1. 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 Jamieson Stanislav Rost Arvind Thiagarajan Mei Yuan NSF NETS/NOSS Informational Meeting 10/18/05 http://wavescope.csail.mit.edu

2. Outline Trends, requirements, architecture The Wavescope System Broadcast + state aware networking Wavescope QP: Declarative queries with: Signal-oriented operations Statistical models

3. Yesterday’s WSN Monitoring Applications Periodic monitoring repeat: wake up and sense transmit data sleep for minutes Event-based monitoring Transmit data on external event Low data rates & duty cycles

4. Next-generation WSN Apps: High-Rate + Low-Latency High sensing rates: O(10 2 – 10 5 ) Hz Non-trivial analysis of gathered data Correlations, aggregates, signal processing Closed-loop control Many domains Industrial monitoring, civil infrastructure, medical diagnosis, automotive,…

5. 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

6. 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

7. Pipeline Monitoring Source: Ivan Stoianov

8. WaveScope Research Thrust General-purpose, reusable, end-to-end system infrastructure 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

9. WaveScope Architecture

10. 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?

11. “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.

12. WaveScope Architecture

13. 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

14. 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

15. 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

16. 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

17. 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?

18. 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