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SNU INC Lab
MOBICOM 2002
Directed Diffusion for Wireless Sensor Networking
C. Intanagonwiwat, R. Govindan, D. Estrin, John Heidemann, and Fabio Silva
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Contents
Introduction
Directed Diffusion□ Interest and Data Naming□ Interest Propagation and Gradients Set-up□ Data Propagation□ Reinforcement
Simulations
Conclusion
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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 EventSensor sources
Sensor sink
Directed Diffusion
A sensor field
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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 : 405. ExpiresAt = 01 : 30 : 40
Data/Reply1. Type = tank2. Instance = [150, 220]3. Location = [125, 220]4. Intensity = 0.65. Confidence = 0.856. Timestamp = 01:20:40
Named using Attribute-Value Pairs
Duration=10 min (time to cache)
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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) Timestamp Gradient1(data rate) Gradient2 ….. Duration
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Low Data-rateInterest
Exploratory Gradient
EventEvent
Low Data-rate Interest
Low Data-rateInterest
Exploratory RequestGradient
Bidirectional gradients established on all links through flooding
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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)
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Exploratory events
Source
Sink
Exploratory event: initial interest 에 대한 event
Instance = [150, 220]
Instance = [150, 220]
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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
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Source
Positive Reinforcement (Cont’d)
Instance = [150,300]
We reinforce that neighbor if it is sending new events
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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
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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
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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.
eventsdistinctof
nodeperenergydissipatedtotal
#
sentoriginally
receivedeventsdistinctof
#
#
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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
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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
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Event Delivery Ratio with node failures
Turn off 10~20% nodes for 30 seconds, repeatedlyEach source sees different vehicles
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High idle radio power
AT&T Wavelan: 1.6W (for transmission), 1.2W (for reception), 1.15W (for idle time)
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Conclusions
Directed Diffusion is significant energy efficient. Directed Diffusion is stable under node failures. Performance depends on sensor radio MAC layers.