Drivers for adaptive and programmable network and distributed system

services

Part 1: Environmental monitoring application drivers (CENS)

Part 2: Sensor computing with cars and people (Balakrishnan, Estrin, Madden, Srivastava; MIT, CENS)

Environmental Monitoring Applications

Goal:create programmable, autonomous, distributed observatories to address compelling science and engineering issues

Goal:create programmable, autonomous, distributed observatories to address compelling science and engineering issues

Case Study: Wastewater reuse in the Mojave DesertT. Harmon et al

• Can we recycle wastewater to supply agricultural irrigation needs in a safe and sustainable manner?

Los Angeles needs to dispose of 16 million liters per day of treated wastewater in a landlocked region

Palmdale, CA wastewater treatment plant

Reclaimed wastewaterirrigation pivot plots

groundwater

top soil

sandclay

sensor network

NO3-

NO3-

As the technology matures we will find wide-reaching applications in the built environment and throughout the business enterprise.

As the technology matures we will find wide-reaching applications in the built environment and throughout the business enterprise.

Begin with science/research applications:…however, engineering and enterprise applications

will eventually dominate

• Embeddable, low-cost sensor devices

• Robust, portable, self configuring systems

• Data integrity, system dependability

• Programmable, heterogeneous, adaptive systems

• Multiscale data fusion, interactive access

• Embeddable, low-cost sensor devices

• Robust, portable, self configuring systems

• Data integrity, system dependability

• Programmable, heterogeneous, adaptive systems

• Multiscale data fusion, interactive access

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Carbon fibers, 7 µm diameter each,~ 20-30 fibers, 1.2 cm depth

3 days after deposition (Slope: 54.3 mV, R2 = 0.9999)

9 days after deposition (Slope: 54.4 mV, R2 = 0.9999)

19 days after deposition (Slope: 52.6 mV, R2 = 0.9999)

Electrochemical deposition (constant current conditions)of polypyrrole dopped with nitrateonto carbon fibers substrate

Potentiometric Response for NO3- Ion

-log(NO3-)

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tage

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Objectives

• Energy

• Scale, dynamics

• Autonomous disconnected operation

• Sensing channel uncertainty

• Complexity of distributed systems

• Energy

• Scale, dynamics

• Autonomous disconnected operation

• Sensing channel uncertainty

• Complexity of distributed systems

Constraints

Current technology research focus

Heterogeneous Wireless Sensor Networks

• Several classes of systems: Mote herds: ScaleCollaborative processing arrays: Sampling rateMobile componentsHuman users!

• Achieve longevity/autonomy, scalability, functionality with:

heterogeneous systemsin-network processing, triggering, actuation

• Optimize across the system as a whole; including the human user

collaborative processing arrays (imaging, acoustics)

sampling rate

lifetime/autonomy

scale

autonomous mobility, human interaction

mote clusters

…human user is a critical tier inthese complex heterogeneous systems

Whole-System Optimization

• Goal is optimization of the hierarchical systemNot merely optimization of devices or any given layerModels, devices, algorithms require co-design

• Context-driven algorithmsNo single algorithm/device is best in all situationsContext is the state of the next level in the hierarchy; choose resources to apply when drilling down to next level according to this stateProbabilistic constraints and algorithms lead to more new optimizations

• Examplessimplified communication patterns and programming interfaces in more resource-constrained parts of systemadaptive and multi-scale sampling

Daily Average Temperature(Geostatistical Analyst)

Aspect(Spatial Analyst)

Slope(Spatial Analyst)

Elevation(Calculated from Contour Map)

Aerial Photograph(10.16cm/pixels)

Interactive Sensor Networks:Real time fusion poses interesting system challenges

Hourly Temperature for June 5 2004

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Series1Series2Series3Series4Series5Series6Series7Series8Series9Series10Series11Series12Series13Series14Series15Series16Series17Series18Series19Series20

3D Images

Graphs

• Even if system archives “every sample”, interactivitycalls for runtime event detection, filtering, in network processing

• Combine system filtering with humanidentification and verification

• Interactive adjustment of in network processing and tasking

From data to information…fusing multi-scale data in real time

• Combine in situ data with remote sensing imagery and computational models

• Environmental monitoring sensor networks will operate in the context of Internet services

• Slogging (TM Mark Hansen) services will allow users to stitch together disparate sensor and archived data

images from Susan Ustin, UC Davis

“Moving On*”: Sensor Computing with Cars and People

Hari Balakrishnan, Deborah Estrin, Sam Madden, Mani Srivastava

• Mobile Sensors are Coming Soon…• 650 million automobiles on the road today

200 million in the USEach car with 50-100 on-board sensors~50 million new cars per year

• 6 billion people in the world300 million in the US1.5-2B people will have cell phones by next yearCell phones are becoming sensors: cameras are just the start

• Cars and people are excellent mobile, sensor computing units

Driving Questions

• What can we do with O(billion) mobile sensors on people and cars?• What are the design principles to build such systems?

(I.e., what are the hard problems that merit a large-scale test-bed?)

Architectural Research Themes

• Dealing with billions• Network stack to handle mobility + intermittent connectivity• Coping with rich, high-rate data streams• System naming and programming model

Challenge: Dealing with Billions

• Its always about hierarchy• But in this case its not clear what the organizing hierarchy or

structure should be pinned toGeography?Data type?Human/owner centric?

• Expect both local and non-local node interactions

Challenge: Mobility + Intermittent Connectivity• Vision: Unconstrained mobility (remember, it’s people and

cars!)• Connectivity will be intermittent; Local storage is critical• “Carry-and-forward” networking• What is the equivalent of “IP” and “TCP” for “carry-and-

forward” networks?• DTN, but large-scale, and with data processing primitives “in

the net”?

Challenge: Coping with Tidal Wave of Rich Sensor Data

• Wide array of image, acoustic, physical, chemical sensor types• Moore’s law + rich sensors ==> data generated at rate comparable to

time-integral of available network capacityTrade capacity for delay

• Most data has to be processed and analyzedAutonomous “back-end” processes, alert generation, modeling, prediction, etc.

• Develop general-purpose “in the net” functions to push processing tonearer sources

Challenge: Security/Privacy• When do we want to go here?

Attacker tracks position of car (e.g., reads GPS data)Virus enters car control system via third-party softwareVirus enters phone (old problem…)Car driver generates fake data (e.g., congestion reports)Car owner “hacks” car: transponder ID, odometer, ignition control computer, etc.

Test-bed Themes

• Environmental monitoringProgrammable, adaptive tiered systems architectureInfrastructure for in-the-field and remote interactive access and data fusionIn-net triggering functions, and slogging services that exploit multiscale and probabilistic techniquesBroadly available, compelling, high and low sampling rate instrument packages

• Instrument cars and cell phonesBasic carry-and-forward network stack as “universal dialtone”Wide-ranging data collection and scaling study to facilitate the researchGeneral-purpose in-the-net data processing functions that exploit multiscale and probabilistic techniquesBroadly-available, compelling instrument packages