Opportunities in High-Rate Wireless Sensor Networking

Hari BalakrishnanMIT CSAIL

http://nms.csail.mit.edu/

Today’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

Pic: Sam Madden

Pic: Sam Madden

High-Rate WSN Applications

• High sensing rates: O(102 – 105) 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?

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

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

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)

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?

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

Challenges

• High-level programming abstractions

• Distributed signal and data processing operators

• Collaborative data acquisition

• High-performance network delivery

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

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

Supporting Iterative Refinement

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?

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

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

Dismal Throughput Distribution

[HJB, Sensys04]

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

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