Context-Aware SensorsContext-Aware Sensors

Eiman Elnahrawy and Badri NathDepartment of Computer Science, Rutgers University

EWSN January 19th 2004

Outline

• Introduction, Motivations, Related Work• Context-Awareness• Approach: Modeling and Learning• Applications• Preliminary Evaluations• Challenges and Research Directions• Conclusion

Introduction

• Sensors expected to become a major source of information

• Applications• Monitoring: sometimes remote harsh environments

– Habitat, climate, contamination– Agriculture and crops– Quality of food– Structures (response to earthquakes)

• Tracking and military applications• Traffic control• Industry (control at assembly lines)• Medical (smart medicine cabinets)

Limitations of Wireless Sensor Networks

• Limited battery life: if abused, sensors last few days, otherwise, may last up to few months

• Limited communication bandwidth

• Limited processing capability

Major design goal

• High rate of packet loss– Poor communication links– Connection failures– Fading of signal strength– Packet collision between multiple transmitters– Constant or sporadic interferences – > 10% of the links suffer average loss rate > 50% – Packet loss of most links fluctuates over time with

estimated variance 9% - 17%

• Topology is continuously changing (node failure,node mobility)

Limitations cause many data quality problems…

1. Outliers: serious events/bogus readings at low battery levels

2. Missing values

– Low level solutions to tolerate loss don’t usually work, problem persists

• Limited resources: Can we sample?

Inevitable!

• (Uncontrollable) harsh environmental conditions, HW and radio problems

• Current technology: cheap low quality sensors, vary in their tolerance to quality problems

• Focus of industry is even cheaper sensors -> lower quality that varies with the cost of the sensor

Serious…

• Incompleteness/Imperfection/Uncertainty• Need to know event/malicious sensor• Seriously affects decision-making/triggers

– False +ve/-ve/misleading answers• May cost you money• May jeopardize application: e.g. routing based on

gradient

I can’t rely on this sensor data anymore. It has too many problems!!?-Missing information-Hmm, is this a malicious sensor-Something strange or sensor gone bad-Can we sample?-Noise-Bias

•Limitations result in many data quality problems•Serious for immediate decision making or actuator triggers!!

General Approach

• Relatively dense networks (coverage, connectivity, robustness, etc.)

• Correlated and/or redundant readings• Spatial and temporal dependencies• Why don’t we exploit these spatio-temporal

relationships among sensors (contextual information)?

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Related Work

• Spatio-temporal correlations in sensor data – Dimensions [Ganesan et al. 2002]– Premon [Goel et al. 2001]– Geospatial data analysis [Heidemann et al. 2001]

• Assume the existence of such correlations without attempting to explicitly quantify them

• Other data quality problems– Reducing the effect of noise [Elnahrawy et al. 2003] – Calibration (a post deployment technique)

[Bychkovskiy et al. 2003]

• In-network aggregation [Madden et al. 2002, 2003, Zhao et al. 2002]

• Motivated our online in-network learning of relationships

• Spatial and temporal data [Shekhar et al. 2003]

• Graphical models in computer vision and image processing [Smyth et al. 1998, Freeman 1999]

Two Concepts

Contextual Information• Encodes spatial

dependencies as well as temporal dependencies

• Enables sensors to locally predict their current readings

Context-Awareness• Sensors are aware of their

context (neighborhood and history)

• Given context information sensors can infer (predict) their reading

Learning the Contextual Information

• Probabilistic approach based on Bayes classifiers

• Scalable (distributed) and energy-efficient procedure for online learning

• Inference computed locally at the node

learning and utilizing

contextual information

learning parameters of

a Bayes classifier and then

making inferences

Mapping

Modeling the Contextual Information

• Markovian Model (short range dependencies): last reading, immediate neighbors

H N

S

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• Simple training and inference (sensors can afford it)

• Bayesian-based models have been used in literature (image processing, spatial data)

• Gives good results and (sometimes) outperforms more sophisticated classifiers

• Has a very nice “decomposability and progressive learning” property -> Distributed learning

Why Bayesian?

Bayesian and Sensor Networks

• Features: h,n– Last reading of sensor h (temporal information)– Current readings of some immediate neighbors

n (spatial information)– In our preliminary work we used 2 neighbors

• Quantization: R = {ri} – Divide range of possible values into a finite set

of non-overlapping subintervals, not necessarily of equal length, each subinterval = class

Prediction in Bayesian Classifiers

• MAP (Maximum A posteriori) : calculate the most likely class of the current sensor reading rMAP given

– The observed features h,n (spatio-temporal information)

– The parameters θ (conditional probability tables)

H N

S

• Naive Bayes– Features conditionally independent given

the target class

• Parameters θ (CI) become 1. The 2 conditional prob. tables for P(h| ri), P(n|

ri)

2. The prior probability of each class P(ri)

Parameters are just ratios of counters!

S P(s)

r1 0.3

r2 0.7

H S P(H|S)

r1 r1 0.1

r2 r1 0.3

r1 r2 0.2

r2 r1 0.4

N S P(N|S)

(r1,r1) r1 0.15

(r1,r2) r1 0.2

(r2,r2) r1 0.16

(r1,r1) r2 0.2

(r1,r2) r2 0.1

(r2,r2) r2 0.2

Total number of counters 1 + m + 3/2 m2 + ½ m3

Frequency of r1

= |r1| / |D|

= # [H r2 current reading r1]/|r1|

= # [n (r2,r2) current reading r2]/|r2|

Learning the Parameters

• Data is free: most networks are readily used for collecting learning data (e.g., monitoring)

• 2 phases: learning and testing

• In-network, in a distributed fashion using in-network aggregation– Sensors collect training data and estimate the

parameters locally ( 1 + m + 3/2 m2 + ½ m3

counters)– Parameters (counters) are then aggregated while

propagating up the routing tree (SUM aggregate) – Flood overall counters to every sensor

Stationary vs. Non-Stationary

• Perfect Stationarity: Use in-network aggregation, most efficient

• Handling dynamic correlations requires a priori knowledge of the dynamics – Over time: re-learn the parameters dynamically at

each change – Over space: cluster the network into geographical

regions where the “stationarity in space" assumption holds inside each region

– Time, space: hybrid approach

Analysis: In-network vs. Centralized

• Both apply, different Communication cost– Roughly measured by size of learning data– Vary from application to another– Depends on accuracy and routing

mechanism – More experiments needed (future work)

• Non-stationary (space): centralized is inferior

Analysis: (Imperfectly) Stationary

In-network learning • Distributive summary aggregate

• k X O(m3)X O(n), k epochs, m classes, and n nodes

O(m3) summary agg., k times

• Effectively reduces traffic

Centralized learning• Centralized agg. (detailed set)

• p X O(n2), p training instances (application-dependent)

p centralized aggregates

• Significant traffic

Examples show centralized learning is an order of magnitude higher

Applications

Detecting malicious sensorsDiscovering outliers Super-resolution

(Sampling)

Predicting any missing value

Inference ProblemInference Problem

Evaluations

• Synthetic data (Tracking data set)– Phenomenon with sharp boundaries– Shockwave propagating around a center

based on Euclidean distance– 10000 sensors over a grid of 100 x 100– Divided range of readings into 10 bins

(classes) – Added outliers with % 10-90

• As % outliers increases– The classifier takes more time (iterations) to learn– The error in prediction increases and then remains

constant at 7% – Sensors rely more on the temporal correlations

• As % outliers increases– We were able to detect about 90% of the added

outliers– Incorrect prediction were off by less than 1

Evaluations

• Real data (Great Duck Island GDI) – Intel’s project off the shore of Maine– Subset of the nodes (2, 12, 13, 15, 18, 24,

32, 46, 55, and 57)– Spatially adjacent– 5 sensors (light, temperature, thermopile,

thermistor, humidity)– Readings from August 6 to September 9,

2002 (about 140,000 each sensor)

Acknowledgement:

Robert Szewczyk

@Berkeley

Light

HumidityThermistor

Temperature

Light

HumidityThermistor

Temperature

Evaluations

• Error becomes small enough in a relatively short time

• > 90% accuracy in most of the cases

• Stationary, random imprecision, noise, and outliers in the testing phase

Challenges

• Dynamic correlations• Heterogeneity• Number of neighbors, selection criteria• Efficient routing• Dealing with rare events• Avoid quantization -> Regression models• Multi-dimensional

Future Work

• Prototype and more Evaluations– Preliminary evaluations to investigate efficiency– Extremely valuable in highlighting major decisions

and potential deployment problems– Characterization– Overall cost

• Integration– Integrating noise, calibration, and context-awareness– Important to ensure learning of accurate correlations

Conclusion

• Dealing with data quality problems is very important

• Context-awareness: learning and making inferences

• Works well• Applications: missing values, outliers, sampling• Many open problems and future work directions

Thank You