Towards a Model-BasedData Collection Framework for Environmental Monitoring Networks

Research ProposalJayant Gupchup

Department of Computer Science, Johns Hopkins University†

75 m

Background – II (motes)

Computing,Storage

Communication(radio)

SensorsSensor Power

Barometric Pressure 10 μW

Humidity/Temperature 80 μW

Soil Moisture 19.6 mW

“Sending one packet costs same energy as thousands of CPU cycles” – Matt Welsh, Harvard

Component Power

Radio (CC2420) RX 38 mW

Radio TX 35 mW

Microcontroller (TI MSP 430. 8Mhz) 3 mW

3.6 V19.0 Ah

All data are not equal

Task list Define “Informative Periods”

Algorithm : Find Informative (or interesting) Periods

Algorithm : Sampling Planner based on the interesting periods

Evaluation

Initial Direction & Main Results

Principal Component Analysis (PCA) based approach

Classification-based approach towards detecting events.

PCA based approach: Motivation

Observations:

• Well behaved days show typical signature (bell-shaped pattern)• Rainy days (or periods) deviate from this signature• Strong trend component from one day to the next• Diurnal, trend features seen in most environmental modalities• PCA is good at capturing variation in collection of similar curves

PCA – Toy Example

First Principal Component

Variable #1

Varia

ble

#2 Finds directions of Maximum Variance

Reduces Dimensionality(truncate to first “p” directions)

Eigenmodes for Air Temperature

Directions ofMaximum Variance

Discriminating event, well-behaved days [5]

Precision Recall51.28% 80%

[5] : J. Gupchup, R. Burns, A. Terzis, and A. Szalay, Model-Based Event Detection in Wireless Sensor Networks, Proceedings of Workshop on Data Sharing and Interoperability on the World-Wide Sensor Web (DSI), ACM/IEEE, 2007

Well-behaved days: “Fits model well” Event day: “Large residuals”

Offline to Online

Offline Basis locked from midnight to midnight Access to complete 24 hour signal

Online Access to signal up to the current hour “d” Basis locked from hour “d” to hour “d”

Vectors cyclically shifted by “d” Eigenvalues remain the same

Online Prediction Residuals

Summary PCA model effective in

finding informative periods

Need to know Shift value, “d” “sundial” [6]

But … why not use Barometric Pressure too?

[6] : Jayant Gupchup, Razvan Musăloiu-E, Alex Szalay, Andreas Terzis. Sundial: Using Sunlight to Reconstruct Global Timestamps, To appear in the proceedings of the 6th European Conference on Wireless Sensor Networks (EWSN 2009)

Classification-Based Approach

2-class problem {Rainy, Sunny}

Most classifiers provide probabilities Sample based on those probabilities

Future Work - I Task 1: Model Improvement

Study effect (or correlation) of Event-magnitude Inter-Arrival Time

Explore Incremental and Robust PCA [7], [8] Explore Label based Classifiers

Combine Air Temp, Barometric Pressure and Light Modalities (joint work with Zhiliang Ma, Dept. of Applied Math and statistics)

Task 2 : Sampling Planner Prediction error and/or Probability of Event (PoE) Neighbor opinion(s) Acquisition cost of each sensor

[7] : Reliable Eigenspectra for New Generation Surveys, Tamas Budavari, Vivienne Wild, Alexander S. Szalay , Laszlo Dobos, Ching-Wa Yip , MNRAS. Accepted for publication [8] : A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data, A.J. Connolly, A.S. Szalay, Astronomical Journal

Future Work - II

Task 3 : Evaluation Define Cost and Benefit functions Compare proposed approach with existing systems

Task 4 : Application and Extensions Identify class of applications where the framework can be used

Questions

???

Overview: Proposed Framework

Model

SamplingScheduler

Update Model

Mote Storage

Prob (Event)

<Xt+1,Xt+2, … Xt+h>

<X1,X2, ... Xt>

Prediction Error

<θ1,θ2, .. θn>

Properties of our PCA model

Transformation: Y = X*V Projected variables are uncorrelated

Compression/Multi-resolution Achieve a massive compression From previous slide, compression ratio = 4/96 = 24X

Online Basis Basis for any “d” to “d” hour using cyclic shifting

Re-projection error Bounds Sum of “left out” eigenvalues

Preliminary Results Rain prediction

Use Barometric Pressure

Simple linear classifiers perform well

Classification Accuracy towards 76%

Eigenvector 5

Online Prediction

Literature Survey Barbie-Query (BBQ, [1])

Approximate query answering (Range, value queries) Sensing cost differential … Energy Saving opportunities! Predictions outside confidence interval, collect samples

Shortcomings NOT collecting long-term environmental data Do not consider the role played by events

PRESTO [2] Reduce Storage costs => Reduce Communication costs

Seasonal-AutoRegressive Integrated Moving Average (S-ARIMA) [3] model for predictions

Model known to node and Basestation When predictions within confidence bounds, do not store collected samples Basestation can reconstruct missing samples.

Shortcomings No adaptive sampling on interesting events

[1] : Model-Driven Data Acquisition in Sensor Networks; Amol Deshpande, et al. VLDB 2004[2] : PRESTO: Feedback-driven Data Management in Sensor Networks; Ming Li, Deepak Ganesan, and Prashant Shenoy; USENIX 2006[3]: P.J. Brockwell, R.A. Davis. Introduction to time series and forecasting. 2002.

Related Work

Near-Optimal Sensor Placement [4] Find most informative locations to place sensors At the same time … Keep the network connected Solution: Information-theoretic (entropy) & Steiner tree approximation

Differences Focus is finding informative locations in an offline fashion Solution addresses spatial variability Sampling rate does not change once locations are fixed

[4] : A. Krause, C. Guestrin, A. Gupta, J. Kleinberg. "Near-optimal Sensor Placements: Maximizing Information while Minimizing Communication Cost". In Proc. of Information Processing in Sensor Networks (IPSN) 2006