1. Opportunities in High-Rate Wireless Sensor Networking Hari Balakrishnan MIT CSAIL http://nms.csail.mit.edu/

2. Today’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 Pic: Sam Madden

3. High-Rate WSN Applications High sensing rates: O(10 2 – 10 5 ) Hz Non-trivial analysis of gathered data Frequency analysis, correlation analysis Many domains Industrial monitoring, civil infrastructure, medical diagnosis, process control,… What are the reusable components of a general architecture for high-rate WSNs?

4. 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 Mbits/s Pic: Wei Hong

5. Intel Fab’s “20 Questions” Is energy in [f1, f2] > E? Compare energy in [f1, f2] with past activity Which frequency bands have highest energy? What is the phase relationship between samples at different locations Provide high-resolution view of last T mins of samples at location L

6. Pipeline Pressure Monitoring Preventive maintenance of (aging) water and sewage infrastructure Leaks are precursors to bursts Monitor pressure and flow at 0.5 to 2 KHz Done manually today Pic: Rory O’Connor (MIT)

7. Thames Water’s “20 Questions” (Thanks to Kevin Amaratunga & Ivan Stoianov) What’s the flow / pressure at location L? Is pressure / flow at location L different from dynamic state estimator? Has there been a significant pressure drop between locations L1 and L2? How long does it take pressure wave to travel from L1 to L2?

8. Constraints Wireless communication rates Total required raw data rates exceed next- generation radio rates Energy Sensing and communication consume energy Want months of operation on batteries Unreliable sensor nodes “In-the-net” processing essential

9. Challenges High-level programming abstractions Distributed signal and data processing operators Collaborative data acquisition High-performance network delivery

10. High-Level Programming Users won’t (can’t) write embedded signal and data processing code Generalized stream processing: continuous query processing + signal processing Develop a declarative stream processing interface Support iterative refinement

11. Generalized Stream Processing Application-independent Continuous query processing (“TinyDB++”) Distributing wavelet, Fourier operators “Boxes and arrows” program specification Connect up processing operators Specify high-level sampling rate Specify energy/lifetime constraints Support iterative refinement

12. Supporting Iterative Refinement

13. Collaborative Data Sampling Sampling rates too high for single sensors Sensing may not be fast enough, or Consumes too much energy Group of sensors subsample, collaboratively produce desired sampling rate Spreads processing and energy burden How should sub-sampled signals be aligned?

14. High-performance Data Delivery WSNs today have per-node delivery rates that are 10x worse than they should be Obtain 5-10x improvement in throughput distribution without physical layer changes Traditional stack layers considered harmful Physical, link+MAC, network layer decomposition bad for wireless

15. Traditional Layering has Problems With wires, links are shielded from one another Sharing starts only at network layer Wireless networks do not have such shielding No “links” over the air Increasing traffic degrades channel quality MAC protocols are too local to resolve contention correctly

16. Dismal Throughput Distribution [HJB, Sensys04]

17. A Different Layering May Help Replace current link+MAC and network layer decomposition Local channel control layer Traffic-based rate control, no per-packet contention resolution Has info about other nodes in “region” Take advantage of path diversity Global topology control layer Large-scale routing

18. Summary Many WSN applications require high sampling rates Want general distributed “in-the-net” processing primitives High-performance wireless data delivery with different layered decomposition