Wireless Sensor Networks · – Parking space finder • Each spot costs $1000s / year to maintain...

Wireless Sensor Networks: Wireless Sensor Networks: Seamless computing across the Seamless computing across the physical and PC worldsphysical and PC worlds

Feng Zhao, Jie Liu, Elaine Cheong, Prabal Dutta, Kamin Whitehouse

Networked Embedded Computing GroupMicrosoft Researchhttp://research.microsoft.com/nec

MSR faculty summit talk, August 2, 2004

A new class of computing platforms

SPOT watch

Microsoft SPOT stamp

Berkeley Spec mote

Berkeley WeC mote

Sensoria WINS NG 2.0iPAQ handheld

Hitachi mu-chip RFID

Gordon Bell’s Law: Technology advances enable a new, lower-priced, higher-volume computing platform or class to form every decade.

What’s happening in the garage

Multiple, concurrent queries:• Traffic engineering: where

can one find a parking spot?

• Security: what is going on down there?

• Corporate health services: when should the air exhaust fan run?

08:09 08:38 09:07 09:36 10:04 10:33 11:02 11:31 12:000

5

10

15

20

25

30Frequency of Vehicle Instances

Arrival statistics

8am 11am

Parking garage application• Application scenarios

– Parking space finder• Each spot costs $1000s / year to maintain

– Other apps include security and air quality monitoring• Sensors may be used to monitor vehicle traffic

– To improve space usage– But wiring is expensive, sensor may fail

• Re-taskable wireless sensor net testbed– Multi-sensing modality– Support cross-node coordination– Answer independent queries from concurrent users

Three application classes

Monitoring activity:• E.g., parking garage,

roadway traffic• Spatio-temporal pattern

Monitoring space:• E.g., habitat• Occupancy, condition

Monitoring objects:• E.g., asset tracking• Location, ID, property

Blurring the boundary between digital and physical worlds

Characteristics:• Heterogeneous devices• Disparate capabilities• Physically embedded

(energy, size, noise, real-time events, …)

• Dynamic topology• Large scale• Inherent uncertainties (in

systems and environment)• Concurrent user queries

Desired properties:• Easy to program, deploy,

and manage• Robust to failure• Responsive• Re-taskable• Scalable• Secure

Parking garage testbedSense• Speed, length, and

direction of vehicles• Magnetic signature of

vehicles• Image of vehicles

Break beam sensors

Micro-server

Camera

Magnetometer

PC server

Multiple, concurrent queries

Camera image

Magnetometer output

Breakbeam output

Report (histogram)

Micro Server Camera server

P2.5: the garage project

Count vehicles

Acquire magnetic signatures of extra

large vehiclesCapture image of vehicles moving in wrong direction

Programming Sensor Net:Finding the happy median

• View the net as a collection of data

• User interacts with it by sending queries

• Little control over where computation is done

Internet

Sensornet

Web servicesWeb

services Sockets/streamshttp/Web

??Signal processing view

Database view

• View it as a collection of programs

• Explicitly specify where computation is done

• May scale poorly

• View it as a collection of services

• Explicitly program services and provide run-time adaptation to changes and failures

• Be more resource aware and efficient

Towards A Service Model for Sensor NetSensor net as provider of services

– Services encapsulating data and computation– Service discovery, composition, execution– Tasking description

PlannerQuery Task graph Generator

TaskingMSTML

Service Composer

Service scheduleExecution

Run-time resource infoRun time

Schedule time

Service info

User

Report

Service Discovery/Self-monitoring

TaskingProgress

Example of services and their composition

Counting vehicles with a sensor array– Extract edges from break beam detections– Sort edges into consecutive detections– Detect vehicles based on timing relations

among detections– Count vehicles– Generate an arrival histogram report

Tasking descriptions in MSTML format

A multi-tier architecture to support the service model

Sensors

Micro servers

Front end/gateway to internet

InternetInternet

Desired properties:• Scalable• Re-taskable• Resource aware

and efficient• Easy to manage

Sample wireless sensor hardware

Telos MicaZ Ember Stargate

Picture

CUP/Memory 8MHz/12bit TI MSP430, 4K RAM, 512K flash

8MHz/8bit ATmega 128L, 4K RAM, 128K flash

8MHz/8bit ATmega 128L,

400MHz/32bit Intel PXA255, 32K flash, 64K SDRAM

Radio Chipcon cc2420, 802.15.4 (zigbee), 2.4GHz, 250kpbs, 120m range

Chipcon cc2420, 802.15.4 (zigbee), 2.4GHz, 250kpbs, 120m range

em2420, 802.15.4 (zigbee), 2.4GHz, 250kpbs, 120m range

802.11b, bridge to mica2 radio, 0.5-10Mbps

Sensors Temperature, humidity

Light, temp, magnetic, sound, vibration,

Add-on (e.g., temp, vibration)

Mica sensor boards

Power needs Tx 17.4mA, Rx 19.7mA, Sleep 2.4µA

Tx 17.4mA, Rx 19.7mA, Sleep

An example of micro-server:SPOT stamp + 802.15.4 radio

Development environment– High-level programming in C#/

Visual Studio .NET– Managed execution with TinyCLR– Hardware abstraction layer (HAL)

CC2420 HAL in C#

SPOT stamp– Derived from MS SPOT watch– ARM7TDMI processor, 27.6MHz– 384KB RAM, 4MB Flash– USB (1), Serial (2), SPI, I2C,

GPIO (24 shared lines)

802.15.4 radio– CC2420 from Chipcon– 2.4GHz, up to 250kbs– Distance up to 40-100m LoS– Connected to SPOT with SPI (4

lines) and GPIO (6 lines)


Sensors

Micro servers


InternetInternet



Scale Sensor Network

Result due to Gupta and Kumar (‘00):

As the density of the network grows, the per node throughput scaleswith )/1( nO )/1( nO

Uncontrolled flooding is not scalable with node density.


Centralized data collection is not scalable with the number of nodes.


A few well-placed, more powerful nodes can form an overlay network, coordinate smaller nodes, reduce communication latency

Centralized decision making is not scalable with physical phenomena.


Sensors

Micro servers


InternetInternet



Juggling Multiple Tasks

A blue vehicle initially spotted


Bottom camera relinquishes blue vehicle to top camera, and starts to track red vehicle

Cameras successfully track both vehicles• Without resource allocation and re-tasking, both might have looked at blue vehicle, leaving no camera to track red vehicle

A red vehicle enters the scene

Sensor tasking and control

• Unique to sensor net– Concurrent events, uncoordinated tasks, limited resources

• Tasking and re-tasking based on– A priori task assignment– Run-time conflict resolution– Load balancing– Cascaded tasks– …

• Need to be aware of the physical phenomena being monitored as well as system configuration

Collaborative processing in sensor networks

• What information to gather and communicate? And how often?

• Which nodes should participate in sensing, processing, or communication?

• How should the information be migrated?

• What is routing or querying in this context?

Moving to a utility-based approach:• What information is critical for the high-level tasks?• What is the cost of accessing the information?

Collaborative processing− Group formation in sensor networks

• Information needs and resource constraints define who should participate in the processing groups

• Group membership (e.g. location)defines the behavior of a node

Information-based group formation

A leader node (blue square) carries belief state• Choose sensors in the neighborhood with good information• Hand off current belief to chosen sensor (new leader) and update

Cascaded query processing“Find the intruder” (minimize energy usage)

Query enters at edge of network; Have minimal knowledge of

vehicle pos.

Redundantbearingsensors

Pos. estimate has improved; pick optimal sensors

Results good enough; return via

dir. diffusion

Actively seek out information• Pick best info source considering resource cost and information utility• Different from traditional query processing:

• Data may need to be collected to serve a query• Need to know where/how data is to be collected and processed

Examples of dynamic tradeoffs in QoS

( ) ( ) ( ) ( ) ( ) ( )1ˆ 1T TT T S SH x x x x x x x x xα α−= − ∑ − − − − −Utility function:

Information gainMahalanobis Distance

Energy costQuery path length

α=1: Information utility only α=0: Energy utility only

Recall in our service model• Generate task graphs and describe in MSTML• Instantiate task graphs• Coordinate among concurrent queries• Adapt to resource availability and resolve conflicts


TaskingMSTML

Service Composer



Schedule time

Service info

User

Report


TaskingProgress

Programming as service compositionServices

– As abstraction of computation and data– Loosely coupled, asynchronous– Composition through input/output ports– Communicate via message passing– Discovered dynamically– May span over multiple nodes

sensingservice

geo-constrained group

speed estimation

service

Programming as service composition

vehicle id service

Programming as service composition

Programming as service compositiontraffic-violation detection service

vehicle id service

speed estimation service

An Example• Task graph for counting vehicle query

– Ports– Services– Wiring services together

• Description in MSTML (micro-server tasking markup language)

Ports

Services

Connectingservicestogether

Recall in our service model• Generate task graphs and describe in MSTML• Instantiate task graphs• Coordinate among concurrent queries• Adapt to resource availability and resolve conflicts


TaskingMSTML

Service Composer



Schedule time

Service info

User

Report


TaskingProgress

Optimization in run-time service composition

• Syntactic analysis – Graph analysis to

remove duplications• Semantic analysis

– Analyze information content to determine reuse or sharing (e.g., sampling)

Vehicle counting services

Large vehicle detection services

Optimize out common components


How do the sensors coordinate and possibly migrate tasks?

Think of a sensor network as a distributed market

• Sensors bid for sensing tasks• Each sensing task has a utility• The goal is to optimally allocate

scarce resources to tasks– In a centralized setting, this

is a classical assignment problem studied in OR and economics

• Here, it may have to be done in a local, peer-to-peer manner!

An example of optimizing resource allocation

• Many concurrent moving objects

• Pan-and-tilt camera sensors are used for sensing

• The goal is to monitor as many “high-value”objects as possible

Joint work with Maurice Chu, et al., IEE IDSS04

Video playback of demo• As the vehicle drives

through the testbed, the infrared break beams can detect its speed, length and direction.

• If the vehicle is very large, a magnetic signature can be acquired. If the vehicle is speeding or moving in the wrong direction, an image can be acquired.

• Users can query the system independently, viewing output such as occurrences of vehicles.

Capture an image of a speeding vehicle

Blurring the boundary between the digital and physical worlds

What we are doing @ MSR:

• Connect sensor networks with PC ecosystems

– Make sensors visible to PCs and physical information available to people 24/7

– Bring services (e.g., web services) to small devices

• Develop platform and tools for networks of embedded devices

– Deal with uncertainties in both systems and environments

– Moving from “building unreliable systems from reliable parts” to “building reliable systems from unreliable parts”

Please visit our demo booth #31:Networked Embedded Computing Group, http://research.microsoft.com/nec

Wireless Sensor Networks: Seamless computing across the physical and PC worldsA new class of computing platformsWhat’s happening in the garageParking garage applicationThree application classesBlurring the boundary between digital and physical worldsParking garage testbedProgramming Sensor Net:Finding the happy medianTowards A Service Model for Sensor NetExample of services and their compositionA multi-tier architecture to support the service modelSample wireless sensor hardwareAn example of micro-server:SPOT stamp + 802.15.4 radioA multi-tier architecture to support the service modelScale Sensor NetworkScale Sensor NetworkA multi-tier architecture to support the service modelJuggling Multiple TasksJuggling Multiple TasksSensor tasking and controlCollaborative processing in sensor networksCollaborative processing - Group formation in sensor networksCascaded query processingExamples of dynamic tradeoffs in QoSRecall in our service modelProgramming as service compositionProgramming as service compositionProgramming as service compositionProgramming as service compositionAn ExampleRecall in our service modelOptimization in run-time service compositionJuggling Multiple TasksThink of a sensor network as a distributed marketAn example of optimizing resource allocationVideo playback of demoBlurring the boundary between the digital and physical worlds

Wireless Sensor Networks · – Parking space finder • Each spot costs $1000s / year to maintain...

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