Wireless sensor networks Overview & applications

Murat Demirbas

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Wireless sensor networks

A sensor node (mote)

8K RAM, 4Mhz processor magnetism, heat, sound, vibration, infrared wireless (radio broadcast) communication up to 100 feet costs ~$10 (right now costs $200)

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Outline

• Vision

Ubiquitous [pervasive | proactive] computing Design space Challenges

• Applications

Ecology monitoring Precision agriculture Asset management Military surveillance

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Ubiquitous computing

Mark Weiser, PARC, 1991

• The most profound technologies are those that disappear:

E.g., Writing: does not require active attention, but the information to be conveyed is ready for use at a glance (Periphery / calm technology)

We should not be required to live in computer’s world (OS, virtual reality), computers should become invisible and ubiquitous (disappear in background) in our physical world

Already computers in light switches, thermostats, stereos and ovens help to activate the world

• For such a technology, localization & scalability are critical

Location-aware devices Wireless communication Micro-kernel OS Distributed computing

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Ubiquitous computing…

• Ubiquitous PC:

Tab : post-it sized; e.g., badge, shrink/store window on a tab Pad : A4/letter sized; e.g., scrap computer, edit each window on a pad Board : yard sized; e.g., long-distance meetings, bulletin boards

• Ubiquitous computers to overcome information overload

“There is more information available at our fingertips during a walk in the woods than in

any computer system, yet people find a walk among trees relaxing and computers

frustrating. Machines that fit the human environment, instead of forcing humans to enter

theirs, will make using a computer as refreshing as taking a walk in the woods.”

iCompUbiquitous Computing Lab

@ Furnas 210

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Proactive computing

David Tennenhouse, Intel VP, 2000

• Moving from human-centered to human-supervised computing

150 million PCs versus 8 billion embedded computers Only 2% of computers are PCs

• Getting physical

embedded computers

• Getting real

real-time, fast responses from computers need to be arbitrated

• Getting out

human above the loop (hidden Markov models)

Reinventing computer science

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Next century challenges: Scalable coordination in sensor networks

Embedded Networked

Sensing

Control system w/Small form factorUntethered nodes

ExploitcollaborativeSensing, action

Tightly coupled to physical world

• Distributed local algorithms are needed for scalability!

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New Class of Computing

year

log

(p

eo

ple

pe

r c

om

pu

ter)

streaming informationto/from physical world

Number CrunchingData Storage

productivityinteractive

Mainframe

Minicomputer

Workstation

PC

Laptop

PDA

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Technology Push

• CMOS miniaturization

• Micro-sensors (MEMS, Materials, Circuits)

acceleration, vibration, gyroscope, tilt, magnetic, heat, motion, pressure, temp, light, moisture, humidity, barometric

chemical (CO, CO2, radon), biological, microradar, ...

actuators too (mirrors, motors, smart surfaces, micro-robots)

• Communication

short range, low bit-rate, CMOS radios

• Power

batteries remain primary storage, fuel cells 10x

solar, vibration, flow

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Design space

• Deployment (random vs manual)

• Mobility (static vs mobile; occasional vs continuous; active vs passive)

• Cost, Size, Resources (brick vs matchbox vs grain)

• Heterogeneity (homogenous vs heterogeneous)

• Communication modality (radio vs light vs inductive)

• Infrastructure (ad hoc vs infrastructure)

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Design space …

• Network topology (single-hop vs multihop)

• Coverage (sparse vs dense)

• Connectivity (connected vs intermittent vs sporadic)

• Network size (10 vs 100 vs 1000 vs 10,000 vs 100,000)

• Lifetime (day vs month vs year vs decade)

• QOS requirements (none vs real-time)

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Challenges in sensor networks

• Energy constraint

• Unreliable communication

• Unreliable sensors

• Ad hoc deployment

• Large scale networks

• Limited computation power

• Distributed execution

: Nodes are battery powered

: Radio broadcast, limited bandwidth, bursty traffic

: False positives

: Pre-configuration inapplicable

: Algorithms should scale well

: Centralized algorithms inapplicable

: Difficult to debug & get it right

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Assignment 1

Present in class one WSN or smartphone application

Outline the overall function of the WSN or smartphone in this application. What is the improvement it offers?

Specify the design parameters and challenges for the proposed system Enumerate the system requirements and challenges

Time for your presentation should be around 7 minutes

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References for assignment

1. Great Duck island

2. Agricultural applications

3. Analysis of a habitat monitoring application

4. NASA SensorWeb

5. Meteorology and Hydrology in Yosemite

6. Monitoring redwoods

7. ZebraNet

8. Virtual fences

9. Active visitor guidance system

10.UVA flock control

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References for assignment

1. Counter-sniper system

2. Self-healing land mines

3. Damage detection in civil structures

4. Smart-tag based data dissemination

5. Continuous medical monitoring

6. Elder care

7. Aware home

8. Smart kindergarten

9. Media production

10. Factory floor monitoring

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Assignment 2

• Summarize one of the following

Some computer science issues in ubiquitous computing (Weiser) Proactive computing (Tennenhouse) Next century challenges (Estrin.)

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Outline

• Vision

Ubiquitous [pervasive | proactive] computing Design space Challenges

• Applications

Ecology monitoring Precision agriculture Asset management Military surveillance

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WSN applications

• a new "scope" to a scientific endeavor

• a new approach to an engineering problem

• a new capability to a computing environment

• a new form of entertainment

• a new product opportunity

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• Monitoring nesting behavior of birds

Great Ducks experiment

• Detecting forest fires

• Detecting chemical or biological attacks

• Monitoring Redwood trees

Ecology monitoring

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Dense Self-Organized Multihop Network

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Temperature vs. Time

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13

18

23

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7/7/039:40

7/7/0313:41

7/7/0317:43

7/7/0321:45

8/7/031:47

8/7/035:49

8/7/039:51

8/7/0313:53

8/7/0317:55

8/7/0321:57

9/7/031:59

9/7/036:01

9/7/0310:03

Date

Tem

pera

ture

(C

)

Humidity vs. Time

35

45

55

65

75

85

95

Rel H

um

idit

y (

%)

101 104 109 110 111

2003, unpublished

Bottom Top

36m

34m

30m

20m

10m

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Precision agriculture

• Wireless sensor networks can be placed on farm lands to monitor temperature, humidity, fertilizer and pesticide levels

• Pesticide and fertilizer can only be applied when and where required

Pesticide and fertilizer per one acre costs $20 Considering 100,000 acres savings of $2 million possible

VineyardsBC

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Equipment Health Monitoring in Semiconductor Fab

Fab Equipment

Mote + Vibration Sensors

Ad Hoc Mote Network

Intranet

802.11 Mesh

Intranet isolation

Root Node

• Equipment failures in production fabs is very costly

Predict and perform preemptive maintenance

• Typical fab has ~5,000 vibration sensors

Pumps, scrubbers, … Electricians collect data by hand few times a year Sample: 10’s kilohertz, high precision, few seconds

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Put tripwires anywhere—in deserts, other areas where physical terrain

does not constrain troop or vehicle movement—to detect, classify &

track intruders

Project ExScal: Concept of operation

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Envisioned ExScal customer application

Gas pipeline

Border control

Canopy precludes aerial techniques

Rain forest – mountains – water

environmental challenges

Convoy protection

IEDHide SiteDetect anomalous activity

along roadside

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ExScal summary

• Application has tight constraints of event detection scenarios: long life but still low latency, high accuracy over large perimeter area

• Demonstrated in December 2004 in Florida

• Deployment area: 1,260m x 288m

• ~1000 XSMs, the largest WSN

• ~200 XSSs, the largest 802.11b ad hoc network

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Line in the sand project

• Thick line allows detection & classification as intruders enter the protected region; also allows fine grain intruder localization

• Grid of thin lines allows bounded uncertainty tracking

Thick Entry Line

A S S E T

1 km

250 m

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ExScal sample scenarios

Intruding person walks through thick line

• (pir) detection, classification, and fine-grain localization

Intruding vehicle enters perimeter and crosses thick line

• (acoustic) detection, classification, and fine-grain localization

Person/ATV traverses through the lines

• coarse-grain tracking

Management operations to control signal chains, change parameters, and programs dynamically; query status and execute commands