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CS 851

Wireless Sensor Networks

Summary Lecture

Professor Jack Stankovic

Department of Computer Science

University of Virginia

November 24, 2003


Goals for Today’s Class


WSN


its niche


Applications revisited


Fundamentals
-

early


Intriguing Concepts


Future Research Areas

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 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 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

Implications


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



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?

Cost


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


20,000 nodes at $1 ea.
-
> $20,000



20,000 nodes at .10 ea.
-
> $2,000

Architecture
-

WSN



Sensors



Actuators



CPUs/Memory



Omni
-
dir. Radio

Architecture
-

WSN


Fixed Deployment (grid, mesh, …)

Taxonomy

HW

Capabilities


Application

Requirements

Software/Middleware

Fundamentals


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?

Fundamentals


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 ranges


False alarm processing


Asymmetry, lost messages, nodes move,

nodes sleep or die, etc.

Radio Model in Evaluation



Radio Model

DOI = Degree of Irregularity

DOI = 0.05

DOI =
0.2

Sensing versus Communication


Sensing/communication range ratio


Sensing/communication/power tradeoffs

Sensing

Range

Communication

Range

What if the

opposite?

Required degree

of coverage?

Fundamentals


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

Fundamentals


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 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
-

Events


Size of targets/events (point/area)


Discrete versus continuous


Probabilistic

Fire

X

Explosion

Fundamentals


Programming Paradigm

Programming Environment



OS: cygwin/Win2000 or gcc/Linux


Software: atmel tools

mot
e

programmin
g board

mote
-
PC
comms

Code
download

nesC


the nesC model:


interfaces:


uses


provides


components:


modules


configurations


application:= graph
of components

Component

A

Component

B

Component

D

Component

C

Application

configuration

configuration

Component

E

Component

F

Sensor/Actuator Clouds

Heterogeneous

Homogeneous

Resource management, team formation,

networking, …



Severe constraints


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

Make 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

Fundamentals


Group Management and Consensus

Example: 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


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

Base Station

Group Management
-

API



Create_Group(name,
function
,
criterion,atleast,acc
uracy
)
-

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 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 Zone

Forwarding Zone

Headway Distance

Hold & Forward Zone

What’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


What is the single most important issue that could
prevent WSNs from wide scale deployment?


Security


2
nd

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


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 Concepts


Space (geography/location)


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


Behavior (emerges versus controls)

SPEED

E2E Distance
j
FS
i
D
Actual Speed
Speed to
destination
(Set Point )
E2E Delay is bound by E2E Distance/Speed SetPoint
USE VELOCITY

Bound Errors





End
-
to
-
end



Real
-
time



Collisions



Congestion

Destination

Source

Error

Propagates

Race Ahead

Use Trajectories

Source

Destination


Trajectory Based Forwarding and Its Applications

Trajectory

Behavior


Flooding


stragglers


Epidemic algorithms and phase transitions


Global routing behavior


more emerged than
controlled


Feedback Control (FC)

2

3

5

9

10

7

Delay

Boo

4

11

6

12

Packet 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


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
Prioritized
Output Queue
Input
Queue
Input
Queue
Aggregation
Pool
Aggregator
De-Aggregator
Network
Output Queue
IsEmpty
degree
Queuing
Delay
AggDegree
&
Rate
Controller
Counting
Behavior
-

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



Interactions


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 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!