emerging wireless networks - mcmaster university

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Emerging Wireless Networks

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OutlineScope

Wireless sensor networkLower Power Personal area networksWireless mesh networks

FocusTechnology overviewRepresentative projects/solutions

Embedded Networked SensingMicro-sensors, on-board processing, and wireless interfaces all feasible at very small scale

can monitor phenomena “up close”

Will enable spatially and temporally denseenvironmental monitoringEmbedded Networked Sensing reveal previously unobservable phenomena

Seismic Structure response

Interaction between ground motion and structure & foundation response

Contaminant Transport

Understand contaminant transport and fate in real systemsEmergency Medical

Care Ecosystems

Adopted from D. Estrin’s Mobicom’02 tutorial

Develop in situ observation of species and ecosystem dynamics

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Enabling Technologies

Embedded Networked

Sensing

Control system w/Small form factorUntethered nodes

ExploitcollaborativeSensing, action

Tightly coupled to physical world



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RFIDRadio frequency identifierActive

Battery powered Read-write and read only versions available Longer read ranges (25 to 100 feet) Higher tag costs ($20 to $70 per tag) 2D location systems possible Example: toll booths

Passive Powered by reader Read-write and read only versions available Shorter read ranges (Inches to 20 feet) Lower tag costs (at least $1 per tag) Item ID Example: item management

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SensorPassive elements: seismic, acoustic, infrared, strain, barometer, humidity, temperature, etc.Passive Arrays: imagers (visible, IR), biochemicalActive sensors: radar, sonar

High energy, in contrast to passive elements

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Networked Sensor Node

Rice GnomeTIMSP430F149 16bit

(7.3728 MHz) 60kB

flash memory and

2kB RAM

900MHz radio, GPS

extension module

MANTIS (U. of Colorado)CC1000 multi-channelradio

ATMEGA 128microcontroller

Intel MoteStrong ARM core, SRAM, FLASH, BT radio

Xbow MoteMICAz 128KB ATMega128Lprogram flash memory512K data memory2.4GHz 802.15.4 Zigbee radio (250 kbps)H/W AES-128

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Sensor Node Energy Roadmap

20002000 20022002 20042004

10,00010,000

1,0001,000

100100

1010

11

.1.1

Ave

rage

Pow

er (m

W)

• Deployed (5W)

• PAC/C Baseline (.5W)

• (50 mW)

(1mW)

Source: ISI & DARPA PAC/C Program

Sensor Handheld Mobile Desktop Server

100W10W1W100mW10mW1mW100uW

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Networked Sensor SoftwareTinyOS: a sensor operating system

nesC: even-driven programming modelA graph of components

Maté: virtual machine that allows online reprogrammingTOSSIM: simulator for TinyOS networks

TinyVizTythonPowerTOSSIM

http://www.tinyos.net/

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TinyOS Overview

CommunicationActuating Sensing Communication

Application

Main (scheduler)

Hardware Abstractions

Concurrency: event-driven architectureModularity:

Scheduler + a graph of components compiled into executables

EfficiencyEvent/command -- function callsFew context switches

FIFO scheduler, non-preemptive

No kernel/application boundary

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TinyOS Component Model

Component has:Frame (storage)Tasks (computation)Command and Event Interface

Messaging Component

Internal StateInternal Tasks

Commands Events

Image courtesy Jason Hill et al

Each component declares the commands it uses and the events it signals.Statically allocated, fixed sized framesTasks run to completion but can be preempted by eventsNon-blocking requests to lower level components

Cannot signal events

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TinyOS Component Model (cont’d)Events

Event handlers deal with hardware events (interrupts) directly or indirectlyDeposit information into a framePost tasksSignal higher level eventsCall lower level commands

The scheduler puts the processor to sleep when the task queue is empty.Peripherals keep operating and can wake up the processor.

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An Example of TOS Component

Messaging Component

AM_SUB_INIT

AM_SUB_POWER

AM_SUB_TX_PACKET

AM_TX_PACKET

_DONE

AM_RX_PACKET

_DONE

Internal State

AM_INIT

AM_POWER

AM_SEND_MSG

AM_MSG_REC

AM_MSG_SEND_DONE

Internal Tasks

Commands Events

//AM.comp//

TOS_MODULE AM;

ACCEPTS{

char AM_SEND_MSG(char addr, char type,char* data);

void AM_POWER(char mode);

char AM_INIT();

};

SIGNALS{

char AM_MSG_REC(char type, char* data);

char AM_MSG_SEND_DONE(char success);

};

HANDLES{

char AM_TX_PACKET_DONE(char success);

char AM_RX_PACKET_DONE(char* packet);

};

USES{

char AM_SUB_TX_PACKET(char* data);

void AM_SUB_POWER(char mode);

char AM_SUB_INIT();

};

Slide courtesy Jason Hill et al

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Internal Component Graph

Slide courtesy Jason Hill et al

RFM

Radio byte

Radio Packet

UART

Serial Packet

I2C

Temp

Photo

Active Messages

Clocksbit

byte

packet

Ad hoc Routing Applicationapplication

HW

SW

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The Communications Stack

Calculates CRC.

Retransmit dropped packetsusing Acknowledgement

Sends and receives data in bytesand notifies data arrival

Setting the parameters forCC1000 radio chip

Packet decomposition and reassembly

*:newly made or modified fromexisting network stack

ReliableCommGenericCommAMStandard

RadioCRCPacketRFComm

ChipconSpiByteFifoC

SecDedEncoding

ChannelMonC

Application

Radio

***

*

*

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RFM

Radio byte

Radio Packet

UART

Serial Packet

I2C

Temp

Photo

Active Messages

Clocksbit

byte

packet

Ad hoc Routing Applicationapplication

Simulation

SW

TOSSIMCompile TinyOS components into native binary

Can experiment directly new application componentsFast and can scale to thousands of nodes

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TinyViz

TinyViz GUI

NesCNesC

events

commands

TinyViz TOSSIM

MoteApplication

Program

Radio Model

ExternalComm

Event BusPlugins

SimComm

Event Queue

A visualization tool

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PowerTOSSIMSimulates the energy consumption of each node in a sensor network

Benchmarking power consumption of different modulesOutput power state transition during simulationPosterior analysis of CPU cycles

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CPU Cycle AnalysisTOSSIM compiles application code into a native PC binaryTherefore, difficult to determine how many CPU cycles used on the moteCould simulate at the AVR instruction level

Very slow

Instead, record runtime basic block execution counts, map basic blocks to cycles used on AVR.

This has low overhead

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Cycle Count Estimation

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Cycle Count Estimation (2)

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Cycle Count Estimation

Potential inaccuracies:Need accurate mapping from C basic blocks to binarySome low level components have no mapping

In many cases, active CPU cycles very smallNeglegible effect on total power

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Sample Sensor ProjectsCaveats: the following slides are mostly taken from the corresponding authors’ presentation

Acoustic localization (UIUC)CotsBots (Berkeley)Great Duck Island (Berkeley)CodeBlue (Harvard)

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Localizing Acoustic EventsIntrusion detectionTracking of objects

Sink/Pursuer

Sensor

Router

Sink/Pursuer

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Lightning Protocol

Proximity-based localizationFind the “closest” sensor“loudest” is not necessarily “closest”!

Based on the observation that electronic-magnetic wave travels faster than acoustic wave (c = 3e8m/s, v = 340m/s)Implementation

MICA mote

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Open-source hardware and software platform for distributed roboticsSmall, cheap, off-the-shelf, and modular robots

Goals for CotsBots

Emphasize application design software for large (> 50) robot networksReduce startup costs to demonstrate distributed multi-robot algorithms

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Building an Application: Beep Diffusion

Simple algorithm to spread robots apartAdjusting microphone gain adjusts distance robots move apart

Time Slot Manager

Send Msg/Beep

Listen

DriveObstacle

My Slot

Heard Msg

Heard Beep

Hit Obstacle

Done Driving

Done Driving

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Building an Application: Beep Diffusion

Implementation

5 robots in 10ft x 10ft area3.5 second slot time

Difficulties with buzzer/tone detector hardware lead to inconsistent beep radius

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Habitat Monitoring on Great Duck IslandQuestions

What environmental factors make for a good nest? How much can they vary?What are the occupancy patterns during incubation?What environmental changes occurs in the burrows and their vicinity during the breeding season?

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Application architecture

Base-Remote Link

Data Service

Internet

Client Data Browsingand Processing

Transit Network

Basestation

Gateway

Sensor Patch

Patch NetworkSensor Node

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GDI 2002 deployment

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http://www.greatduckisland.net/index.php

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CodeBlueProvide routing, naming, discovery, and security for wireless medical sensors, PDAs, PCs used to monitor and treat patients in a range of medical settings.

Integration of medical sensors with low-power wireless networks Wireless ad-hoc routing protocols for critical care; security, robustness, prioritization Hardware architectures for ultra-low-power sensing, computation, and communication Interoperation with hospital information systems; privacy and reliability issues

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What we have coveredKey difference between wireless and wireline communication

Wireless link quality is highly variableNo physical boundary in wireless connectivityAllows mobility

Some quantitative results in propagation model and modulation

Some quantitative results in propagation model and modulation

How does these characteristics affect the network protocol stack? Or how should the network protocol be (re-)designed differently

•MAC protocols, IEEE 802.11•Routing, Mobile IP•Modification to TCP•Security•Power

•MAC protocols, IEEE 802.11•Routing, Mobile IP•Modification to TCP•Security•Power

Enabling new neworkarchitecture, new services, new applications

Multi-hop wireless networks sensornet, wireless mesh networks etc.• Routing protocol• Mobility model• Location aware applications & mobile services

Multi-hop wireless networks sensornet, wireless mesh networks etc.• Routing protocol• Mobility model• Location aware applications & mobile services

Educational goals:• Familiar with network simulation tool, tcl, C++ programming• Get some hand-on experience• Learn how to do “experiments”, analyze results, and draw observations• Learn how to present technical ideas in writing & verbally• And think critically!

Educational goals:• Familiar with network simulation tool, tcl, C++ programming• Get some hand-on experience• Learn how to do “experiments”, analyze results, and draw observations• Learn how to present technical ideas in writing & verbally• And think critically!

emerging wireless networks - mcmaster university

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