Post on 12-Jan-2016
description
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
T
T+1T+2
• 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