3.Location = [125, 220] 4.Intensity = 0.6 5.Confidence = 0.85 6.Timestamp = 01:20:40  Named using Attribute-Value Pairs Duration=10 min (time to cache)

5.  SNU INC Lab Interest Propagation and Gradients Set-up  Sink periodically broadcasts interest  Exploratory interest with a large interval □Low data rate (few data packets are need in unit time)  Neighbors update interest-cache and forwards the interest □Flooding □Directional flooding based on location. □Directional Propagation based on previously cached data  Gradients set-up □Gradients are set up to the upstream neighbors □Weight : data rate Interest(type)TimestampGradient1(data rate)Gradient2…..Duration

6.  SNU INC Lab Low Data-rate Interest Exploratory Gradient Event Low Data-rate Interest Low Data-rate Interest Exploratory Request Gradient Bidirectional gradients established on all links through flooding

7.  SNU INC Lab Data Propagation  If Event occurs,  Search interest cache for “matching interest entry”  Compute the highest event rate among all its gradients, and Sample events at this rate  And Send data to the relevant neighbors  Receiving node: □Find matching entry in interest cache, no match – silent drop □Check and add data cache (loop prevention) □Re-send message with appropriate rate (down-conversion)

8.  SNU INC Lab Exploratory events Source Sink Exploratory event: initial interest 에 대한 event Instance = [150, 220]

9.  SNU INC Lab Positive Reinforcement  After sink starts receiving exploratory events, Reinforces one particular neighbor for real data  Is achieved by “data driven” local rules  Example of such a rule: □Receives previously unseen event from a neighbor  Sink re-send original interest with a “smaller interval” (higher data rate)  Receiving node also reinforce at least one neighbor □Using data cache □Example: neighbor from which it first received the latest event matching the interest

10.  SNU INC Lab Source Positive Reinforcement (Cont’d)

11.  SNU INC Lab Source Positive Reinforcement (Cont’d) Instance = [150,300] We reinforce that neighbor if it is sending new events

12.  SNU INC Lab Positive Reinforcement (cont’d)  It ’ s possible more than one path being reinforced  Selects empirically low-delay path □When one path delivers event faster, □Sink uses this path for high-quality data

13.  SNU INC Lab Negative Reinforcement  Negatively reinforce a path □To time-out data gradient unless it is explicitly reinforced □To explicitly send negative reinforcement message  Local repair for failed paths □When C detects its failure, negatively reinforce failed link and reinforce another path

14.  SNU INC Lab Simulations  Vehicle tracking system in ns-2  3 Metrics □Average dissipated energy □Average delay  One way latency between transmitting events and receiving it □Distinct-event delivery ratio  These metrics are studied as a function of network size.

15.  SNU INC Lab Parameter setting  Sensor field: 50 nodes in 160m x 160m square  Radio range is 40m  Keep the average density of sensor nodes constant  5 sources and 5 sinks (  low load)  Each source generates two events per second  Rate for exploratory events is one event per 50 seconds  Window for negative reinforcement is 2 seconds  1.6Mb/s 802.11 MAC  Energy model □Idle time: 35mW □Receiving power: 395mW □Transmission power: 660mW

16.  SNU INC Lab Average dissipated energy Omniscient multicast is idealized scheme, but has no data aggregation. Multiple path Reinforcement is very aggressive Negative reinforcement is very conservative Listening energy

17.  SNU INC Lab Average delay Reinforcement rules seem to be finding the low delay paths

18.  SNU INC Lab Event Delivery Ratio with node failures Turn off 10~20% nodes for 30 seconds, repeatedly Each source sees different vehicles

19.  SNU INC Lab Average delay with node failures

20.  SNU INC Lab Average dissipated energy with node failures

21.  SNU INC Lab Negative reinforcement

22.  SNU INC Lab Duplicate suppression

23.  SNU INC Lab High idle radio power AT&T Wavelan: 1.6W (for transmission), 1.2W (for reception), 1.15W (for idle time)

24.  SNU INC Lab Conclusions  Directed Diffusion is significant energy efficient.  Directed Diffusion is stable under node failures.  Performance depends on sensor radio MAC layers.

25.  SNU INC Lab Acknowledged problems  Experiments did not evaluate operation under high load  Reinforcing multiple routes leads to wasteful excess transmissions  Experiments used the wrong MAC layer