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Opportunities in High-Rate Wireless Sensor Networking Hari Balakrishnan MIT CSAIL http://nms.csail.mit.edu/ Today’s WSN Monitoring Applications • Periodic monitoring repeat: wake up and sense transmit data sleep for minutes Pic: Sam Madden • Event-based monitoring • Transmit data on external event • Low data rates & duty cycles 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 Pic: Rory O’Connor (MIT) • Monitor pressure and flow at 0.5 to 2 KHz • Done manually today 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 nextgeneration 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
