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WaveScope – An Adaptive Wireless Sensor Network System for High Data-
Rate Applications
PIs:Hari Balakrishan (MIT) Sam Madden (MIT)Kevin Amaratunga (Metis Design)
Students & Staff (MIT):Kyle JamiesonStanislav RostArvind ThiagarajanMei Yuan
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.NSF NETS/NOSS Informational Meeting
10/18/05http://wavescope.csail.mit.edu
Outline
• Trends, requirements, architecture
• The Wavescope System
• Broadcast + state aware networking
• Wavescope QP: Declarative queries with:• Signal-oriented operations• Statistical models
Yesterday’s WSN Monitoring Applications
• Periodic monitoringrepeat:
wake up and sensetransmit datasleep for minutes
• Event-based monitoring• Transmit data on external event
• Low data rates & duty cycles
Next-generation WSN Apps: High-Rate + Low-Latency
• High sensing rates: O(102 – 105) Hz
• Non-trivial analysis of gathered data• Correlations, aggregates, signal processing
• Closed-loop control
• Many domains• Industrial monitoring, civil infrastructure,
medical diagnosis, automotive,…
Example: Industrial Monitoring
• Preventive maintenance of fabrication plant equipment (Intel)• Done manually today, offline processing
• Sense vibration (acceleration)• 100 machines, >10 observation
points per machine• 10-40 kHz frequency band• Aggregate data rate about 10 – 100 Mbps
• Real time monitoring -> in-net. signal processing• E.g., freq. xform to capture relevant freq. bands
Aka condition-based monitoring
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Three Testbeds
• Automotive monitoring (CarTel)• Vibration, microphone signals• Small scale, in-lab deployment with microphones• 10+ cars by 2006• http://cartel.csail.mit.edu
• Pipeline Monitoring (Ivan Stoianov)
• Airplane wing monitoring (Metis Design)• Vibration signatures for structural weakness
QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.
WaveScope Research Thrust
General-purpose, reusable, end-to-end systeminfrastructure for monitoring and control in high-rate, low-latency WSNs
General-purpose, reusable, end-to-end systeminfrastructure for monitoring and control in high-rate, low-latency WSNs
• Network architecture• Congestion management + quality aware
routing • Broadcast-based architecture• Generalized state management
• Information processing• “In-the-net” processing operators• Data fusion, probabilistic models, signal
processing
Broadcast-based Architecture• With wires, links are shielded from one another
• Sharing starts only at network layer
• Wireless networks have no such shielding
• Radios are not wires!
• Unnatural and inefficient to think in terms of links
• Need a new abstraction that embraces broadcast
• Many new techniques: frame combining, opportunistic routing, multi-radio diversity, network coding, etc.
• Open question: Can we build a broadcast-based wireless network architecture?
“In-the-net” processing: State semantics
• Internet architecture: soft state, fate sharing
• Does not accommodate “in-the-net” processing
• Open question: What are the right principles for dealing with state upon failure, churn, topology reconfiguration, etc?
• Example: In-network database computing aggregate over last ten minutes of data from several sensors.
Information Processing in WSNs
• TinyDB: “Sensornets meets relational databases”• Streaming data aggregation, filtering, joins
• WaveScope QP• High-rate, signal-oriented data processing• Statistical models and inference
• To deal with noisy and missing data
WaveScope QP Challenges
• Support high rate sensing (> a few Hz)
• Provide “signal oriented” operations
• “Information intelligence” (models)
• Detect failures + outliers
• Detect correlations
• Predict missing values
Goal 1: Generalizing to Signals
• Want signal level processing• Maintain generality, application-
independence • Include e.g., wavelet, time-series operators
• Workflow style programming• Connect up processing operators• Specify high-level sampling rate• Specify energy/lifetime constraints• Specify signal-level filters
Goal 2: Statistical Models
• Idea: Build a model of the data, use to answer
queries
• Sensor readings update the model as needed
• Example models: probability distribution
• Benefits:
• Transmit less data
• Report correlations, detect anomalies
• “Smart” interpolation for missing data
• Answer complex probabilistic queries
Allow users to understand their data
Central Model
Interface Challenge
• How do users pose queries?• Query language• “Boxes and arrows”
• How do users specify rates and priorities?
• How do users select and specify models?
Status and Wrap-up
• High-rate and low-latency will be a defining feature of next-generation WSNs
• Requires “signal oriented” thinking
• Techniques to model data, detect outliers, predict missing values• “In-network intelligence”
• Current status: • Several signal-oriented testbeds
• Audio, automotive, pipelines• Converging on common set of SP primitives• Broadcast-based, state-aware networking• See poster