CSE 410/510 Sensor NetworksWinter 2010

Lecture 1: Course OverviewIntroduction to Sensor Networks

Wireless Sensor Networks

Sensing Computing

Communication

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

• Enables spatially and temporally dense environmental monitoring

Logistics

Location and Time: Mon/Wed 12:00 – 13:50 pmFAB 150

Office Hours: Mon 4:00 – 5:00 pm

Book:No book. Refer to Lectures and supplementary material.

Pre-requisites: Java programming or equivalent

StaffInstructor: Nirupama BulusuAssistant ProfessorComputer Science Department Portland State UniversityOffice: FAB 120Phone: 503 725 2404 E-mail: nbulusu AT cs.pdx.eduOffice Hours: Monday 4 – 5 pmURL: http://www.cs.pdx.edu/~nbulusu

Course URL: http://sys.cs.pdx.edu/trac/syn/wiki/CS510Class mailing List: cs497-win10@googlegroups.com

Grading

5% Class Participation65% Labs and Project15% Midterm Exam15% Final Exam

Tentative Schedule

http://sys.cs.pdx.edu/trac/syn/wiki/CS510/LectureSchedule

On Sensor Networks

“One of the 10 technologies that will change the world.”

MIT Technology Review, 2003

On Sensor Networks

“More than half a billion sensor nodes will ship for wireless sensor applications in 2010 for an end-user market worth at least $7 billion”

“Demand growing at 300% between 2004 and 2005”.

ON World, a wireless research firm.

Burgeoning Research and Commercial ActivityNSF Research Centers

Center for Embedded Networked Sensinghttp://cens.ucla.edu

More than 100 Companies (many started after 2003)Crossbow, Sensoria, Millennial Net, Sensicast, Tendril

Networks, Ember, EnOcean, Dust Networks, Chipcon, Arch Rock Corporation, Moteiv, Ubisense (UK), SpeedInfo, Grape Networks, Aleier, Gentag, Electrobit, Blue Vector, MeshNetics, Sybase, Savi, Synapsense, NTT Docomo.

Besides Intel, Microsoft, Sun, Cisco, Motorola, Nokia.

Technology Trends

Moore’s lawMicro-electro Mechanical Systems (MEMS)Energy capacity miniaturization

Moore’s Law

Source: Wikimedia Commons

Micro Electro Mechanical Systems (MEMS)System-on-chip integration of mechanical

elements, sensors, actuators and electronics

First introduced in automobiles (mid 90s) - MEMS accelerometers for air-bag

deployment systems

SensorsPassive elements:

• seismic, acoustic, infrared, strain, salinity, humidity, temperature

Passive Arrays: • imagers (visible, IR), biochemical

Active sensors: radar, sonar • High energy, in contrast to passive elements

Technology trend: • use of IC technology for increased robustness, lower cost, smaller

size

• COTS adequate in many of these domains; biochemical work in progress

Sensor Node Energy Roadmap

20002000 20022002 20042004

10,0010,0000

1,0001,000

100100

1010

11

.1.1

Ave

rag

e P

ow

er

(mW

)

• Deployed (5W)

• PAC/C Baseline (.5W)

• (50 mW)

(1mW)

Rehosting to Rehosting to Low Power Low Power COTSCOTS (10x)(10x)

--System-On-System-On-ChipChip-Adv Power -Adv Power ManagementManagementAlgorithms Algorithms (50x)(50x)

Source: ISI & DARPA PAC/C Program

Comparison of Energy Sources

Power (Energy) Density Source of Estimates

Batteries (Zinc-Air) 1050 -1560 mWh/cm3 (1.4 V) Published data from manufacturers

Batteries(Lithium ion) 300 mWh/cm3 (3 - 4 V) Published data from manufacturers

Solar (Outdoors)

15 mW/cm2 - direct sun

0.15mW/cm2 - cloudy day. Published data and testing.

Solar (Indoor)

.006 mW/cm2 - my desk

0.57 mW/cm2 - 12 in. under a 60W bulb Testing

Vibrations 0.001 - 0.1 mW/cm3 Simulations and Testing

Acoustic Noise

3E-6 mW/cm2 at 75 Db sound level

9.6E-4 mW/cm2 at 100 Db sound level Direct Calculations from Acoustic TheoryPassive Human

Powered 1.8 mW (Shoe inserts >> 1 cm2) Published Study.

Thermal Conversion 0.0018 mW - 10 deg. C gradient Published Study.

Nuclear Reaction

80 mW/cm3

1E6 mWh/cm3 Published Data.

Fuel Cells

300 - 500 mW/cm3

~4000 mWh/cm3 Published Data.

With aggressive energy management, sensorWith aggressive energy management, sensor networks could live in perpetuity.networks could live in perpetuity.Source: UC Berkeley

Communication/Computation Technology

Assume: 10kbit/sec. Radio, 10 m range.Assume: 10kbit/sec. Radio, 10 m range.

Large cost of communications relative to computation Large cost of communications relative to computation continuescontinues

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

Source: ISI & DARPA PAC/C Program

State-of-the-Art

Telos Mote(Source: David Culler, Berkeley)

Sun SPOT(Source: Sun Microsystems)

Telos (2005)Single board philosophy

Robustness, Ease of use, Lower CostIntegrated Humidity & Temperature sensor

First platform to use 802.15.4 (Zigbee)CC2420 radio, 2.4 GHz, 250 kbps (12x mica2)3x RX power consumption of CC1000, 1/3 turn on timeSame TX power as CC1000

Motorola HCS08 processorLower power consumption, 1.8V operation,faster wakeup time40 MHz CPU clock, 4K RAM

(Source: D. Culler, UC Berkeley)

TelosPackage

Integrated onboard antenna +3dBi gainRemoved 51-pin connectorEverything USB & Ethernet based2/3 A or 2 AA batteriesWeatherproof packaging

Support in TinyOS 2.0 Release

Co-designed by UC Berkeley and Intel ResearchAvailable Moteiv (moteiv.com)

(Source: D. Culler, UC Berkeley)

Sun SPOT (2006)

Programmable entirely in Java using the Squawk VM (runs on bare metal, no OS)

Interface with desktops, laptops using USB

Sun SPOT

Processor Board180 MHz, 32-bit ARM 920T core processor,

802.15.4 radio, 512 KB RAM, 4MB Flash memory

Battery3.6V rechargeable 750mAh lithium-ion

battery, consumes only 36 μA in deep sleep mode

Sensor BoardAccelerometer, light, temperature sensorseight multicolored 24-bitLEDs, 2 push-button

control switches, 5 digital I/O pins, 6 analog inputs, and 4 digital outputs

Application Drivers

• Most applications fall into of one of 3 categories*– Monitoring Space– Monitoring Objects– Monitoring Interactions of Objects and Space

* Classification proposed by Culler, Estrin, Srivastava

Monitoring Space

• Environmental and Habitat Monitoring• Building automation• Precision Agriculture• Data Centers• Indoor Climate Control • Military Surveillance• Treaty Verification• Intelligent Alarms

Precision Agriculture

The “Wireless Vineyard”– Sensors monitor

temperature, moisture– Roger the dog collects the

data

Source: Richard Beckwith,Intel Corporation

Data Center Monitoring

The thermal image of a cold aisle in a data center. The infrared thermal image shows significant variations on intake air temperature across racks and at different heights. (Source: Microsoft Research)

DC Genome System Overview

Monitoring Objects

• Structural Monitoring• Eco-physiology• Condition-based Maintenance• Medical Diagnostics• Urban terrain mapping

Example: Condition-based MaintenanceIntel fabrication plants

– Sensors collect vibration data, monitor wear and tear; report data in real-time

– Reduces need for a team of engineers; cutting costs by several orders of magnitude

Source: Mark Yarvis, Intel Corporation

Monitoring Interactions between Objects and Space• Wildlife Habitats• Disaster Management• Emergency Response• Ubiquitous Computing• Asset Tracking• Health Care• Manufacturing Process Flows

Example: Habitat MonitoringThe ZebraNet Project:Collar-mounted sensors

monitor zebra movement in Kenya

Source: Margaret Martonosi, Princeton University

DataBase station (car or plane)

Data

Data

Store-and-forward communications

Data

Tracking node with CPU, FLASH, radio and GPS

Sensor Network Attributes ZebraNet Other Sensor Networks

Node mobility Highly mobile Static or moderate mobile

Communication range Miles Meters

Sensing frequency Constant sensing Sporadic sensing

Sensing device power Hundreds of mW Tens of mW

The Computing ChallengeBuild Robust, Long-lived systems that can be

un-tethered (wireless) and unattended

Communication will be the persistent primary consumer of scarce energy resources (MICA Mote: 720nJ/bit xmit, 4nJ/op)

Autonomy requires robust, adaptive, self-configuring systems

Leverage data processing inside the network

Computation near data source to reduce communication overhead

Collaborative signal processingAchieve desired global behavior with localized

algorithms (distributed control)

Some Problems

Calibration = correcting systematic errors in sensor dataCauses: manufacturing,

environment, age, crud

Localization = establish spatial coordinates for sensors and target objects

Energy Conservationlow-power media

access; power-aware routing of data packets

Macro-programming = high-level program for a sensor network; not low-level programs for individual sensors

The Engineering (Systems) ChallengeImmense ScaleLimited AccessExtreme Dynamics

What are the Building Blocks?

Calibration, or lack thereof

Calibration = correcting systematic errorsSources of error: noise,

systematicCauses: manufacturing,

environment, age, crud

Traditional in-factory calibration not sufficientmust account for coupling of

sensors to environment

Source: Vladimir Bychkovsky

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

Localization

A mechanism for discovering spatial relationships between objects

MathematicallyGiven: xi, cij for some i, j € {1,

…N}Estimate: xs for any s

1 (0,0,0)2 3

4(100,0,0)

56

7

8

9

10 11

C23 = 5

C5.11 = 5

Time SynchronizationAlso crucial in many other

contextsRanging, tracking,

beamforming, security, medium access control, aggregation etc.

Global time not always neededNTP: often not accurate or flexible

enough; diverse requirements!New ideas

Local timescalesReceiver-receiver syncMultihop time translationPost-facto sync

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

Why can we not use Internet protocols and architecture as is?Internet routes data using IP Addresses in Packets and Lookup

tables in routersHumans get data by “naming data” to a search engineMany levels of indirection between name and IP addressWorks 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

Like the “next tier of the Internet”

IP on Small Devices

IP for Smart Objects (IPSO) Alliance - http://www.ipso-alliance.org- Consortium of many companies- Lightweight IP stack for embedded

devices- 6LowPAN – IPv6 over IEEE 802.15.4- Enable “Internet of Things”

Wireless Channel Characterization*

Great variability over distance (50-80% of communication range, vertical lines). 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.).

*Cerpa, Busek et. al

Reception vs. Distance

*Cerpa, Busek et. al

Asymmetry vs. Power

*Cerpa, Busek et. al

Reliability vs Reporting frequency

Initially, reliability increases linearly with reporting frequencyThere is an optimal reporting frequency (fmax), after which

congestion occursFmax decreases when the # of nodes increases

Heterogeneity

Tiered Architecture for scalability, longevityOne size does not fit all….Combine heterogeneous

devices as in memory hierarchiesSmall battery powered Motes (Mica2 8 bit microcontrollers,

TOS, 10s of Kbps, ~600kbytes storage) hosting in situ sensors

Larger solar powered Micro-servers (32-bit processors, Linux OS, 10s of Mbps, ~100 Mbytes storage)

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)

Networked Info Mechanical Systems

Security and Privacy

In sensing layer and physical coupling

Problem #1Privacy and AnonymityProbably the most important issue in many

applications of WSNsLost battle?

“The End of Privacy” [Economist, May 1, 1999]Privacy vs. convenience/security/costPreventing sensing is beyond technology

Can’t just jam RF signal, disable tags etc. as for RFID

There are just way too many sensing modalities!

Eventually, legal infrastructure to take care of extreme cases and to limit use of sensed data

Problem #2Securing the sensing layerProblem: manipulation of the environment or the sensing

channel for cheating and attacks on integrity of sensingE.g. fooling sensor networks that detect violation of

regulations or detect conditions dangerous to safetyBlocking sensors, camouflage, artificial events etc.

Hard to combatBeyond cryptography?

Borrow techniques from estimation theory, data mining etc.?

Inevitably application/scenario specificWhat are reasonable restrictions on adversary

capabilities?Reputation-based approach

Problem #3Securing the estimation of physical characteristics of

WSNtime, location, calibration parameters, topology

etc.Essential to core functioning of WSNs

detect, identify, localize, track physical phenomenon,

Problem #4Node compromiseUnattended, unsecured, cheap nodes in WASN can

be physically compromised easilyAdversary gets hold of crypto keysImpacts in-network processingHow to detect it and prevent it?

Data Management

Data Centric CommunicationsData 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 processingNot just end to end data deliveryNot just a database query--storage is also

constrained

TinyDB: Sensor Network as a Database Embedded: nesC/TinyOS

PC: Java GUI or command window Applications can use TinyDB API

1/8/2007

Macro-programming• 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

Lessons

Channel modelsSimplistic circular channel models can be very

deceiving so experimentation and emulation are critical

Named dataIs the right model but its only a small step toward

the bigger problem of in-network processing, macro-programming

Lessons

Duty cycling

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

Tiered Architectures

Optimal distribution of resources (energy, storage, comm,cpu) across the distributed system is an interesting problem

NIMS provides an exciting/powerful tier in system ecology

Systems need to be programmable/taskable

Participatory Sensor Networks*Sensor networks for urban applications will

form the “next tier of the Internet”+

- Leverage cell phones for acoustic and image sensing

- Using internet search, blog, and personal feeds, along with automated location tags, to achieve context, and in network processing for privacy and personal control

* Source: Deborah Estrin, UCLA

+ Source: David Culler Berkeley

Ethical, Legal, & Social Implications

Pervasive Computing“ The ethical, legal, and policy issues must be addressed during the design and use stages of these Embedded Network systems…[A] more in-depth analysis of public policy issues is urgently needed that would lead to appropriate recommendations for solving likely problems.”—National Academy of Sciences

Ethical, Legal, & Social Implications * D. Cuff, J. Kang

“Interesting” Developments RFIDs: You might not care about someone tracking your razor blades…but what about your tires? (Jay Warrior, Agilent) Camera phones, Green Phones Fusion of sensor modalities