CS 851Wireless Sensor Networks

Introductory Lecture

Professor Jack Stankovic

Department of Computer Science

University of Virginia

September 2003

Purpose of this LecturePurpose of this Lecture

• Get you to think differently– Regardless of whether you are new to WSN or

have been working with them

• Introduce the basic key issues and their implications

• Reduce work to its essence

Motivation

• 1998– 100 million processors for workstations

– 6.4 billion for embedded systems

– approximately - 2% for workstations

• 2003– approximately 0%

• Ubiquitous computing (seemless, invisible, pervasive, amorphous, …)– wireless sensor networks

The field is exploding

Smart SpacesSmart Spaces

Smart School

Smart CitySmart Factory

Other Applications• Battlefields/Surveillance• Earthquake areas• Environmental Monitoring• Airport security• Emergency Response• Location Services

More Applications More Applications

• Interface with the Internet

• Handheld PDAs/laptops

• Element in pervasive computing

From your reading did you find interestingapplications or ideas about applications that wereSurprising?

Ad Hoc Wireless Sensor Networks

Ad Hoc Wireless Sensor Networks

• Sensors• Actuators• CPUs/Memory• Radio

Research QuestionsResearch Questions

• What are the correct HW elements to make solutions at the OS/middleware/application levels easier?– Current motes are only 1 possible platform

• How about DSPs? Special security HW?

– What capacities (cpu speed, memory, bandwidth, power, etc.) and their fundamental limitations, have if any, on solutions

Sensor/Actuator CloudsSensor/Actuator Clouds

HeterogeneousHomogeneous

Resource management, team formation, networking, …

Severe constraints

power, memory, bandwidth, cpu, cost, ...

• Background: Challenge fundamental assumptions underlying distributed systems technology

– How the problems change

• Key Areas to be Addressed

– Routing

– Power Management

– Localization

– Security

– Paradigms

– Theory

– Other Issues

• Examples: key research problems/solutions

– Spatial-Temporal Routing

– Application Independent Data Aggregation

– Localization Realities

How the Problems ChangeHow the Problems Change

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

– massively parallel interfaces (sometimes)

– faulty, highly dynamic, non-deterministic

– wireless (indirect impact on remote entity)

– 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 ChangeHow 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-*)

– fuzzy membership and team formation

– manage power/mobility/real-time/security tradeoffs

– geographical/location based (spatial)

– real-time/real world (temporal)

– data centric

ExamplesExamples

• Can you give me examples of simple decentralized algorithms that exhibit aggregate behavior?

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

Example: Resource Management

Example: Resource Management

• Measure communication errors – if too many

• increase communication power or if a mobile node it might move closer to the destination

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 ConsensusNew 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

Example: Group Management (Tracking)

Example: Group Management (Tracking)

Base Station

Group Management - APIGroup Management - API

– Create_Group(name,function,criterion,atleast,accuracy) - implicit and explicit

– Destroy_Group(name)– Join()– Leave()– Move_COG()– Expand() -- to gain sensing confidence– Shrink() -- to save power– Commit(grp_ID) - to synchronize group re-

configurations

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/min. commun.

The EssenceThe Essence

• Power

• Other limited resources (BW, CPU, …)

• Extreme Scale

• Changing “everything” / uncertainty

• Aggregation– unimportant individual nodes– decentralized, very simple algorithms

• What I do impacts you (collisions) – mutual exclusion

Six ThemesSix Themes

• Routing

• Power

• Localization

• Security

• Paradigms

• Theory

• Are there others? Yes…..

RoutingRouting

• Solutions must be– Power aware– Robust to lost messages, dead motes, voids– Real-time– Communication range variations– Moving end points– Amount of state information – Extreme Scale– Secure

PowerPower

• Example Algorithms– AFECA – power up and power down with time

proportional to the number of neighbors– GAF – create grid and keep at least one mote alive

in each grid (rotate among them in the grid)– SBPM – no grids; non-deterministic; minimize

connectivity; decentralized; complete sensing coverage (60% savings over no power management)

– Differentiated Surveillance • 50% less energy than “best” other solution

PowerPower

• Other power savings:– Vary transmission power– Turn off devices not needed

• On – all devices on

• Off – microprocessor in low power state so that registers/memory are not lost and clock interrupt can occur

– Checking – microprocessor and radio are on

– Choose routes that minimize power– Aggregate messages to save power

LocalizationLocalization

• Space (localization) and Time (clock sync) Basis– Environmental monitoring – where and when

events occurred

• Localization is a function of– Hardware available, cost requirement, signal

propagation model, timing and energy requirements, network makeup, nature of environment, node and beacon density, time sync, communication costs, error requirements, device mobility, …

SecuritySecurity• 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)

SecuritySecurity

• Localization– Attacker can report he is close to everyone– Chirp then wait then transmit to give false

location (normally chirp and transmit simultaneously – measure signals difference)

• Network Discovery– Provide false messages to create false topology– Prevent convergence

ParadigmsParadigms

• Virtual Machines

• SQL and data services models

• EnviroTrack

• Tie to physical systems/physics

• Swarm computing

• Biological metaphors

TheoryTheory• Theory of computation for WSN

• Emerging behavior of local/decentralized algorithms

• New graph theory

• New spatial-temporal analysis

• Aggregate control theory

• Utilization Equivalent Bounds

• Modeling and Analysis

• What are the fundamental scientific questions

Other Key Issues (1)Other Key Issues (1)

• Sensing/communication range ratio

• Sensing/communication/power tradeoffs

Sensing Range

CommunicationRange

What if the opposite?

Other Key Issues (2)Other Key Issues (2)

• Reality programming– Robust to faults– Sensor realities

• Don’t believe one reading

• Hysteresis

• Sensor fusion

• Activation delays

• Avoid false alarms

• Self-Calibration

Other Key Issues (3)Other Key Issues (3)

• Limited capacities

• Rapid dynamics

• Scaling factors and implications on behaviors– Extreme scaling

• Insidious interactions– High density with many motes off to enable long

system lifetime; turn on when activity happens then too many with many collisions and poor response

Other Key Issues (4)Other Key Issues (4)

• Architecture – hierarchy of control/capability/functionality

• Size of targets/events (point/area)

Fire

X

Explosion

Middleware ServicesMiddleware Services

• Non-traditional– Configuration service– Automatic calibration– Network programming– Reset services– Management services

Middleware ServicesMiddleware Services

• Real-Time Routing– SPEED – spatial-temporal concept

• Application Independent Data Aggregation– AIDA – feedback control

• Localization– APIT – realities of wireless world

Sensor Net RoutingSensor Net Routing • End-to-end• Real-time• Collisions• Congestion

Destination

Source

Assumption: Nodes know location

SPEEDSPEED

E2E Di stance

j

FS

iD

Actual Speed

Speed todestination(Set Point )

E2E Delay is bound by E2E Distance/Speed SetPoint

USE VELOCITY

Application Independent Data Aggregation

Application Independent Data Aggregation

• Expensive to acquire the “channel”

• Small data packets

• Group data packets into 1 MAC packet

• Works in addition to other data aggregation techniques which are based on semantics

Transport Layer

A.I DataAggregation

Network Layer

MAC Layer

ApplicationLayer

Data CentricRouting

MAC Layer

ApplicationLayer

TransportLayer

Data CentricRouting

MAC Layer

ApplicationLayer

TransportLayer

A.I DataAggregation

a. AIDA b. ADDA c. Both

Major Architectural DifferenceMajor Architectural Difference

FIXED SCHEMEFIXED SCHEME

• Accumulate N packets

• N: degree of aggregation– FIXED

– On Demand

– Adaptive/FC

• T: Time out for old packets when accumulation rate is slow

MAC

AIDA

Network

InputQueue

Input Queue

AggregatePool

AggregatorDe-Aggregator

NetworkOutput Queue

CounterPurge Timer

activate

Activate

ReachAggDegreeor Time Out

Activate

PrioritizedOutput Queue

DYNAMIC/Adaptive FCDYNAMIC/Adaptive FC

• 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

LocalizationLocalization

• Determine the geographic location of each node with a high degree of accuracy (necessary for application)– Applications

• search and rescue

• disaster relief

• target tracking

– Protocols• location aware routing

• guaranteeing sensing coverage

• location directory services

• Fundamental and Enabling Service

Radio Model in Evaluation Radio Model in Evaluation

Radio ModelDOI = Degree of Irregularity

DOI = 0.05 DOI = 0.2

M

NA

Anchor Receiving nodes

X

Known: Signal strength is not goodindicator of distance over the entireregion

Hypothesis: Signal strength IS accurate enough for nodes very close to each other!

Testing HypothesisTesting Hypothesis

300

350

400

450

500

550

600

1 5 9 13 17 21 25 29 33 37

Beacon Sequence Number

sig

na

l Str

en

gth

(m

v)

1 Foot

5 Feet

10 Feet

15 Feet

SummarySummary

• (Much) Current Distributed Systems Technology– wired networks, powerful nodes, highly reliable nodes,

interaction with users, fixed numbers of resources/team members, unlimited power, ...

• Embedded (Large Scale) Distributed Systems– wireless, simple nodes, unreliable nodes, interaction with

the environment, resources being added and deleted continuously, power management needed, …