Sensor Networks ACOE 422 Adopted from IEEE Tutorial on Sensor Networks

Sensor Networks

ACOE 422

Adopted from IEEE Tutorial on Sensor Networks

Sensing

Remote Sensing In-situ Sensing

Networked Sensing

Remote Sensing

As the term implies, sensors not co-located with phenomenon

Generally, sensors detect electromagnetic radiation from target passive: spectroscopes or

magnetometers, camera or TV active: laser distance finders,

radar scanners Locations

Near-surface (e.g. aerial photography), satellites [From http://www.science.edu.sg/]

Remote Sensing Applications

Remote Sensing

Mapping Earth’s Physical Properties

Prospecting for minerals and other resources

Agriculture: Mapping Vegetation

Urban Reconnaisance

Planetary Exploration

[From http://rst.gsfc.nasa.gov ]

Remote SensingAnalysis and Systems Analysis: mostly image-processing

related spectral analysis image filtering classification (maximum

likelihood) principal components analysis

Systems GIS: Geographic Information

Systems Flexibly combine various

images

[From http://erg.usgs.gov/ ]

In-situ Sensing

Sensors situated close to phenomena accurate, microscopic

observations … but with limited range

General uses in engineering applications Condition monitoring Performance tuning

[From http://www.sensorland.com/ ]

State of In-Situ Sensing

System architecture One or a small cluster of

dumb sensors … wired to a data

acquisition unit … or to a device controller

Very impressive device engineering Sensing accuracy Miniaturization Ruggedization Calibration

[From http://www.sensorland.com/ ]

Current Applications

Agriculture Plant positioning Precision hoeing

Automotive, highway systems Engine pressure and oxygen

monitoring Suspension positioning of racing cars

and motorcycles Road noise measurements

Aviation Aircraft engine pressure

Entertainment Rotational stability of Ferris wheels Acoustic adjustments in symphony

halls

Manufacturing processes Humidity in compressed air Tail-lift testing Fan pitch monitoring Calibrating bullet speeds

Railways Braking control load distribution Traction control in slippery

conditions Shipping

Rudder positioning Space

Rocket engine valve positioning Utilities

Water distribution and storage

Industry

Many, many sensor manufacturers Sensor Magazine’s buying

guide lists 240 manufacturers of acceleration measurement sensors!

Some names Agilent, Wilcoxon,

Crossbow, GEMS, Penny and Giles, Delphi, Motorola, ScanTek, Bosch, National Instruments

The applicability of sensors is vast …

Companies often stratified by Industry segment (e.g.

Delphi automotive) Application (e.g. vibration

measurement, or pressure measurement)

… and differentiate themselves by offering a wide range of

products with different specifications or differing form factors

Standardization

Fair amount of activity since the mid-1990s IEEE P1451.x

Two thrusts: Sensor board to processor

interfaces wired/wireless bus,

point-to-point both for data access and

sensor self-identification

Object-oriented abstractions for sensor data and application interaction

[From Plug-and-Play Sensors Sensors Magazine, December 02]

Sensing

Remote Sensing In-situ Sensing

Networked Sensing

Networked Sensing Enabler

Small (coin, matchbox sized) nodes with Processor

8-bit processors to x86 class processors

Memory Kbytes – Mbytes range

Radio 20-100 Kbps initially

Battery powered Built-in sensors!

The Opportunity

Large-scale fine-grain in-situ sensing and actuation 100s to 1000s of nodes 5m to 50m spacing

Inherently collaborative … sensors cannot act alone because they have limited view

Inherently distributed … since communication is energy-intensive (we’ll see this later)

Embedded (In-Situ) Networked Sensing

Applications

Application Areas

Seismic Structure Response

Contaminant Transport

Marine Microorganisms

Ecosystems, Biocomplexity

Structural Condition Assessment

Seismic Structure Response

Interaction between ground motions and structure/foundation response not well understood.

Current seismic networks not spatially dense enough to monitor structure

deformation in response to ground motion.

to sample wavefield without spatial aliasing.

A Wired Seismic Array

A Wireless Seismic Array

Use motes for seismic data collection Small scale (10 or so) Opportunity: validate

with existing wired infrastructure

Experiments Factor building Four Seasons building

Condition Assessment

Longer-term Challenges:

Detection of damage (cracks) in structures

Analysis of stress histories for damage prediction

Applicable not just to buildings Bridges, aircraft

Contaminant Transport

Industrial effluent dispersal can be enormously damaging to the environment marine contaminants groundwater contaminants

Study of contaminant transport involves Understanding the physical (soil

structure), chemical (interaction with and impact on nutrients), and biological (effect on plants and marine life) aspects of contaminants

Modeling their transports Mature field! Fine-grain sensing can help

Responsible Party contributions for cleanup of “Superfund” sites (source: U.S. EPA, 1996)

1 9 8 0 1 9 8 5 1 9 9 0 1 9 9 5 0

42

681012

Bil

lion

s of

dol

lars

Lab-Scale Experiments

Use surrogates (e.g. heat transfer) to study contaminant transport

Testbed Tank with heat source

and embedded thermistors

Measure and model heat flow

[From CENS Annual Technical Report, 03]

Field-Level Experiments

Nitrates in groundwater Application

Wastewater used for irrigating alfalfa

Wastewater has nitrates, nutrients for alfalfa

Over-irrigation can lead to nitrates in ground-water

Need monitoring system, wells can be expensive

Pilot study of sensor network to monitoring nitrate levels

Marine Micro-organism Monitoring

Algal Blooms (red, brown, green tides) impact Human life Industries (fisheries and

tourism) Causes poorly understood,

mostly because Measurement of these

phenomena can be complex and time consuming

Sensor networks can help Measure, predict, mitigate

Lab-Scale Experimentation

Build a tank testbed in which to study the factors that affect micro-organism growth

Actuation is a central part of this Can’t expect to deploy at

density we need Mobile sensors can help

sample at high frequency Initial study:

thermocline detection

1m

Tethered-robot sample

collectors

Ecosystem Monitoring

Remote sensing can enable global assessment of ecosystem

But, ecosystem evolution is often decided by local variations Development of canopy, nesting patterns often

decided by small local variations in temperature In-situ networked sensing can help us understand

some of these processes

James Reserve

Clustered architecture Weather-resistant housing design Sensors

Light, temperature, pressure, humidity

Great Duck Island

Study nesting behavior of Leach’s storm petrels

Clustered architecture: 802.11 backbone multihop sensor cluster

Now running for several months

Base-Remote Link

Data Service

Internet

Client Data Browsing and Processing

Transit Network

Basestation

Gateway

Sensor Patch

Patch Network

Sensor Node

Challenges and Goals

Networked Sensing Challenges

Energy is a design constraint Network lifetime now becomes a metric

Interaction with the physical world A lot messier than we’ve been used to

Autonomous deployment We’re not used to building systems that can self-deploy

Single (or a small number) of users Need a different model

Communication is Expensive

The Communication/Computation Tradeoff Received power drops off as the fourth power of distance

10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Gets networking and distributed systems researchers excited! At short transmission ranges, reception costs are significant

Implications Avoid communication over long distances Cannot assume global knowledge, or centralized solutions Can leverage data processing/aggregation inside the networkCan leverage data processing/aggregation inside the network

Communication is Expensive The Communication/Computation Tradeoff

Received power drops off as the fourth power of distance

10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Gets networking and distributed systems researchers excited! At short transmission ranges, reception costs are significant

Implications Avoid communication over long distances Cannot assume global knowledge, or centralized solutions Can leverage data processing/aggregation inside the networkCan leverage data processing/aggregation inside the network

The Goal

An infrastructure that can be used by many different sensing applications

Components of Infrastructure

Processor Platforms Radios Sensors

Operating Systems

Localization Time Synchronization Medium Access Calibration

Collaborative Signal Processing

Data-centric Routing Data-centric Storage

Querying, Triggering

Aggregation and Compression

Collaborative Event Processing

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Secu

rity

Tutorial Overview

Discuss components bottom-up Present a networking and systems view In each topic

Present relatively mature systems (to the extent they exist) first

Then discuss systems research Finally, present some theoretical underpinnings

Hardware: Platforms, Radios and Sensors

Overview

Berkeley motes

Mantis

Cerfcube

Processors Atmel StrongARM X-Scale

Radios Chipcon CC1000 Bluetooth Zigbee

Sensors Platforms

Mica-2 Stargate iMote

Processors

Architecture • CISC vs. RISC • Von-Neumann vs. Harvard • Most embedded

processors/MCUs are RISC Harvard architecture!

Speed Cache/Memory Power Dissipation

Atmel Atmega 128L

Harvard 8-bit RISC Speed : 8MHz Memory:

128KB program memory 4KB SRAM 4KB EEPROM

Power draw: run mode: 16.5mW sleep mode: < 60µW

Microcontroller used in Mica2

Intel StrongARM SA1100

Harvard 32-bit RISC Speed: 206MHz Cache:

16KB instruction cache 8KB data cache

Power draw: run mode : 800mW idle mode : 210mW sleep mode : 50µW

Microprocessor used in iPAQ H3700

Intel XScale PXA-250

A successor to the StrongARM Harvard 32-bit RISC Speed : 200/300/400MHz Cache:

32KB instruction cache 32 KB data cache

Power draw: run mode : 400mW idle mode : 160mW sleep mode : 50µW

Microprocessor used in iPAQ H3900

Atmel ATMEGA128(L) StrongARM SA1100

Intel XScale

Type MicroController Microprocessor Microprocessor

Architecture Harvard, 8-bit RISC Harvard 32-bit RISC Harvard 32-bit RISC

Speed 8MHz/16MHz 206MHz 200/300/400 MHz

Cache/

Memory

128KB programming memory

4KB data memory

16KB instruction cache

8KB data cache

32KB instruction cache

32KB data cache

Power Draw run mode: 16.5mW

sleep mode: < 60µW

run mode: 800 mW

idle mode: 210 mW

sleep mode: 50µW

run mode: 400 mW

idle mode: 160mW

sleep mode: 50µW

Example Platforms

Mica2 mote Compaq iPAQ3700 Compaq iPAQ3900

Processor Comparison

Radios

Low power, short range a must Relevant criteria:

Frequency Modulation scheme Encoding scheme Data rates Frequency diversity

Power considerations Receive power Transmit power For short range

communication, the two are comparable!

[From http://www.rfm.com]

RFM TR1000

OOK/ASK 433/916 MHz Date rate up to 115.2Kbps Power draw:

Rx: 3.8mA Tx:12mA

No spread spectrum support Used in earlier platforms

Now largely obsolete

[From http://www.chipcon.com]

Chipcon CC1000

FSK, up to 76.8 KBaud Frequency range 300 – 1000

MHz programmable frequency in

250Hz steps Encoding scheme: NRZ,

Manchester Current Draw:

programmable, min 5.3 mA, max 26.7 mA

Used in Mica2

Bluetooth

Modulation Gaussian Filtered FSK (GFSK) 2.4GHz (ISM)

Diversity coding Frequency Hopping Spread

Spectrum. Power consumption:

Transmit: 150 mW Receive: 90 mW

Gross data rate: 6-12 KBps Point-to-point and point-to-multipoint

protocols. up to 8 devices per piconet

IEEE 802.15.4

Carriers and Modulation 868 Mhz, 900 Mhz ISM, 2.5 Ghz

ISM Different modulations at different

frequencies Diversity coding

Direct sequence MAC layer

CSMA/CA Status

Standards complete, radios expected soon

Zigbee Alliance is pushing for this Home and industrial automation They have defined a simple topology

construction/routing layer on top of this

Sensors

We describe some of the sensors types commonly used in some of our applications Theory of operation Performance parameters

[From Sensors Magazine, “How to Select and Use the Right Temperature Sensor”]

Temperature Sensor

Operation Change in resistance

induced by temperature change

Semiconductor or metal Differential changes in

resistance Thermocouples,

thermopiles Parameters

Temperature range Linearity Resolution (sensitivity)

Photo Sensors

Operation Uses Photoconductive material

Resistance decreases with increase in light

Parameters Peak Sensitivity Wavelength Illuminance Range Ambient Temperature

Accelerometer

Operation Capacitive Piezoresistive

Parameters Single Axis, 2-Axis, 3-Axis Acceleration range ( in g) Acceleration sensitivity ( in

mV/g) Dynamic acceleration.

(vibration), static acceleration. (gravity)

Shock survival limit ( in g)

Displacement Sensors (LVDT)

Operation Iron core between Primary

and Secondary Coils Displacement causes

voltage output Parameters

Range Linearity Sensitivity

Humidity Sensors

Operation Capacitive Polymer dielectric that absorbs or releases water

proportional to humidity Change in capacitance measure of humidity

Parameters Range (e.g. 10% to 90%) and Accuracy Sensitivity Ambient Temp. Range Response Time

Magnetic Field Sensor

Operation Ferro-magnetic material (e.g. iron, nickel, cobalt) change

shape and size when placed in a Magnetic Field Parameters

Number of axes ( one, two, three) – direction of magnetic field

Range (Gauss) Noise Sensitivity Linearity Sensitivity (V/Gauss)

Pressure Sensors

Operation Pressure information is

converted to displacement which is measured using a Disp. Sensor

Parameters Range Temperature Sensitivity Accuracy, Resolution Response Time

Platforms

Several platforms in the community Various combinations of the processors, radios and

sensors discussed so far

MICA 2

Atmel processor Multi channel radio receiver:

Chipcon CC1000 Light, Temperature, pressure,

acceleration, acoustic, Magnetic sensors

Wireless Reprogramming Software Platform

TinyOS

[http://www.xbow.com]

Intel Research Mote

Strong Arm Processor 12 Mhz, 64kB SRAM, 512 kB

FLASH Bluetooth for communication Digital sensor interface UART, JTAG Link Layer reliability and security Battery Life

> 6 months with AA cells and 1% duty cycle

Software platform TinyOS Abstraction layer for Bluetooth [From Intel Corp.]

Stargate

High Processing Node (Gateway)

400Mhz Xscale processor, 64MB RAM, 32MB Flash

3.5'' x 2.5 '' Ethernet, UART, JTAG, USB

via daughter card Connectors for sensor boards Standard Mica2 Connector Software Platform

Linux

[From Crossbow Inc., http://www.xbow.com]

GNOME

16-bit MSP chip 12-bit ADC 60k Flash Solar panel for rechargeable

battery Sensors for temperature,

humidity Compass and GPS Bluetooth support, RF radio,

Ethernet

http://cmlab.rice.edu/projects/sensors

Medusa MK-2

High Capability node Two Microcontrollers

8-bit RISC Atmega 128L 4MHz, 32k Flash, 4kb RAM, JTAG, UART

32-bit RISC, 40MHz, 1MB Flash, 136KB RAM, JTAG, UART, GPS

They communicate via UART On Board Power Mgmt. And

Tracking Unit

Medusa MK-2

Power consumption 200 mW (fully operational)

RF Radio compatible with Mica motes

Two accessory board for ultrasonic distance measurement

Software Platform PALOS (power aware light

weight OS) Event Driven Priority Support

[http://nesl.ee.ucla.edu]

MANTIS

Hardware: The Nymph Chipcon CC1000 10 bit ADC GPS Support UART and JTAG

interface 3.5 x 5.5 sq. cm Support for up to 8

batteries

[http://mantis.cs.colorado.edu]

MANTIS

MOS Unix like development and

runtime environment Multithreaded with priority

support. But Round-Robin (not event driven)

New H/w addition via a new H/w driver

Remote login and reprogramming via wired and wireless

[http://mantis.cs.colorado.edu]



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Operating Systems

Depending the platform, various choices Tiny OS [Hill et al. 2000] Embedded Linux -OS [Shih et al. 2001]

We focus on Tiny OS Most different from the *nix variants

TinyOS

De-facto sensor programming platform Initially developed by UC-Berkeley History

v0.5.1 – released at 10/19/2001 v0.6.0 – released at 02/13/2002 v0.6.1 – released at 05/10/2002 v1.0.0 – released at 10/14/2002 v1.1.0 – released at 09/23/2003

H/W Platforms using TinyOS

Crossbow Mica Crossbow Mica2 Intel Mote

CPU ATmega128L ATmega128L(4MHz) ARM core(12MHz)

Program Memory

128k Flash memory

128k Flash memory 512k Flash memory

Data Memory

4k SRAM 4k SRAM 64k SRAM

Radio RFM TR1000 ChipCon CC1000 Bluetooth

Data Rate 40 kbps 76.8 kBaud 1 Mbps

Frequency 916.50 MHz 315/433/868/915MHz

2.4GHz

TinyOS Programming Model

Event-driven execution no polling, no blocking

Concurrency intensive operation multi-threads based, no long-running thread

Component-based program := layering of components

No dynamic memory allocation program analysis and code optimization[Gay03] easy migration from software to hardware

TinyOS Architecture

A tiny scheduler + a graph of components 2-level tiny scheduler

task : run to completion (FIFO scheduling) event: immediately performed, preempt task

Component Task Frame Command handler Event handler

Command

Component

Event

Signal

Task

Command handlers

Event handlers

Frame Task

Call

TinyOS-nesC System programming language [Gay03]

To support TinyOS programming model Component specification

provides and uses interfaces interface contains commands and events

Component implementation Module : provide code and implement interfaces Configuration : connect uses-interface of component to provides-

interface of other component Support for concurrency

interf ace SendMsg { command result_t send(uint16_t, uint8_t, TOS_Msg *m); event result_ t sendDone(TOS_Msg *m, result_ t res);}interf ace ReceiveMsg { event result_ t receive(TOS_Msg *m);}interf ace StdControl { command result_t init();}

module AMStandard { provides { interf ace StdControl; interf ace SendMsg[uint8_t id]; interf ace ReceiveMsg[uint8_t id]; } uses { interf ace BareSendMsg as RadioSend; interf ace ReceiveMsg as RadioReceive; }}

AMStandardM

SendMsg ReceiveMsg StdControl

RadioSend RadioReceive StdControl

SendMsg ReceiveMsg StdControl

RadioCRCPacketM

RadioSend RadioReceive StdControl

Component Specification

Component Implementation

module AMstandard { .... }

I mplement { bool state; TOS_Msg* buff er;

command result_ t SendMsg.send[uint8_t id](uint16_t addr,uint8_t length, TOS_Msg* data) { .... post sendTask(); buff er = data; .... } event result_ t RadioSend.sendDone(TOS_Msg* msg, result_ t res) { .... signal SendMsg.sendDone[msg->type](msg, success); .... } event TOS_MsgPtr RadioReceive.receive(TOS_Msg* packet) { .... signal ReceiveMsg.receive[packet->type](packet); .... } task void sendTask() { ..... call RadioSend.send(buff er); .... }

confi guration GenericComm { .... }

implementation{ components AMStandard; components RadioCRCPacket as RadioPacket; components UARTFramedPacket as UARTPacket;

Control = AMStandard.Control; SendMsg = AMStandard.SendMsg; ReceiveMsg = AMStandard.ReceiveMsg; sendDone = AMStandard.sendDone;

AMStandard.UARTControl -> UARTPacket.Control; AMStandard.UARTSend -> UARTPacket.Send; AMStandard.UARTReceive -> UARTPacket.Receive;

AMStandard.RadioControl -> RadioPacket.Control; AMStandard.RadioSend -> RadioPacket.Send; AMStandard.RadioReceive -> RadioPacket.Receive;}

Code Wiring

A TinyOS application: DIM

DI M

GPSR Timer

Clock

HPLClock

Clocks

Temperature

ADC

HPLADC

I 2C

AM

RadioCRCPacket

CC1000RadioI nt

CC1000

UARTFramPacket

UART

UART

SW

HW

application

routing

message

packet

byte

Common System Components

AM (Active Message) Messaging layer

implementation that for packet de-muxing

RadioCRCPacket Provides simple radio

abstraction Send/receive packets

over radio

RadioCRCPacket

CC1000RadioI nt

RadioCRCPacket

MicaHighSpeedRadio

Mica2Radio(76.8 KBaud)

MicaRadio(40Kbps)

Common System Components UARTFramedPacket

provides serial communication to Host PC 19.2kbps(Mica/Mica2Dot), 57.6Kbps(mica2)

Timer provides periodic and one-shot timers

ADC abstraction of the analog-to-digital converter used by sensing components

Temperature, light sensor



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MAC Layer Issues

Energy-efficient MAC layers Topology control for higher energy-efficiency MAC and radio layer performance

Medium Access Control

Important design considerations Collision avoidance Energy efficiency Scalability in node density Latency Fairness Throughput Bandwidth utilization

Reduce idle listening, collisions, control overhead, overhearing

MAC Design in TinyOS

CSMA/Collision Avoidance Optional MAC layer acknowledgement (Mica) Hill et al. 2002

Synchronization

Start Symbol Search Receiving individual bits

StartSym Detection

RX

MAC Delay Transmitting encoded bits

TX

Ack

StartSym

Sensor-MAC (S-MAC)

Tradeoffs Higher latency, less fairness Higher energy efficiency

Major components in S-MAC Periodic listen and sleep Collision avoidance Overhearing avoidance Message passing

Combine TDMA and contention-based protocols

Ye et al., Infocom2002

Latency

Fairness Energy

Collision Avoidance

Solution: Similar to IEEE 802.11 ad hoc mode (DCF) Physical and virtual carrier sense Randomized backoff time RTS/CTS for hidden terminal problem RTS/CTS/DATA/ACK sequence

Overhearing avoidance Reserve channel for duration of entire message (rather than a

fragment) … so that others can aggressively sleep to avoid overhearing

Periodic Listen and Sleep

Reduce long idle time • Reduce duty cycle to ~ 10% (120ms on/1.2s off) • Longer time-slots than TDM, looser synchronization requirements

Schedule can differ Preferable if neighboring nodes have same schedule

• easy broadcast & low control overhead

Node 1 sleeplisten listen sleep

Node 2 sleeplisten listen sleep

Schedule 2

Coordinated Sleep

Nodes coordinate on sleep schedules Nodes periodically broadcast schedules New node tries to follow an existing schedule Nodes on border of two schedules follow both

Periodic neighbor discovery and synchronization Early part of listen interval devoted to this

Schedule 1 1

2

Implementation on Testbed

Platform Mica/Mica2 Motes TinyOS Used as NIC for x86/xscale embedded Linux box

Configurable S-MAC options Low duty cycle with

adaptive listen Low duty cycle without

adaptive listen Fully active mode (no

periodic sleeping)

S-MAC Performance

Two-hop network at different traffic loads

S-MAC consumes much less energy than 802.11-like protocol w/o sleeping

At heavy load, overhearing avoidance is the major factor in energy savings

At light load, periodic sleeping plays the key role

Source 1

Source 2

Sink 1

Sink 2

0

2

4

6

8

10

200

400

600

800

1000

1200

1400

1600

1800 Average energy consumption in the source nodes

Message inter-arrival period (second)

Ene

rgy

cons

umpt

ion

(mJ)

802.11-like protocol without sleep

Overhearing

avoidance

S-MAC

Adaptive Topology Control

Can we put nodes to sleep for long periods of time?

More aggressively than S-MAC Leverage redundant deployments

Topology adapts to Application activities Environmental changes Node density

Extend system lifetime Reduce traffic collision Complementary to topology control

schemes that adjust transmit power levels

Example: ASCENT

The nodes can be in active or passive state Active nodes forward data packets (using a routing

mechanism that runs on the topology). Passive nodes do not forward packets but might sleep or

collect network measurements. Each node joins the network topology or sleeps according to

the number of neighbors and packet loss as measured locally.

ASCENT State Transitions

Test

Passive Sleep

Active

after Tt

after Tp

after Ts

neighbors < NT and • loss > LT • loss < LT & help

neighbors > NT (high ID for ties); or loss > loss T0

NT: neighbor threshold

LT: loss threshold

T?: state timer values (p: passive, s: sleep, t: test)

Topology Control Schemes

Empirical adaptation: Each node adapts based on measured operating region. ASCENT (Cerpa et al. 2002)

Routing/Geographic topology based: Redundant links are removed. SPAN (Chen et al. 2001), GAF (Xu et al. 2001)

Cluster based: Workload is shared within clusters CEC (Xu et al. 2002)

Data/traffic driven: Nodes starts on demand using paging channel STEM (Tsiatsis et al. 2002)

Understanding Radio Vagaries

Notoriously unpredictable Variable environment noise Device calibration Non-linear signal strength decay Multi-path effect Transmission collision

Additional constraints for sensor networks Energy efficiency (Low power

radio) Possibly high density

deployment High packet loss, Asymmetry,

High temporal variance Zhao et al.

Impact on systems design Hardware/Physical Layer

Modulation Scheme Base-band Frequency Encoding Scheme

MAC Protocol Reliable Data Delivery Path Selection in Routing Congestion Control “Soft-state” Maintenance

Spatial Profile of Packet Delivery

Node positions

4B6B Encoding

High Tx Power

In-door

2hrs (7200 pkts)

“Gray Area” is evident in the communication range

Grey Area in Packet Loss Relatively large region of

poor connectivity Across a wide variety of

environments Spanning as large as 30%

of the effective transmission range

In-door

Out-door Unobstructed

4B6B Encoding

High Tx Power

High Packet Loss

Note: Nodes are not uniformly spaced. CDF is slightly bias to bad link.

Heavy tail in packet loss distributions for both in-door and habitat environments

Standard Deviation in Packet Loss

Window size = 40

4B6B Encoding

High Tx Power

Variability over time with large dynamic range



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What is localization?

Determining the location of a node in a global coordinate system

Availability of location information is a fundamental need Interpreting the data Routing (GPSR) Geo-spatial queries Location based addressing

Why not equip every node with GPS? GPS needs line of sight, cannot be used

in indoor environments in the presence of foliage

Early Schemes

Active Bat (AT&T) People wear badges which emit ultra-sound pulse Receivers mounted in a regular grid on ceiling Time of flight based triangulation (centralized)

CRICKET (MIT) Ultrasound ranging Fixed emitter infrastructure with known positions

RADAR (Microsoft Research) Uses existing 802.11 LAN Signal/Noise ratio of the targets used for localization

Ad-hoc Localization

Sensor nodes are randomly scattered

“Where am I”? Only a “small” fraction of

nodes have GPS Anchors

The rest have to infer their global positions somehow

General Approach

Find distance to neighboring nodes Ranging

Neighbors of anchors fix position relative to anchors Position fixing

Other nodes fix their positions relative to at least three neighbors

Iterative refinement

Ranging

Radio Received signal strength based Use a path loss model to estimate range Need careful calibration for accuracy

Can get to within 10% of radio range Examples: SpotON (Hightower et al.), Calamari (Whitehouse et al.)

Acoustic Use time of flight of sound (ultrasound) Potentially high accuracy: 1% of radio range May need code spreading to counter multipath effects Examples: Girod et al., Savvides et al.

•

Position Fixing Taxonomy

Topological Schemes Rely only on topology information Can result in very inaccurate localization Usually require less resources

Geometric Schemes Use geometric techniques to determine location Usually result in highly accurate position estimates May require more resources

d1

d2

d3

d1+d2+d3

Topological Schemes

DV-Hop (Niculescu et. al.) Find average distance or hop

davg distance = davg*(No of Hops) requires no ranging!!!

DV-Dist (Niculescu et. al.) Standard distance vector

algorithm range as metric

Refinement can help significantly

x

y

Geometric Schemes

Savvides et. al. Each anchor defines a

coordinate system, anchor is the origin.

nodes localize in this coordinate system using hop by hop lateration

nodes maintain (nodeId, x,y) 3 such tuples can be used to

localize Observations

Local coordinates may be translated, rotated or flipped versions of the global system

distance from anchor is invariant

Comparison of existing schemes

3 schemes (20% anchors, 1% error) Geometric (Savvides et al.,

Niculescu et. al.) Topological (DV-dist)

Localization extent Nodes localized within 2m

Topological scheme Higher localization extent

than geometric scheme. Need 10-11 neighbors

The State of Localization

Lots of research in the area Probably far from deploying robust systems in the

field Every component is hard and error-prone

Ranging Position-fixing



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Time Synchronization

Critical piece of functionality Applications:

Sample-level correlation Event time correlations

Wide variety of requirements Microsecond level for acoustic

localization Perhaps less for events Global? Post-facto?

Prior work NTP

Research Systems

RBS (Elson et al.) TPSN (Ganeriwal et al.) DMTS (Ping Su) LTS (van Greunen et al.)

Theory Optimal global

synchronization (Karp et al. and Hu et al.)

One-Hop Synchronization

Key step: determine clock offset

Observation: Can use timestamped message exchange to infer clock offset Similar to the NTP

algorithm Tricky

1T

2T 3T

4T

))()((5.0 3412 TTTToffset

Timing Components

Send Processing Time

Access Time

Propagation Latency

Transmission Latency

Receive Processing

Time

Non-Deterministic

Negligible

Deterministic

RBS (Elson et al.)

Inter-receiver synchronization Based on broadcast from sender Receivers exchange timestamps of

received messages Sender side non-determinism eliminated Receive processing costs Gaussian

Estimated by averaging Clock drift estimated by linear

regression

Reference Broadcast

Inter-receiver Synchronization

TPSN and LTS

Sender-side synchronization Use the NTP algorithm TPSN

Timestamps packets as close to radio as possible to remove non-determinism

LTS Uses RBS-style averaging

to remove non-determinism

1T

2T 3T

4T

))()((5.0 3412 TTTToffset

DMTS One-way, one-packet synchronization Receiver computes offset from sender’s clock

Do away with all sources of non-determinism by timestamping close to radio layer

Send Processing Time

Access Time

Propagation Latency

Transmission Latency

Receive Processing

Time

Non-Deterministic

Negligible

Deterministic

TimestampTimestamp

Multihop Synchronization

Goals Synchronize a pair of nodes

across multiple hops Synchronize all nodes with

a base-station Basic idea is the same in both

cases Successively synchronize

nodes along a path In the latter case, do it over

a spanning tree Error accumulates linearly

Reducing the Error

One-hop synchronization estimates offsets between nodes For global synchronization, need:

Minimum variance offset estimation Consistent maximum likelihood estimation of clock values

Theoretical result: There exists estimators that jointly determine minimum variance offsets that give maximum likelihood times Karp et al. and Hu et al.

Both based on the observation that one can use information along multiple paths

Error grows logarithmically



Operating Systems







Mon

itor

ing

Documents

Sensor Networks ACOE 422 Adopted from IEEE Tutorial on Sensor Networks