1. Edward Knightly http://knightly.rice.edu
High Resolution Sensing and Real-time Communication for Urban Environmental Hazards
Edward Knightly

2. Application Drivers: Urban Real-time Environmental Sensing
Problem
Urban chemical and industrial plants
Nearby low-income communities at risk
Emergencies: residents learn of hazardous leaks and extreme events via news casts: “shelter in place”
Valero refinery near Houston, TX
Chevron refinery in Bay Area, CA

3. 2012 Chevron Fire in Richmond, California
Extreme Events
2016 Dupont leaked 24,000 pounds of methyl mercaptan in La Porte, Texas
4 workers died, investigation ongoing
2012 Chevron Fire in Richmond, California
15,000 hospitalized
Dupont claimed 100 pounds
$2M fine, 210 arrests

4. Limits of Today’s Sensors
Current air quality assessment is coarse grained
Spatial: city average
Temporal: daily
Composition: ozone and NO2 only
Limited actionability

5. Application Drivers: Urban Real-time Environmental Sensing
Objectives
Empower communities near environmental hazards with real-time actionable information
High resolution data sets to foster environmental justice
Proof of concept at Technology For All
Technological goals
Gas sensor network with high spatial, spectral, and temporal resolution
Real-time data collection, communication, and processing

6. Laser Wavenumber (cm-1)
Rice Sensor
Absorption
Laser Wavenumber (cm-1)
Laser spectroscopy
Pollutants have spectral signature
Identify chemical and concentration
Internal reflectors to increase resolution

7. Road Map Commoditizing laser spectroscopy Data science
Real-time processing of air quality spectrum
Actionable alerts & minimum false positives
Networking feedback loop
Wirelessly stream high resolution data sets
Cloud data processing
High resolution data for scientists, plant, and first responders
Location-based alerts for community

8. Wireless Challenges Faster, farther, lower latency, more ubiquitous
Data rate: break the Tb/sec barrier
Spectrum: diverse and wideband from MHz to THz
Density: bits/sec/Hz/m2
Latency: avg and tail

9. New Performance Metrics
Asymmetry
Minimize client requirements
form factor, communication, computation, energy efficiency
Via advanced infrastructure
Scalability
Air time efficiency reduction per incremental node
Robustness to mobility
Throughput reduction with “nomadic” baseline

10. Rationale for Enabling Technologies
Massive MIMO for capacity scaling
Infrastructure has sophisticated signal processing, plug in power source, and large array
Mobile clients and sensors are constrained
single antenna, energy efficiency, etc.
Diverse spectrum for robust availability
No ‘one size fits all’ spectrum
UHF, ISM, 3.5 GHz, 60 GHz, and more
New rate/range design space

11. Rice Platform and Plan Goals
Scalable and flexible h/w and s/w platform to explore the design space
Protocols and algorithms at all layers
Experimental evaluation in real-world propagation environments with sensor applications

12. Summary Driving applications Diverse performance metrics
High resolution environmental sensing
Diverse performance metrics
bits/sec/Hz/m2 to air-time-efficiency scaling
Open challenges
Clean slate design to operational experience