Wireless Sensor Networks

Summary

Professor Jack StankovicDepartment of Computer

ScienceUniversity of Virginia

OutlineOutline

• WSN – its niche• Applications revisited• Fundamentals – early in research• Some Intriguing Concepts in this

field• Future Research Areas

WSN – Its Niche WSN – Its Niche • Distributed Computing

– Load balancing, group management, distributed OS, middleware, network protocols, …

• Sensor Networks (wired or powerful wireless)– Submarines, automated factories, fleets of ships,

…– Real-time systems– DSP

• Radio Communications (Wireless)– Radio signals

Combine all three with significant constraints

WSN – Its NicheWSN – Its Niche

• Mobile Ad Hoc Networks (MANET)– Laptops– Single hop?

• Distributed Embedded Systems of Appliances– Ubicomp– Products with embedded

sensors/computing (toasters, refrigerators, air conditioning, etc.)

– RFIDs

WSN – Its NicheWSN – Its Niche

• Dust to grids– Dust, motes, heterogeneous sensor

nets, Internet, the grid

• Cell phones (increasing capabilities)– Connect to WSNs (act as base stations)– Connect to products with embedded

computing– Connect to Internet– Connect to Grid– Oh yeah –> also make phone calls

1 Billion Node WSN1 Billion Node WSN

• Future World:

– Every Cell Phone has pollution sensor and reports readings periodically

– Add other sensors

– Universal device?

How the Problems Change

How the Problems Change• Environment

– connect to physical environment (large numbers, dense, real-time)

– faulty, highly dynamic, non-deterministic– wireless – contention, irregular patterns– power management critical

• Network– structure is dynamically changing– sporadic connectivity– new resources entering/leaving– large amounts of redundancy– self-configure/re-configure– individual nodes are unimportant - route/query to AREA

How the Problems Change

How the Problems Change

• OS/Middleware– manage aggregate performance

• Control the system to achieve required emerging behavior• How do we know it works?

– self-organizing (self-*)– team formation with fuzzy membership– manage power/mobility/real-time/security tradeoffs– geographical/location based (spatial)– real-time/real world (temporal)– data centric– support new (language) paradigms

ImplicationsImplications

• Fundamental Assumptions underlying distributed systems technology has changed– wired => wireless (limited range, high error

rates)– unlimited power => minimize power– Non-real-time => real-time– fixed set of resources => resources being

added/deleted– each node important => aggregate

performance

• New solutions necessary

Applications Applications

• Passive sensing of environment/data collection

• Same as above with actuators• Active tracking/target discrimination• Degrees of mobility• Interface with the Internet• Handheld PDAs/laptops (seemless

integration)• Heterogeneity• Placed versus ad hoc deploymentAny killer apps? Any wild new apps?

Function(Cost)Function(Cost)

• 200 nodes at $100 ea. -> $20,000• 20,000 nodes at $1 ea. -> $20,000

• 20,000 nodes at .10 ea. -> $2,000

One Architecture One Architecture

• Sensors• Actuators• CPUs/Memory• Omni-dir. Radio

Second Architecture Second Architecture

• Fixed Deployment (grid, mesh, …)

TaxonomyTaxonomy

HWCapabilities

ApplicationRequirements

Software/Middleware

FundamentalsFundamentals

• What is truly fundamental about WSN?– Power limitations?

• Solar cells/close down for a time to recharge/plug into wall socket, etc.

• Probably a major problem for a long time and for many applications

– Cpu/memory capacity?• New platforms are being built

– Large Scale?• Not necessary for all systems

– Long Lifetimes?

FundamentalsFundamentals

• Interact with the environment – sensing– Consider all the realities of sensing …– Sensor fusion/data aggregation– False Alarm Processing

• Multi-hop wireless radio communication– Consider all the realities of radio comm.– Asymmetry, lost messages, nodes

move, nodes sleep or die, etc.

• Ratio of communication/sensing ranges

Radio Model in Evaluation

Radio Model in Evaluation

Radio ModelDOI = Degree of Irregularity

DOI = 0.05 DOI = 0.2

CommunicationCommunication

• A funny thing happened on the way to the destination– Lost packet– Congestion (long delay)– Lost node– Eavesdropped on– Corrupted– Changed on purpose– Cycle– Interfered with by other radio

transmissions

Sensing versus CommunicationSensing versus Communication

• Sensing/communication range ratio• Sensing/communication/power

tradeoffs

Sensing Range

CommunicationRange

What if the opposite?Required degreeof coverage?

FundamentalsFundamentals

• Self-configure, self-manage, self-heal• Self-awareness

– Space (location/geography), time, energy, dynamics, security, reliability

• Self-calibrate• Self-*• Unattended operation (completely or

almost completely) -> difficult physical accessibility

Self-stabilizing algorithms

• Localization: A mechanism for discovering spatial relationships among objects

Fundamentals

LocalizationLocalization

• Node• Target• Discovery Service

• Robust, secure, Fn(many parameters)

FundamentalsFundamentals

• Aggregate Behavior – biological metaphors

• Simple decentralized algorithms (localized behavior)– Epidemic/virus type algorithms– Randomized algorithms– Develop local rules that yield desired

macroscopic behavior

• Lazy behavior (fast dynamics)

Epidemic AlgorithmsEpidemic Algorithms

• Final state– Backward links

• The flood extends towards the source

– Stragglers• MAC-level collisions

– High clustering• Most nodes have few

descendants• A significant few have

many children

Fundamentals - ScaleFundamentals - Scale

• 20 ---- 200 ---- 2000 ---- 20,000

• Flooding• Acknowledgements• Information into and out of system• State of system• Management/Maintenance

FundamentalsFundamentals

• Uncertainty– Packets delivered– Irregular communication range– Faults– False alarms (and sensor processing)– Changing environment– Changing topology– Resources entering/leaving– Power degrading

Fundamentals - EventsFundamentals - Events

• Size of targets/events (point/area)• Discrete versus continuous• Probabilistic

Fire

X

Explosion

FundamentalsFundamentals

• Group Management and Consensus

Example: ConsensusExample: Consensus

• Classical consensus: all correct processes agree on one value– No power constraints– No real-time constraints– Does not scale well to dense networks– Approximate agreement (some work

here) - on sets of values (physical quantities)

• New Solutions ?

New Concept of Consensus

New Concept of Consensus

• Termination: every correct processor eventually decides some value

• Uniform Agreement: no two processors decide differently

• Group Membership: join/leave - everyone knows who is in the group

• Termination: “at least n” correct processors decide some value by time t

• Group Agreement: at least n processors decide the same value within epsilon

• Area/Function Membership: join/leave an area or by function

Classical New Definitions

Examples: Tracking and

Map Regions

Examples: Tracking and

Map Regions

Base Station

What’s HardWhat’s Hard

• Multiple targets• Crossing targets• False Alarms

– Depends on (changing) environment, sensors, confidence tradeoffs, noise, lost messages, …)

• Speed of targets• Uniqueness of targets• Classify targets• Proper abstractions• Save power/minimize communication

Fundamentals - Security

Fundamentals - Security• What is the single most important issue that

could prevent WSNs from wide scale deployment? – Security– 2nd issue -> Privacy

• At application level– Authenticity and integrity

• Security of each service (examples)– Routing:

• non-secure if a single node is captured!• Eavesdrop or change message• Flood

• Insidious unintended consequences of collecting data– Monitor oceans for fish migration (data mine location of

submarine fleet)

Fundamentals - Analysis

Fundamentals - Analysis

• Control Theory• Markov Processes• Real-time Schedulability Analysis• Optimization Theory• Graph Theory (Random Graphs?)• Information Theory• Phase Transitions• Guarantee Quality of Service• Diffusion Theory?

Intriguing ConceptsIntriguing Concepts

• Space (geography/location)– GF

• Time (deadlines/periods/event lifetime/power lifetime)– SPEED, clock sync, power management

• Aggregate Behavior (emerges versus controls)

Velocity (Spatio-Temporal)

Velocity (Spatio-Temporal)

E2E Di stance

j

FS

iD

Actual Speed

Speed todestination(Set Point )

E2E Delay is bound by E2E Distance/Speed SetPoint

USE VELOCITY

Bound ErrorsBound Errors • End-to-end• Real-time• Collisions• Congestion

Destination

Source

ErrorPropagates

Race Ahead

BehaviorBehavior

• Flooding – stragglers• Epidemic algorithms and phase

transitions• Global routing behavior – more

emerged than controlled

Feedback Control (FC)Feedback Control (FC)

23

5

9

10

7

DelayBoo

411

6

13

12Packet 1

Packet 1

Beacon

Packet 2

Packet 2

Packet 2

Packet 2

Packet 2

• SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks.

Use FC – Packet Aggregation

Use FC – Packet Aggregation

• Adaptive choice of N

• Take into account the output Queue delay

• Delay is used to adjust the output queue push rate and degree of aggregation MAC

AIDA

Network

PrioritizedOutput Queue

InputQueue

Input Queue

AggregationPool

Aggregator

De-Aggregator

NetworkOutput Queue

IsEmpty

degree

Queuing Delay

AggDegree&

RateController

Counting

Integrated SolutionsIntegrated Solutions

• Routing solutions must be– Power aware– Robust to lost messages, dead motes,

voids– Provide real-time QoS– Robust to communication range

variations and asymmetries– Handle moving end points – Scale– Secure

InteractionsInteractions

• Insidious interactions– Assume high density with many motes

turned off to enable long system lifetime

– Turn on when activity happens

– Then too many are active with many collisions and poor response

Future Research DirectionsFuture Research Directions

• New platforms/architectures• Higher level middleware• Application level semantics

– E.g., N events in nursing home implies patient is OK

• Aggregate behavior (algorithms, control, predict…)

• Systems implementations/applications

• Systems of systems (pervasive computing)

Future Directions of Research

Future Directions of Research

• Real-Time• Security• Privacy • Analysis Techniques and Tools• Mobility• View as Storage Systems• Programming Paradigms• Localization

Future Directions of Research

Future Directions of Research

• Data Association• Sensor Fusion• Classification• Turn-Key System• Autonomic WSN