Department of Computer ScienceUniversity of Massachusetts, Amherst

PRESTO: Feedback-driven Data Management in Sensor Network

Ming Li, Deepak Ganesan, and Prashant ShenoyUniversity of Massachusetts, Amherst

(*PREdictive STOrage)

UNIVERSITY OF MASSACHUSETTS, AMHERST

Emerging large-scale sensor networks

◊ Hierarchical wireless networks composed of low power sensors.

◊ Enables densely and closely monitoring of phenomena.

Tracking

Surveillance

Structure/Machinery Monitoring

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Hierarchical Sensor Network Architecture

Internet

Client Data Browsing, Querying and Processing

Mesh Network

Base-station

Sensor Proxy

Remote Sensors

Sensor Proxy

Remote Sensors

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Approaches to Proxy-Sensor Interaction

Sensor-centric Architecture Proxy-centric Architecture

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Proxy-Centric Architecture

◊ Overview Proxy determines when to pull

data, which sensor to query, and what data to pull using complex modeling and query processing mechanisms.

◊ Pros: Intelligence placed where

resources are available. More complex algorithms possible.

◊ Cons: Cannot capture anomalies. Less energy-efficiency Greater query error.

BBQ [Deshpande04]

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Sensor-Centric Architecture

◊ Overview Forward queries into the

sensor network. Perform data fusion, query processing and filtering within the network.

◊ Pros: Greater query accuracy Better energy-efficiency.

◊ Cons: Greater sensor complexity. Greater query latency. Directed Diffusion [Heidemann01]

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PRESTO Model

Sensor-centric Proxy-centricPRESTO

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Key Ideas in PRESTO

◊ Steal from the rich (proxy) and give to the poor (sensors).

◊ Exploit predictable structure in sensor data when possible.

◊ Adapt to data & query dynamics to minimize energy usage.

◊ Exploit low-power storage for efficient archival querying.

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Outline

◊ Motivation◊ Key Ideas◊ Example◊ ARIMA Model◊ Evaluation◊ Summary & Future Work

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Sensor Proxy

Example-Modeling

Data

11 −− += ttt eXX θModel

11 −− += ttt eXX θ

Build Model

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Sensor

Example-Model Driven Push

?|| δ>− tt XT

Proxy

tX

tt confX ,Predict

11 −− += ttt eXX θ

Predict11 −− += ttt eXX θ

11, −− tt eX

11, −− tt eX

tT

Yes tT

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Sensor

Example-Query

ProxyQuery

What is the reading at time t with confidence c?

tt confX ,

?cconft ≤Yes tXNoPull Tt

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Sensor Proxy

Example-Feedback

11 ' −− += ttt eXX θ

Build Model

11 −− += ttt eXX θ

11 ' −− += ttt eXX θModel

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Sensor

Example - Update Cache after Push

Push Tt

Proxy

Interpolation

Ttt eTT

TtXX

'

''

−−

−=

Interpolation

Ttt eTT

TtXX

'

''

−−

−=

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Sensor

Example - Update Cache after Pull

Pull Tt

Proxy

Interpolation

Interpolation

Re-prediction

Re-prediction

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Outline

◊ Motivation◊ Key Ideas◊ Example◊ ARIMA Model◊ Evaluation◊ Summary & Future Work

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Goals

◊ Catches data trends

◊ Easy to compute on sensors

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Data Trends

◊ Temperature data trace shows very obvious temporal trend

◊ Shows both long term trend and short term trend.

Seasonal Period

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Data Trends

◊ ARIMA model can catch both of these trends

( ) ( ) (1 ) (1 ) ( ) ( )S S D d SP p t Q q tB B B B X B B eφ θΦ ⋅ ⋅ − ⋅ − ⋅ =Θ ⋅ ⋅

Long Term Trend

Short Term Trend

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Computation

◊ Easy to predict Five additions and three multiplies

1111 ' −−−−−−−− Θ+Θ−+−+= StSttStSttt eeeXXXX θ

Previous prediction results Previous prediction errors

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Outline

◊ Motivation◊ Key Ideas◊ Example◊ ARIMA Model◊ Evaluation◊ Summary & Future Work

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Evaluations

◊ Both numerical simulations and real deployments

◊ Test Bed: 1 Stargate (Proxy) / 20 Tmote’s (Sensor) 1 Stargate acts as emulator

◊ Data Trace: James Reserve

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Micro Benchmark

Component OperationEnergy (nJ)

NAND Flash20B Read + 8B Write

152

MSP430 Processor

Predict 1 Sample 24

CC2420 Radio

Transmit 1 byte 2000

Model Asymmetry

Component Operation Energy (nJ)

Stargate Model Building 11000

Telos MotePredict 1 Sample

24

Cost of model building is 500x more than prediction

Total cost of prediction and storage is 10x less than communication.

Breakdown of Energy Costs

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Model-driven Push Performance

◊ Matlab simulation shows that Model-driven push performs better than model-driven pull.

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Scalability

◊ Impact of System Scale Uses emulator to get large network scale

Support up to 100 sensor nodes per proxy

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Scalability

◊ Impact of Query Frequency System adapts to high query frequency. Query latency does increase with query frequency

Most of the queries are answered using proxy cache

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Adaptation

◊ Adapt to query dynamics Reduce query latency by 50% compared to

before adaptation

Adapt to the low query tolerance after a short period

Average query tolerance changes to a lower value which brings more pulls

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Adaptation

◊ Adapt to data dynamics Reduce communication by 30% compared to

non-adaptive scheme

Reduces 30% of communications

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Failure Detection

◊ Detect sensor failure using pulling messages Detection latency decreases with query interval,

as well as query tolerance.

Longest detection latency less than 2 hours

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Summary and Future Work