Department of Computer Science University of Massachusetts, Amherst PRESTO: Feedback-driven Data Management in Sensor Network Ming Li, Deepak Ganesan,

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


Hierarchical Sensor Network Architecture

Internet

Client Data Browsing, Querying and Processing

Mesh Network

Base-station

Sensor Proxy

Remote Sensors

Sensor Proxy

Remote Sensors


Approaches to Proxy-Sensor Interaction

Sensor-centric Architecture Proxy-centric Architecture


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]


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]


PRESTO Model

Sensor-centric Proxy-centricPRESTO


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.


Outline

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


Sensor Proxy

Example-Modeling

Data

11 −− += ttt eXX θModel

11 −− += ttt eXX θ

Build Model


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


Sensor

Example-Query

ProxyQuery

What is the reading at time t with confidence c?

tt confX ,

?cconft ≤Yes tXNoPull Tt


Sensor Proxy

Example-Feedback

11 ' −− += ttt eXX θ

Build Model

11 −− += ttt eXX θ

11 ' −− += ttt eXX θModel


Sensor

Example - Update Cache after Push

Push Tt

Proxy

Interpolation

Ttt eTT

TtXX

'

''

−−

−=

Interpolation

Ttt eTT

TtXX

'

''

−−

−=


Sensor

Example - Update Cache after Pull

Pull Tt

Proxy

Interpolation

Interpolation

Re-prediction

Re-prediction


Outline



Goals

◊ Catches data trends

◊ Easy to compute on sensors


Data Trends

◊ Temperature data trace shows very obvious temporal trend

◊ Shows both long term trend and short term trend.

Seasonal Period


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


Computation

◊ Easy to predict Five additions and three multiplies

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

Previous prediction results Previous prediction errors


Outline



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


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


Model-driven Push Performance

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


Scalability

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

Support up to 100 sensor nodes per proxy


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


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


Adaptation

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

non-adaptive scheme

Reduces 30% of communications


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


Summary and Future Work

Department of Computer Science University of Massachusetts, Amherst PRESTO: Feedback-driven Data Management in Sensor Network Ming Li, Deepak Ganesan,

Documents

sensor data

amherst approaches

amherst goals

amherst key ideas

greater sensor complexity

rich proxy

predictive storage slide

data query dynamics