Towards a Model-BasedData Collection Framework for Environmental Monitoring Networks

Research ProposalJayant Gupchup

Department of Computer Science, Johns Hopkins University

Background II (motes)Computing,StorageCommunication(radio)

SensorsSending one packet costs same energy as thousands of CPU cycles Matt Welsh, Harvard

SensorPowerBarometric Pressure10 WHumidity/Temperature80 WSoil Moisture19.6 mW

ComponentPowerRadio (CC2420) RX38 mWRadio TX35 mWMicrocontroller (TI MSP 430. 8Mhz)3 mW

All data are not equal

Task listDefine 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: MotivationObservations:

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

Eigenmodes for Air Temperature

Discriminating event, well-behaved days [5][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, 2007Well-behaved days: Fits model wellEvent day: Large residuals

PrecisionRecall51.28%80%

Offline to Online

OfflineBasis locked from midnight to midnightAccess to complete 24 hour signal

OnlineAccess to signal up to the current hour dBasis locked from hour d to hour dVectors cyclically shifted by dEigenvalues remain the same

Online Prediction Residuals

SummaryPCA model effective in finding informative periods

Need to knowShift value, dsundial [6]

But why not use Barometric Pressure too?[6] : Jayant Gupchup, Razvan Musloiu-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 probabilitiesSample based on those probabilities

Future Work - ITask 1: Model ImprovementStudy effect (or correlation) of Event-magnitude Inter-Arrival TimeExplore 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 PlannerPrediction 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 : EvaluationDefine Cost and Benefit functionsCompare proposed approach with existing systems

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

Questions???

Overview: Proposed Framework

Properties of our PCA model

Transformation: Y = X*VProjected variables are uncorrelated

Compression/Multi-resolutionAchieve a massive compressionFrom previous slide, compression ratio = 4/96 = 24X

Online BasisBasis for any d to d hour using cyclic shifting

Re-projection error BoundsSum of left out eigenvalues

Preliminary ResultsRain predictionUse 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 samplesShortcomingsNOT collecting long-term environmental dataDo not consider the role played by events

PRESTO [2]Reduce Storage costs => Reduce Communication costsSeasonal-AutoRegressive Integrated Moving Average (S-ARIMA) [3] model for predictionsModel known to node and BasestationWhen predictions within confidence bounds, do not store collected samplesBasestation can reconstruct missing samples.ShortcomingsNo 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 sensorsAt the same time Keep the network connectedSolution: Information-theoretic (entropy) & Steiner tree approximation

DifferencesFocus is finding informative locations in an offline fashionSolution 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

*Mention modalities*Highlight POWER. Merge into next slide by dropping informative periods at this point.*Try to change the plot. Make titles better*Define informative periods****Notation for x and X**Make this graphical***Conclusion slide********Reduce Reduce Reduce!**