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

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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

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Pipeline Monitoring

Source: Ivan Stoianov

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

WaveScope Architecture

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.

WaveScope Architecture

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