CS 851 Wireless Sensor Networks Summary Lecture Professor Jack Stankovic Department of Computer Science University of Virginia November 24, 2003.

CS 851Wireless Sensor Networks

Summary Lecture

Professor Jack Stankovic

Department of Computer Science

University of Virginia

November 24, 2003

Goals for Today’s ClassGoals for Today’s Class

• WSN – its niche

• Applications revisited

• Fundamentals - early

• Intriguing Concepts

• 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

• Radio Communications (Wireless)– Radio signal– Sensing signal– DSP

• MANET

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 – 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 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

– support new 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• Degree of mobility• Interface with the Internet• Handheld PDAs/laptops (seemless integration)• Heterogeneity• Placed versus ad hoc deployment

Any killer apps? Any wild new apps? Impact of cost changes?

CostCost

• 200 nodes at $100 ea. -> $20,000

• 20,000 nodes at $1 ea. -> $20,000

• 20,000 nodes at .10 ea. -> $2,000

Architecture - WSNArchitecture - WSN

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

Architecture - WSNArchitecture - WSN

• 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

– Scale?• Not necessarily for all systems

– Long Lifetimes?

FundamentalsFundamentals

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

• Multi-hop wireless radio communication– Consider all the realities of radio comm.

• Ratio of communication/sensing rangesFalse alarm processing

Asymmetry, lost messages, nodes move,nodes sleep or die, etc.

Radio Model in Evaluation Radio Model in Evaluation

Radio ModelDOI = Degree of Irregularity

DOI = 0.05 DOI = 0.2

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

• A mechanism for discovering spatial

relationships among objects

FundamentalsFundamentals

• Aggregate Behavior – biological metaphors• Simple decentralized algorithms (localized behavior)

– Epidemic/virus type algorithms

– Randomized algorithms

– Develop local rules that yield desired macroscopic behavior

• Uncertainty• Lazy behavior (most of the time/mobility)• Composition

– Functional

– Performance

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 - EventsFundamentals - Events

• Size of targets/events (point/area)

• Discrete versus continuous

• Probabilistic

Fire

X

Explosion

FundamentalsFundamentals

• Programming Paradigm

Programming EnvironmentProgramming Environment

• OS: cygwin/Win2000 or gcc/Linux

• Software: atmel tools

mote

programming board

mote-PC comms

Code download

nesCnesC

• the nesC model:– interfaces:

• uses

• provides

– components:• modules

• configurations

• application:= graph of components

Component A

Component B

ComponentD

Component C

Application

configurationconfigurationComponent

E

ComponentF

Sensor/Actuator CloudsSensor/Actuator Clouds

HeterogeneousHomogeneous

Resource management, team formation, networking, …

Severe constraints

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

Make Scripts MobileMake Scripts Mobile

Script can populate/migrate

Language + Run-time Environment = SensorWare

Scripts move NOT due to explicit user

instructions, but due to node’s state

and algorithmic instructions

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 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

Examples: Tracking andMap Regions

Examples: Tracking andMap Regions

Base Station

Group Management - APIGroup Management - API

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

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

configurations

Mobicast FrameworkMobicast Framework

Delivery zone: the area that message should be delivered

Forwarding zone: The area that message should be forwarded, which is some distance ahead of the delivery zone

Headway distance: The physical distance between the forwarding zone its delivery zone

Hold & Forward Zone: The area that receive the message before entering the forwarding zone

Delivery Zone

Future Delivery ZoneForwarding Zone

Headway Distance

Hold & Forward Zone

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 - SecurityFundamentals - 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 - AnalysisFundamentals - 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)

• Time (deadlines/periods/event lifetime/power lifetime)

• Behavior (emerges versus controls)

SPEEDSPEED

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

Use TrajectoriesUse Trajectories

Source

Destination

• Trajectory Based Forwarding and Its Applications

Trajectory

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 AggregationUse 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

Behavior - Integrated SolutionsBehavior - Integrated 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– 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

Future Directions of ResearchFuture Directions of Research

• New platforms/architectures

• Higher level middleware

• Aggregate behavior (algorithms, …)

• Systems implementations/applications

• Systems of systems (pervasive computing)

• Security

• Analysis

• Mobility

• Storage Systems

• Heterogeneous

• Programming Paradigms

The End!The End!