Team 8 – Feng Kai and Michael Thomsen02202, September 7 th, lecture 2 Wireless Sensor Networks for Habitat Monitoring Alan Mainwaring, Joseph Polastre,

Team 8 – Feng Kai and Michael Thomsen02202, September 7th, lecture 2

Wireless Sensor Networks for Habitat Monitoring

Wireless Sensor Networks for Habitat Monitoring

Alan Mainwaring, Joseph Polastre, Robert Szewczyk, and David Culler, June 2002

Alan Mainwaring, Joseph Polastre, Robert Szewczyk, and David Culler, June 2002

Presented by Team 8:

Feng Kai and Michael Thomsen,

September 2004

Presented by Team 8:

Feng Kai and Michael Thomsen,

September 2004


OutlineOutline

• Habitat monitoring• Network Requirements• System Architecture• Hardware and Design• Results


Why?Why?

Why are we interested in Habitat Monitoring?• Focus attention on a REAL network• Some problems have simple concrete solutions, while others

remain open research areas• Application driven approach separates actual problems from

potential ones, and relevant issues from irrelevant ones• Collaboration with scientists in other fields helps define

broader application space as well as scientific requirements


• How much can they vary?• What are the occupancy patterns during incubation?• What environmental changes occurs in

the burrows and their vicinity duringthe breeding season?

Scientific MotivationScientific Motivation

Questions• What environmental factors make for a good nest?



• Collect detailed occupancy data from a number of occupied and empty nests

• Validate a sample of sensor data with a different sensing modality

• Augment the sensor data with deployment notes (e.g. burrow depth, soil consistency, vegetation data)

• Try to answer the questions based on analysis of the entire data set

Methodology• Characterize the climate inside and outside the burrow



Solution• Deployment of a sensor network

• The impact of human presence can distort results by changing behavioral patterns and destroy sensitive populations

• Repeated disturbance will lead to abandonment of the colony

Problems• Seabird colonies are very sensitive to disturbances


Field StationsField Stations

Great Duck Island• Almost 1 km2 (237 acre) remote island• Coast of Maine• Large Breeding colonies of Leech’s

Storm Petrels and other seabirds

James San Jacinto Mountains Reserve• Small 0,1 km2 (29 acre) ecological preserve• Near Idyllwild, California

(about 100 km east of LA)• Studying nest boxes, and

ecosystems over time


Application RequirementsApplication Requirements

• Hierarchical network (local networks over longer distances)• Sensor network longevity (at least 9 months)• Operating off-the-grid (battery or solar cells) • Remote management (personnel just available 2-3 months)• Inconspicuous operation (should not disrupt natural

processes or behaviors under study)• System behavior (stable, predictable and repeatable behavior)• In-situ interactions (during deployment and maintenance)• Sensors and sampling (light, temperature, IR, humidity,

pressure)

General Requirements• Internet access


Patch Network

Gateway(low power)

System ArchitectureSystem Architecture

Base-Remote Link

Data Service

Internet

Client Data Browsingand Processing

Transit Network

Base-station(house-hold power)

Sensor Patch

Sensor Node(power)


Sensor NodesSensor Nodes

• Sensor nodes communicate andcoordinate with one another

• Battery powered• Small (5 x 3.8 x 1.25 cm3)• Mechanically robust• Weather-proofed (but ventilated

to avoid data distortion)

Sensor Nodes• Separated into two logical components:

• Computational module (MICA)• Sensing Module (Weather board)



Computational Module• Mica Platform

• Atmel Atmega 103 micro controller @ 4 MHz• 512 kb Flash memory• 916 MHz, 40 kbps radio• 2 AA batteries w/boost converter



Sensor Module• Mica Weather Board

• Temperature• Photoresistor• Barometric pressure• Humidity• Passive IR (Thermopile)

• Designed to coexist with other sensor boards• Low variation when interchanged with other sensors of same

model


Power ManagementPower Management

Sensor Node Power• Limited Resource (2 AA batteries)• Estimated supply of 2200 mAh at 3 volts• Each node has 8.128 mAh per day (9 months)• Sleep current 30 to 50 uA (results in 6.9 mAh/day for tasks)• Processor draws apx 5 mA => can run at most 1.4 hours/day• Nodes near the gateway will do more forwarding

75 minutes


Power ManagementPower Management

Compression• Does the compression require more power than transmission?• Even if it does it may be worthwhile to compress if data must

pass through many nodes• 2 - 4 times reduction by combining:

• Delta compression and• Standard compression algorithm:


GatewayGateway

Function• Relay sensor patch network data to base station• Coordinate the activity within the sensor patch• Provides additional computation and storage• Gateway to base station distance apx 100 m

Hardware• Strong-Arm based embedded system (CerfCube)• Runs an embedded version of Linux• Compact Flash memory• 2.5 W supplied by solar cells and lead-acid battery

inches


Base StationBase Station

• Connects to the Internet:• Great Duck Island: Two-way satellite• James Reserve: T1 line

Base Station• Laptop:

• Coordinates the sensor patches• Provides database service


The GizmoThe Gizmo

In situ Interaction (Planned)• PDA-sized device• Communicate directly with the sensor patch• Direct communication to the mote• Provides fresh readings• Interactively control the network (sampling rates, power

management etc)• Useful during initial deployment and

retasking of the network


CommunicationCommunication

Routing• Routing directly from node to gateway not possible• Approach proposed for scheduled communication:

• Determine routing tree• Each gate is assigned a level based on the tree• Each level transmits to the next and returns to sleep• Process continues until all level have completed

transmission• The entire network returns to sleep mode• The process repeats itself at a specified point in the future


Network RetaskingNetwork Retasking

Network Retasking• Initially collect absolute temperature readings• After initial interpretation, could be realized that information of

interest is contained in significant temperature changes• Full reprogramming process is costly:

• Transmission of 10 kbit of data• Reprogramming application: 2 minutes @ 10 mA• Equals one complete days energy

• Virtual Machine based retasking:• Only small parts of the code needs to be changed


ConclusionConclusion

Paper conclusion• Two small scale sensor networks deployed at

Great Duck Island and James Reserve (one patch each)• Results not evaluated• No calibration was done, development of auto-calibration

procedure suggested

Future• Develop a habitat monitoring kit


ProblemsProblems

• Base Station laptop must run unattended (problem with unscheduled system reboots (mostly fixed))

• Temperature Sensors:• Measured temperature inside the enclosure, rather than

ambient temperature• Good correlation with Cost Guard measurements on cloudy

days • Batteries:

• Only operational down to 2.5 V (expected down to 1.6V)

2002 Deployed Network• No new science about petrel breeding behavior, many insights

into how to build sensors that would yeild that science


ProblemsProblems

2002 Deployed Network• Rain affecting the humidity sensor and short-circuiting the

battery

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Po

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lati

on

att

riti

on

(%

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Time (days)

Failed humidity sensorTotal node population


Additional ReferencesAdditional References

• R. Szewczyk, J. Polastre, A. Mainwaring, D. Culler: ”Lessons from a Sensor Network Expedition”, UC Berkerly, Jan 2004

• J. Polastre: ”Design and Implementation of Wireless Sensor Networks for Habitat Monitoring”, Research Project, 2003

• www.greatduckisland.net


2003 Network2003 Network

New design• Lithium battery based• New enclosure

Burrow, Ground, D-Cell, 2002 Ground

Team 8 – Feng Kai and Michael Thomsen02202, September 7 th, lecture 2 Wireless Sensor Networks for Habitat Monitoring Alan Mainwaring, Joseph Polastre,

Documents

feng kai

michael thomsen02202

burrow slide

time slide

scientific requirements

sample of sensor data

vegetation data

real network