Embedded Networked Sensing for Environmental Monitoring: Applications and Challenges

Deborah EstrinCenter for Embedded Networked Sensing (CENS), Director

UCLA Computer Science Department, Professor

Work summarized here is largely that of students and staff at CENS

Embedded Networked Sensing Potential

• Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close”

• Enables spatially and temporally dense environmental monitoring

Embedded Networked Sensing will reveal previously unobservable phenomena

Contaminant TransportEcosystems, Biocomplexity

Marine Microorganisms Seismic Structure Response

ENS enabled by Networked Sensor Node Developments

LWIM III

UCLA, 1996

Geophone, RFM

radio, PIC, star

network

AWAIRS I

UCLA/RSC 1998

Geophone, DS/SS

Radio, strongARM,

Multi-hop networks

Sensor Mote

UCB, 2000

RFM radio,

Atmel

Medusa, MK-2

UCLA NESL

2002

Predecessors in• DARPA Packet Radio program• USC-ISI Distributed Sensor Network Project (DSN)

ENS: Technology Design Themes

• Long-lived systems that can be untethered (wireless) and unattended• Communication will be the persistent primary consumer of scarce

energy resources (Mote: 720nJ/bit xmit, 4nJ/op)• Autonomy requires robust, adaptive, self-configuring systems

• Leverage data processing inside the network• Exploit computation near data to reduce communication, achieve

scalability• Collaborative signal processing• Achieve desired global behavior with localized algorithms

(distributed control)

• “The network is the sensor” (Manges&Smith, Oakridge Natl Labs, 10/98)• Requires robust distributed systems of hundreds of

physically-embedded, unattended, and often untethered, devices.

ENS Architecture Drivers

Energy and scalabilityEnergy and scalability

Heterogeneity of devicesHeterogeneity of devices

Smaller component size and costSmaller component size and cost

EmbeddableMicrosensorsEmbeddableMicrosensors

Networked Info-MechanicalSystemsNetworked Info-MechanicalSystems

Distributed Signal andInformation ProcessingDistributed Signal andInformation Processing

DRIVERS TECHNICAL CAPABILITIES

Adaptive Self-ConfiguringWireless SystemsAdaptive Self-ConfiguringWireless Systems

Varied and variableenvironmentsVaried and variableenvironments

CENS Systems under design/construction

• Biology/Biocomplexity• Microclimate monitoring• Triggered image capture• Canopy-net (Wind River

Canopy Crane Site)

• Contaminant Transport• County of Los Angeles

Sanitation Districts (CLASD) wastewater recycling project, Palmdale, CA

• Seismic monitoring• 50 node ad hoc, wireless,

multi-hop seismic network• Structure response in USGS-

instrumented Factor Building w/ augmented wireless sensors

Ecosystem Monitoring

• Sensor system logical components

• Tasking, configuration (sample rates, event definition, triggering)

• Data Transport• Device management,

sample manipulation and caching with timing

• Duty cycling

• Other important examples of habitat monitoring systems

• Berkeley/Intel GDI and Botanical gardens

Extensible Sensing System (ESS) Software*

• Tiered architecture components

• Mica2 )motes (8 bit microcontrollers w/TOS with Sensor Interface Board hosting in situ sensors

• Microservers are solar powered, run linux, 32-bit processors

• Pub/sub bus over 802.11 to Databases, visualization and analysis tools, GUI/Web interfaces

• Data multicast over Internet on publish-and-subscribe bus system (called Subject Servers) to databases, GUIs, other data analysis tools, clients.

* Osterweil, Rahimi, Mysore, Wimbrow

Common theme: local adaptation and redundancy

Irregular deployment and environment

Dynamic network topology Hand configuration will fail

• Scale, variability, maintenance

Event Detection

Localization &Time Synchronization Calibration

Programming Model

Information Transport, Aggregation and Storage

Long-lived, Self-configuring Systems

Network Architecture: Can we adapt Internet protocols and “end to end” architecture?

• Internet routes data using IP Addresses in Packets and Lookup tables in routers• Humans get data by “naming data” to a search

engine• Many levels of indirection between name and IP

address• Works well for the Internet, and for support of

Person-to-Person communication

• Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection

Directed Diffusion*--Data Centric Routing

• Data centric approach has the right scaling properties

• name data (not nodes) with externally relevant attributes (data type, time, location of node, SNR, etc)

• diffuse requests and responses across network using application driven routing (e.g., geo sensitive)

• support in-network aggregation and processing

• Not end to end data delivery

• Not just a database query

* Heidemann et.al. SOSP ‘01, ** Krishnamachari et al. ‘02

Sink

Sources

Interest

GradientRouted Data

• Optimized version of general diffusion (Heidemann et al.)

• Pulls data out to only one sink at a time (saves energy)

• Used in Ecosystem application over Mica 2 motes:TinyDiffusion (Osterweil et al)

Diffusion: One Phase Pull *

Voronoi Scoping: Restricted Floods from Multiple Sinks*

• Benefits of multiple sinks • Reduce average path length• Equalize load over multiple trees• Tiered architecture, redundancy• BUT: Linear increase in interests

flooded!• Voronoi clusters: partition topology,

each subset contains nodes closest to associated sink.

• Only fwd interests from closest sink • No overlap between floods• Motes receive interest from their

closest sink• Scalable: both tiers grow, load per

mote remains constant.•Live network (emstar/emview)•3 sinks, 55 motes• color-coded clusters*With Henri Dubois-Ferrière, EPFL

Multi-hop data extraction characteristics using Tiny Diffusion

• Collected basic network characteristics to verify readiness for sensor deployment

• Average system loss rates analyzed over fixed intervals and related to nodes of with various: average, minimum, and maximum hop counts (under 3% end to end)

• Additional nodes deployed to augment persistent ESS topology to study effects such as loss experienced by nodes introduced with less ground clearance.

• UCB/Intel GDI deployment has good results from their fielded borrow monitoring system using same Mote platform

Characterizing wireless channels*

• Great variability over distance (50-80% of communication range, vertical lines). • Reception rate not normally distributed around mean and standard deviation. • Real communication channel is not circular.

• 5 to 30% asymmetric links.• Not correlated with distance or transmission power. • Primary cause: differences in hardware calibration (rx sensitivity, energy

levels, etc.).• Time variations correlated to mean reception rate, not distance from transmitter.

*Cerpa, Busek et. al

NIMS Architecture: Robotic, aerial access to full 3-D environment Enable sample acquisition

Coordinated Mobility Enables self-awareness of

Sensing Uncertainty Sensor Diversity

Diversity in sensing resources, locations, perspectives, topologies

Enable reconfiguration to reduce uncertainty and calibrate

NIMS Infrastructure Enables speed, efficiency Low-uncertainty mobility Provides resource transport for

sustainable presence* (Kaiser, Pottie, Estrin, Srivastava,

Sukhatme, Villasenor)

Research Challenge:

Networked Info Mechanical Systems (NIMS)*

* P. Davis

• Core requirement is multi-hop time synchronization to eliminate dependence on GPS access at every node

Broadband ad hoc seismic array *

GPS is the usual way to time-sync data collection --GPS is the usual way to time-sync data collection -- but satellites are blocked in some interesting places but satellites are blocked in some interesting places

Under FoliageUnder FoliageUnder FoliageUnder Foliage

CanyonsCanyonsCanyonsCanyons

UnderwaterUnderwaterUnderwaterUnderwaterIndoorsIndoorsIndoorsIndoors

Sensor networks can propagate timeSensor networks can propagate time from nodes that have a sky view from nodes that have a sky view to those that don’t. to those that don’t.

Enabling technology: “RBS” -- a new form ofEnabling technology: “RBS” -- a new form ofsynchronization that exploits the nature of asynchronization that exploits the nature of awireless channel to achieve exceptional precision* wireless channel to achieve exceptional precision*

* Elson et al. OSDI 12/02

Time Synchronization in Sensor Networks

• Also crucial in many other contexts• Ranging, tracking, beamforming,

security, MAC, aggregation etc.• Global time not always needed• NTP: often not accurate or flexible

enough; diverse requirements!• New ideas

• Local timescales• Receiver-receiver sync• Multihop time translation• Post-facto sync

• Mote implementation• ~10 s single hop• Error grows slowly over hops

Sender Receiver

NIC

Physical Media

NIC

Propagation Time

Receiver

NICI saw itat t=4 I saw it

at t=5

1

3

2

A4

8

C

5

7

6B

10

D11

9

1

3

2

4

8

5

7

6

10 11

9

* Elson et al. OSDI 12/02

• Regulators require proof that the nitrate-laden treated water will not impact groundwater if used for irrigation.

• monitoring wells cost of $75K

each

• Vertical array of sensors will measure rate of diffusion of water and nitrate levels

• Observed nitrate levels, local model will trigger contribute to field-wide estimate of hazardous Nitrate levels

• Field wide estimate re. concentrations and trends fed back to sprinkler quantity

* T. Harmon

Contaminant Transport Monitoring: Palmdale Pivot Study *

Research Challenge: Distributed Representation, Storage, Processing

• In network interpretation of spatially distributed data• Statistical or model based filtering

• In network “event” detection and reporting

• Direct queries towards nodes with relevant data

• Trigger autonomous behavior based on events

• Expensive operations: high end sensors or sampling

• Robotic sensing, sampling

• Support for Pattern-Triggered Data Collection• Multi-resolution data storage and retrieval

• Index data for easy temporal and spatial searching

• Spatial and temporal pattern matching

• Trigger in terms of global statistics (e.g., distribution)

• Exploit tiered architectures

K V

K VK V

K V

K V

K V

K VK V

K V

K VK V

Tim

e

Tiered Data Processing*

• Processing uses a two tiered network.• Task divided into local

computation and cluster head computation.

• Scope of local computation depends on relative cost of local (blue-blue) and cluster-head (blue-red) communication

• Example: identify regions over which large gradient occurring

• Locally, large gradients detected and traversed (up to some scope)

• Gradients paths over length threshold identified and reported

• Each cluster head combines identification results and classifies

* T. Schoellhammer, et al

Research Challenge:Calibration, or lack thereof

• Storage, forwarding, aggregation, triggering useless unless data values calibrated

• Calibration = correcting systematic errors

• Sources of error: noise, systematic• Causes: manufacturing, environment, age,

crud

• Traditional in-factory calibration not sufficient

• must account for coupling of sensors to environment

• Nearer term: identify faulty sensors and flag data, discard for in-network processing

• Significant concern that faulty sensors can wreak havoc on in network processing

* Bychkovskiy , Megerian, Potkonjak

70º

85º69º

73º

61º

72º

Un-calibrated Sensors

72º

72º

72º

62º

Factory Calibrated Sensors; Later

Dust

72º

72º72º

72º

72º

72º

Factory Calibrated Sensors: T0

70º

71º

Research Challenge:Macroprogramming*

• How to specify what, where and when?

• data modality and representation, spatial/temporal

resolution, frequency, and extent

• How to describe desired processing?

• Aggregation, Interpolation, Model parameters

• Triggering across modalities and nodes

• Adaptive sampling

• Primitives

• Annotated topology/resource discovery

• Region identification and characterization

• Intra-region coordination/synch

• System health data, alerts

• Topology, Resources (energy, link, storage)

• Sensor data management (buffering, timing)

•…* Greenstein, Culler, Kohler…

Lessons

• Channel models

• Simplistic circular channel models can be very deceiving so experimentation and emulation are critical

• Named data

• Is the right model but its only a small step toward the bigger problem of Macroprogramming

• Duty cycling

• Critical from the outset…and tricky to get right--granularity, level (application or communication)

• Tiered Architectures

• One size doesn’t fit all and maybe it doesn’t fit any--distribution of resources (energy, storage, comm, cpu) across the distributed system is an interesting problem

• Its all a lot harder, and even more interesting than it looked 5 years ago

Follow up regarding IT aspects

• Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers, Computer Science and Telecommunications Board, National Research Council - Washington, D.C., http://www.cstb.org/

• Conferences: ACM Sensys (Nov 03), WSNA (today), IPSN, SNPA (ICC), Mobihoc, Mobicom, Mobisys, Sigcomm, Infocom, SOSP, OSDI, ASPLOS, ICASSP, …

• Whose involved:• Active research programs in many CS (networking, databases, systems,

theory, languages) and EE (low power, signal processing, comm, information theory) departments

• Industrial research activities at Intel, PARC, Sun, HP, Agilent, Motorola…• Startup activity at Crossbow, Sensicast, Dust Inc, Ember, …

• Related Funding Programs• DARPA SenseIT, NEST• NSF ITR, Sensors and sensor networks