snu inc lab mobicom 2002 directed diffusion for wireless sensor networking c. intanagonwiwat, r....

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SNU INC Lab MOBICOM 2002 Directed Diffusion for Wireless S ensor Networking C. Intanagonwiwat, R. Govindan, D. Estr in, John Heidemann, and Fabio Silv a

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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)

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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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Average delay

Reinforcement rules seem to be finding the low delay paths

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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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Average delay with node failures

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Average dissipated energy with node failures

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Negative reinforcement

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Duplicate suppression

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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.

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Acknowledged problems

Experiments did not evaluate operation under high load Reinforcing multiple routes leads to wasteful excess

transmissions Experiments used the wrong MAC layer