Smart Wireless Sensor Network Systemsgupta/teaching/cs603/wsnSp04/lec1 010804 … · 04/08/2001...

Smart Wireless Sensor Network Systems

Ajay GuptaDepartment of Computer Science

Western Michigan UniversityKalamazoo, MI 49008

[email protected]://www.cs.wmich.edu/wsn/

Copyright © 2003

Research Supported in part by DOE: FIE-R215K020362 and WMUWSN intro and projects listing, partially taken from FIE

Symp talk on 061903 by Ajay Gupta, WMU-CS, September 04 2003 1

Outline

– Introduction

– Motivating Applications

– Enabling technologies

– Unique constraints

– R & D projects undertaken at WMU during last 12 months

– Research Challenges and Issues

WSN intro and projects listing, partially taken from FIE Symp talk on 061903 by Ajay Gupta, WMU-CS, September

04 2003 2

Smart Sensors

What are they?

Micro-sensors +on board processing + low-power wireless interfaces

All feasible at very small scale• Berkeley Motes• MIT µAMPs• Sensoria WINS• Hidra


04 2003 3

Sensor Node H/W-S/W Platforms

sensors CPU radio

battery

Acoustic, seismic, image, magnetic, etc.

interface

Electro-magnetic interface

Event detectionWireless communication with neighboring nodes

In-node processing

Limited battery supply

Energy efficiency is the crucial h/w and s/w design criterion


04 2003 4

A Smart Sensor Network System

Command Center

Satellite

Base Station

Supervisor Sensor

Smart Sensors


04 2003 5

Embedded Networked Sensing Potential (UCLA/UCB)

• Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale

– can monitor phenomena “up close”

• Will enable spatially and temporally denseenvironmental monitoring

• Embedded Networked Sensing will reveal previously unobservable phenomena

Seismic Structure response

Contaminant Transport

Marine Microorganisms

Ecosystems, Biocomplexity


04 2003 6

Enabling Technologies: Some Networked Sensor Node Developments

LWIM III

UCLA, 1996

Geophone, RFM

radio, PIC, star

network

AWAIRS I

UCLA/RSC 1998

Geophone, DS/SS

Radio, strongARM,

Multi-hop networks

Processor

WINS NG 2.0Sensoria, 2001Node developmentplatform; multi-sensor, dual radio,Linux on SH4,

Preprocessor, GPS

UCB Mote, 20004 Mhz, 4K Ram

512K EEProm,128K code, CSMAhalf-duplex RFM radio


04 2003 7

“The network is the sensor” (Oakridge National Labs)

Requires robust distributed systems of thousands of

physically-embedded, unattended, and often untethered, devices.


04 2003 8

New Design Themes• Long-lived systems that can be untethered and unattended

– Low-duty cycle operation with bounded latency– Exploit redundancy and heterogeneous tiered systems

• Leverage data processing inside the network– Thousands or millions of operations per second can be done

using energy of sending a bit over 10 or 100 meters (Pottie00)– Exploit computation near data to reduce communication

• Self configuring systems that can be deployed ad hoc– Un-modeled physical world dynamics makes systems appear ad hoc– Measure and adapt to unpredictable environment– Exploit spatial diversity and density of sensor/actuator nodes

• Achieve desired global behavior with adaptive localized algorithms– Can’t afford to extract dynamic state information needed for

centralized controlWSN intro and projects listing, partially taken from FIE


From Embedded Sensing to Embedded Control

• Embedded in unattended “control systems”– Different from traditional Internet, PDA, Mobility applications – More than control of the sensor network itself

• Critical applications extend beyond sensing to control and actuation– Transportation, Precision Agriculture, Medical monitoring and drug

delivery, Battlefield applications, Military/Defense Applications

– Concerns extend beyond traditional networked systems• Usability, Reliability, Safety, Security

• Need systems architecture to manage interactions– Current system development: one-off, incrementally tuned, stove-

piped– Serious repercussions for piecemeal uncoordinated design:

insufficient longevity, interoperability, safety, robustness, scalability...


04 2003 10

Smart Sensor Network System Building Blocks

SensorsSensors

Network SelfNetwork Self--OrganizationOrganization

Programming Programming modelsmodels

Database Database policiespolicies

and architectureand architecture

Connection toConnection toinfrastructureinfrastructure

Cooperative Cooperative DetectionDetection

CommunicationCommunicationLinksLinks

TheoreticalTheoreticalframeworkframework

Node Node LocalizationLocalization

Mobility andMobility andnavigationnavigation Target IdentificationTarget Identification

AlgorithmsAlgorithms

System System EnergyEnergy

ManagementManagement

ActuationActuation

HumanHumaninterfaceinterface

Modeling ofModeling ofEnvironmentEnvironment

CalibrationCalibrationWSN intro and projects listing, partially taken from FIE


WMU R & D Projects(January 2003 – present)

1. Design of Security Protocols : DSPS2. Location Tracker using Motes3. Directed Diffusion: Attacks & Countermeasures 4. Remote Home Surveillance5. Improving the Accuracy of Measurements

taken by Motes using Neural Networks6. Smart Occupancy Monitoring System using

Motes7. Comparative Study of Network Simulators 8. Collective Image Processing9. DENSe: a Development Environment for

Networked Sensors


04 2003 12

WMU R & D Projects(January 2003 – present)

10.Incorporating mobile-ware in distributed computations / computational-grid

11.Extend ns-2 to allow satellite and WCN simulation

12.Smart antennas for WCNs13.Energy efficient MAC protocols for IEEE

802.11x14.A Wireless Security Testing System15.Mobile and Self-calibrating Irrigation System16.Collective communications for sensornets


04 2003 13

DSPS: A Distributed Security Protocol for SensornetsVijay Bhuse, Rishi Pidva, Ajay Gupta

GoalsDesign a (tiny) security and intrusion detection protocol for WSNs

Which is– Scalable,– Distributed– Secures communication infrastructure

Which satisfies– Data Confidentiality– Data Authentication– Data Integrity– Data Freshness


04 2003 14

Existing Proposals for WSN Security Protocols

SPINS (Perrig, Szewczyk, Wen, Culler, and Tygar, 2002)• Centralized approach• Clocks Synchronized• Powerful base-station (can’t be compromised)• Inefficient for node-node communications

TinySec at UCB (Karlof, Sastry, Shankar, Wagner 2003)• Secure link layer communication


04 2003 15

DSPS Building Blocks• Key Generation

– A common key, K, shared among nodes in a cluster– A “moving” key server generates K– Every node has a common secret password P– K is changed periodically

• Key Distribution– Use extension of Diffie-Hellman algorithm with encrypted

key exchange– Exchange keys are different than K; every pair has a key

• Signatures– Provide secure channel of communication – Authentication, Integrity and Freshness

• RC5, DES or TEA– can interchangeably be used as encryption algorithms


04 2003 16

DSPS: Key Distribution

K= gx % n x, y : random numbers P’ = F ( P, counter )

Node A Node B


04 2003 17

Location Tracker using MotesJunaith Ahmed, Mark Terwilliger, Ajay Gupta

Goals• Locate mobile nodes (laptops,

cameras, projectors, keys, remote controllers, kids☺…)

• Develop expertise in building applications for motes

• Test capabilities of motes

• Build expertise in tinyOS and NesC


04 2003 18

Location Tracker: How it works

• When a mobile node needs to be located, it begins a series of transmissions

• It begins by transmitting with the highest potentiometer value, which is 99

• If a fixed station responds, we know that it is within 2 feet of the mobile node

• Every 3 seconds, the mobile node adjusts its potentiometer setting in order to broadcast to a larger range

Potentiometer Reading

99 95 90 85 80 75 70 60 50

Range (feet) 2 5 8 10 12 15 18 24 30


04 2003 19

Location Tracker: How it works• Once the mobile node has received responses from at least 3 fixed

stations, it will stop broadcasting• It sends the IDs and the corresponding potentiometer readings to the

base station• Finally, the base station uses the collected information to calculate the

exact location of the mobile node using the triangulation method


04 2003 20

Directed Diffusion: Attacks and CountermeasuresY. Lu, J. Shao, X. Zhong, Y. Zhu, W. Shen

Goals• Implement the directed diffusion protocol on TinyOS by

writing two separate applications (sink and source)

• Present the detailed security analysis of directed diffusion protocol and energy conserving topology maintenance algorithms

• Describe practical attacks against directed diffusion protocol that would defeat any reasonable security goals

• Discuss countermeasures and design considerations for directed diffusion protocols in sensor networks


04 2003 21

Remote Home Surveillance using MotesV. Bhuse, V. Kinra, R. Pidva, N. Shahreyar, A. Gupta

GoalsGoals

•• Implement scalable and energyImplement scalable and energy--efficient routing protocolefficient routing protocol

•• Design and Implement a remote Design and Implement a remote access utility to perform onaccess utility to perform on--demand demand query on temperature, light etc. at query on temperature, light etc. at your home from remote locationyour home from remote location


04 2003 22

RHS: Architectural Design

RHS Communication based onbased onClient / BaseClient / Base--station station CommunicationCommunication

BaseBase--station / Sensor Network station / Sensor Network CommunicationCommunication

RHS ComponentsJavaJava--enabled Cell Phone / PDAenabled Cell Phone / PDA

Personal ComputerPersonal Computer

Apache Tomcat WebServerApache Tomcat WebServer

Tiny DBTiny DB

Sensor MotesSensor Motes

Internet AccessInternet Access

Room # 4 Room# 3

Room # 2 Room#1


04 2003 23

RHS

Achievements1. Successfully implemented a secure, power efficient

communication between client and Base Station and Vice Versa with client as cell phone as well as PC.

2. Power efficient routing of packets.3. Various aspects of security for radio & wireless

communication4. Making the radio sleep when not needed

Future Work1. Add reliability and security to the application. 2. Extend our project to control parameters in home

environment.


04 2003 24

Improving the Accuracy of the Measurements taken by Motes Using Artificial Neural Networks

Mohammad Ali Salahuddin, Ajay Gupta

• Since the intentions of smart sensor networks are to establish a real-time system, it is crucial to have the sensor devices tightly synchronized

• Apart from time synchronization, networks of motes also have to be adjusted to varying ‘sensor drifts’

• The aim of this research project is to introduce the largely ignored concept of sensor drifts in motes

• We intend to explore how to predict a sensor drift using Artificial Neural Networks and how to accommodate for these drifts


04 2003 25

Smart Occupancy Monitoring System Using MotesS. Desaraju, K. Jayaraman, G. Suzuki, V. Vaidyanathan, A. Gupta

Goal• Determine the occupied and unoccupied rooms in a

building using the motes as the primary sensing devices

Design• Remote motes in the target room communicate with

the intermediate motes that in turn communicate with the base station

• These communication and networking capabilities are achieved using TinyDB, a query processing system for extracting information from a network of sensors


04 2003 26

Base Station

Intermediate Mote

Target Room

Other Features of the SOMS

• Abnormalities - Allows the user to monitor individual parameters for any abnormalities like lights not turned off in an unoccupied room

• Fire Alarm – Alarming the user if the temperature in a room goes beyond a preset threshold value

• Power Monitoring - Allows the user to monitor the power remaining in a mote

Mote

Smart Occupancy Monitoring


04 2003 27

A Comparative Study of the Network Simulators:NIDO, REAL 5.0 and NS-2

Z. Kamal, N. Sait, P. Unkule, T. Piatkowski

Goals• Identify a simulator

–– Wireless sensor Wireless sensor networksnetworks

–– Thousands of nodesThousands of nodes–– Energy consumptionEnergy consumption–– AdAd--hochoc–– FaultFault--tolerancetolerance

• Identify extensions

NIDO (UCB), REAL (Cornell), NS-2 (USC/ISI, Xerox, LBNL)

24 st.hrs24 st.hrsYes

NoNoSimulate energy consumption

24 st. hrs24 st. hrsYes

NoNoSimulate individual clocks

96 st. hrsYesYes

NoSimulate network flow, routing and control protocols.

96 st. hrsYes

NoYesSupports Mobile Nodes

72 st. hrs96 st. hrs

No

Yes,Maximum

40 node

s

Yes,Maximum

1000 nodes

Supports simulations of large number of sensor MOTEs

8 st. hrs8 st. hrs

NoNoYesSimulate a MOTE

NS-2REAL 5.0NIDOCost of simulating feature and

output


04 2003 28

Collective Image Processing Matt Johnson and Ajay Gupta

Goals• Deploy a randomly

distributed array of “low” power image sensors (avoid high power camera)

• Generate a composite image from the ad-hoc network of image sensors

• Use in-network processing for composite image

red image nodes collect images

green node queries the image nodes and transmits the composite image


04 2003 29

Collective Image Processing

Image boundaries for an ad-hoc distribution of a network of seven image sensors focused on a particular geographic region.

Network simulation output of four sensor nodes at random positions and angles using the averaging method to manage image overlap.

Network simulation output of four sensor nodes at random positions and angles using the overlap method to manage image overlap.


04 2003 30

DENSe – A Development Environment for Networked Sensors

Ajay Gupta and Wuwei Shen

Goals• Advance the state-of-the-art in WSN security

• Novel use of UML/Rational Rose to design protocols and software systems

• Aid in rapid development of WSN applications

• Develop an integrated all-encompassing framework, DENSe


04 2003 31

Protocol Model Checker in DENSe

Protocol Model

Class Diagram

State Chart Diagram

Sequence Diagram

Class Diagram

Translator

State Chart Diagram

Translator

Sequence Diagram

Translator

ASML Model

ASML Model Checker

OCL Library


04 2003 32

Flow of DENSe

UtilityModules for timeSync,

data management, location etc

Setup Wizard

Compiler

Pre-Compiler

Application Code

SecurityModules for

authentication, key distribution etc

CommunicationModules for networking

protocols in different layers

Execution / Simulation

Application

Library


04 2003 33

Representative Research Challenges

• Embedded Networked Systems– Support for mobile code / agents

– OS services in support of sensor I/O– Low latency feedback across hw/sw boundaries

– Performance and configuration tuning at runtime

• Sensor Information Technology– Large scale distributed micro sensor networking– Fixed and mobile internetworking

– Collaborative signal processing– Security - nano-cryptography


04 2003 34

Research Issues and Future Work

• Naming• Wireless networking

protocols• Address free routing• Data management• Dependability, reliability…

Continue to extend the nine WSN R&D projectsContinue to extend the nine WSN R&D projects

• Real-time security• Middleware services• Scalability• Adaptability• Load balancing, load

sharing…


04 2003 35

Sensor Network ComputingSensor Network Computing is the is the

“challenge of the century”“challenge of the century”!!!!


04 2003 36

Sample Layered Network Architecture

Resource constraints call for more tightly integrated layers

Open Question:

Can we define anInternet-like architecture for such application-specific systems??

In-network: Application processing, Data aggregation, Query processing

Adaptive topology, Geo-Routing

MAC, Time, Location

Physical comm., sensing, actuation, SP

User Queries, External Database

Data dissemination, storage, caching

Why fuzzy boundary?


04 2003 37

MIT’s “Expressive Footware”

Dance shoes with wireless link & a suite of sensorsmeasure dynamic parameters at a dancer's foot

differential pressure at 3 points and bend in the sole, 2-axis tilt, 3-axis shock, height off the stage, orientation, angular rate and translational position)

example use: generate accompanying music


04 2003 38

Smart Kindergarten Project (UCLA): Sensor-based Wireless Networks of Toys

for Smart Developmental Problem-solving Environments(Srivastava et al)

SensorsModules

High-speed Wireless LAN (WLAN)WLAN-Piconet

Bridge

Piconet

WLAN-PiconetBridge

WLAN AccessPoint

Piconet

SensorManagement

SensorFusion

SpeechRecognizer

Database& Data Miner

Middleware Framework

Wired Network

NetworkManagement

Networked Toys

Sensor Badge


04 2003 39

Enabling Technologies

Embedded Networked

Sensing

Control system w/Small form factorUntethered nodes

ExploitcollaborativeSensing, action

Tightly coupled to physical world

Embed numerous distributed devices to monitor and interact with physical world

Network devices to coordinate and perform higher-level tasks

Exploit spatially and temporally dense sensing and actuation


04 2003 40

Some Networked Sensor NodeDevelopments

LWIM III

UCLA, 1996

Geophone, RFM

radio, PIC, star

network

AWAIRS I

UCLA/RSC 1998

Geophone, DS/SS

Radio, strongARM,

Multi-hop networks

Processor

WINS NG 2.0Sensoria, 2001Node developmentplatform; multi-sensor, dual radio,Linux on SH4,

Preprocessor, GPS

UCB Mote, 20004 Mhz, 4K Ram

512K EEProm,128K code, CSMAhalf-duplex RFM radio


04 2003 41

Sensors

• Passive elements: seismic, acoustic, infrared, strain, salinity, humidity, temperature, etc.

• 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 and MOTES adequate in many of these domains; work remains to be done in biochemical


04 2003 42

Choose The Right Digital Architecture …

MAC

Unit

Addr

Gen

mP

Prog Mem

Embedded Processor

(lpArm)

Direct MappedHardware

EmbeddedFPGA

DSP(e.g. TI 320CXX )F

lexi

bilit

y

Power Dissipation

ReconfigurableProcessors

(Maia)Factor of 100-1000

100-1000 MOPS/mW

10-100MOPS/mW

.5-5MIPS/mW


04 2003 43

Communication/Computation Technology Projection

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


04 2003 44

Energy to Play a Major Role

1

10

100

1000

10000

100000

1000000

10000000

1980

1984

1988

1992

1996

2000

2004

2008

2012

2016

2020

Algorithmic Complexity

(Shannon’s Law)

Processor Performance (~Moore’s Law)

Battery Capacity

Source: Data compiled from multiple sources (mainly UCB)

1G

2G

3G


04 2003 45

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, ENS mightWith aggressive energy management, ENS mightlive off the environment.live off the environment.

Source: UC Berkeley

Smart Wireless Sensor Network Systemsgupta/teaching/cs603/wsnSp04/lec1 010804 … · 04/08/2001...

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