Download sensor networks - BWN-Lab

WIRELESS SENSOR NETWORKS
Ian F. Akyildiz
Broadband & Wireless Networking Laboratory
School of Electrical and Computer Engineering
Georgia Institute of Technology
Tel: 404-894-5141; Fax: 404-894-7883
Email: [email protected]
Web: http://www.ece.gatech.edu/research/labs/bwn
1. INTRODUCTION
SENSOR NETWORKS ARCHITECTURE
Internet,
Satellite,
etc
Sink
Sink
 Several thousand
nodes
 Nodes are tens of
feet of each other
 Densities as high as
20 nodes/m3
Task
Manager
I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,
“Wireless Sensor Networks: A Survey”, Computer Networks (Elsevier) Journal, March 2002.
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Key technologies that enable
sensor networks:
Micro electro-mechanical systems
(MEMS)
Wireless communications
Digital electronics
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Sensor Network Concept
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Sensors nodes are very close to each other
Sensor nodes have local processing capability
Sensor nodes can be randomly and rapidly deployed
even in places inaccessible for humans
Sensor nodes can organize themselves to
communicate with an access point
Sensor nodes can collaboratively work
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SENSOR NODE HARDWARE
Location Finding System
SENSING UNIT
Mobilizer

PROCESSING UNIT

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Processor
Transceiver
Sensor ADC
Memory

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

Power Unit
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Small
Low power
Low bit rate
High density
Low cost (dispensable)
Autonomous
Adaptive
Power Generator
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Example: MICA Motes
BWN Lab @ GaTech
Processor and
Radio platform
(MPR300CB) is
based on Atmel
ATmega 128L
low power
microcontroller
that runs TinyOs
operating system
from its internal
flash memory.
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Berkeley Motes
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Specifications of the Mote
Processor/Radio Board MPR300CB
Remarks
Speed
4 MHz
Flash
128K bytes
SRAM
4K bytes
EEPROM
4K bytes
Radio Frequency
916MHz or 433MHz
ISM Band
Data Rate
40 Kbits/Sec
Max
Power
0.75 mW
Radio Range
100 feet
Power
2 x AA batteries
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Programmable
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Examples for Sensor Nodes
UCLA: WINS
UC Berkeley: COTS Dust
UC Berkeley:
Smart Dust
JPL: Sensor Webs
Rockwell: WINS
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Examples for Sensor Nodes
Rene Mote
Dot Mote
Mica node
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weC Mote
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Zylog’s eZ80
 Provides a way to
internet-enabled process
control and monitoring
applications.
 Temperature sensor,
water leak detector and
many more applications
 Metro IPWorks™ software
stack embedded
 Enables users to access
Webserver data and files
from anywhere in the
world.
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Systronix STEP board
 A first tool to support
hardware development
and prototyping with the
new Dallas TINI Java
Module.
 Embedding the internet
with TINI java
 A complete Java Virtual
Machine, TCP/IP stack,
ethernet hardware,
control area network,
iButton network and dual
RS232 all on SIMM72
module
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2. Sensor Networks Applications
Sensor networks may consist of sensor types such as:
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Seismic
Low sampling rate magnetic
Thermal
Visual
Infrared
Acoustic
Radar.
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Sensor Networks Applications
Sensors can monitor ambient conditions including:
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Temperature
Humidity
Vehicular movement
Lightning condition
Pressure
Soil makeup
Noise levels
The presence or absence of certain kinds of objects
Mechanical stress levels on attached objects, and
Current characteristics (speed, direction, size) of an object
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Sensor Networks Applications
Sensors can be used for:
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Continuous sensing
Event detection
Event identification
Location sensing
Local control of actuators
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Sensor Networks Applications
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Military
Environmental
Health
Home
Other commercial
Space exploration
Chemical processing
Disaster relief
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Sensor Networks Applications
Military Applications:
Command, control, communications, computing, intelligence, surveillance,
reconnaissance, targeting (C4SRT)
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Monitoring friendly forces, equipment and ammunition
Battlefield surveillance
Reconnaissance of opposing forces and terrain
Targeting
Battle damage assessment
Nuclear, biological and chemical (NBC) attack detection
and reconnaissance
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SensIT:
Sensor Information Technology

“SensIT was a program for developing software for distributed wireless
sensor networks.”
SensIT pursued two key thrusts:
* New networking techniques
* Network information processing.

“ SensIT nodes can support detection, identification, and tracking of threats,
as well as targeting and communication.”
http://www.darpa.mil/DARPATech2000/Speeches/ITOSpeeches/ITOSensIT(Kumar).doc
S. Kumar, D. Shepherd, “SensIT: Sensor information technology for the warfighter,” 4th
Int. Conference on Information Fusion, 2001.
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ForceNet (US Navy)
ForceNet binds together Sea Strike, Sea Shield, and Sea Basing.
Sea Strike—Projecting Precise and Persistent Offensive Power
 Sea Shield—Projecting Global Defensive Assurance
 Sea Basing—Projecting Joint Operational Independence

It is the framework for naval warfare that integrates
warriors, sensors, command and control, platforms, and weapons
into a networked, distributed combat force.
http://www.chinfo.navy.mil/navpalib/cno/proceedings.html
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SAD: SEAL Attack Detection &
Anti-Submarine Warfare
antenna
led
hooks
cable
sensor
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Other Projects

ESG: Expeditionary Sensor Grid.

NCCT: Network Centric Collaborative Targeting.

Sea Web.

Smart Web

Sensor Web
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Other Military Applications
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Intrusion detection (mine fields)
Detection of firing gun (small arms) location
Chemical (biological) attack detection
Targeting and target tracking systems
Enhanced guidance and IFF systems
Battle damage assessment system
Enhanced logistics systems,
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Environmental Applications
 Tracking the movements of birds, small animals, and insects
Monitoring environmental conditions that affect crops and livestock
 Irrigation
 Macroinstruments for large-scale Earth monitoring and
planetary exploration
 Chemical/biological detection
 Biological, Earth, and environmental monitoring in marine, soil, and
atmospheric contexts
 Meteorological or geophysical research
 Pollution study, Precision agriculture
 Biocomplexity mapping of the environment
 Flood detection, and Forest fire detection.

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Forest Fire Detection
Purpose: Detect fire before spread uncontrollable.
 Maybe strategically, randomly, and
densely deployed
 Millions of sensor nodes can be deployed
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Health Applications
Providing interfaces for the disabled
 Integrated patient monitoring
 Diagnostics
 Monitoring the movements and internal processes of
insects or other small animals
 Telemonitoring of human physiological data
 Tracking and monitoring doctors and patients inside a
hospital, and
 Drug administration in hospitals

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Drug Administration in Hospitals
Purpose: Minimize prescribing the wrong medication to patients.
 Identify patients allergies and required medications
Current computerized systems can reduce medication errors
and prevent many Adverse Drug Events (ADE)
 Cost of ADEs is as high as $5.6 millions/year /hospital,
and 770,000 Americans injured and die annually because of ADEs.
 Save hospitals up to $500,000/year
 Only 5% of civilian hospitals have computerized system
 Can prevent 84% of dosage errors
 Start-up cost is around $2 million (cheap sensor nodes can be deployed).

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Home Applications
Types:
Security
 Home automation, and
 Smart Environment

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Smart Environment
Purpose: Allowing users to seamlessly
interact with their environment.
Two perspectives:
human-centered, or technology-centered
 Example: “Aware Home” project at
Georgia Tech.

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Smart Environment
Human-centered:
A smart environment must adapt to the needs
of the users in terms of I/O capabilities.
Technology-centered
New hardware technologies, networking
solutions and middleware services must be
developed.
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Smart Environment (Cont’d)
Wired or wireless connection
Room 2
Room 1
Scanner and phone
with embedded
sensor nodes.
Computers
with embedded
sensor nodes.
User enters
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Server
User enters
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Commercial Applications
Building virtual keyboards
 Monitoring product quality
 Constructing smart office spaces
 Interactive toys
 Monitor disaster areas
 Smart spaces with sensor nodes embedded inside
 Machine diagnosis
 Interactive museums
 Managing inventory control
 Environmental control in office buildings
 Detecting, and monitoring car thefts, and
 Vehicle tracking and detection.

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Vehicle Tracking and Detection
Purpose: Locate a vehicle
AMPS sensor nodes are deployed
 Two ways to detect and track the vehicle
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- determine the line of bearing (LOB) in each
cluster and then forward to the base-station, or
- send all the raw data to the base-station
(uses more power as distance increases)
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iBadge - UCLA
 Investigate behavior of children/patient
 Features:
–
–
–
–
Speech recording/replaying
Position detection
Direction detection/estimation (compass)
Weather data: Temperature, Humidity,
Pressure, Light
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iBadge - UCLA
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iButton Applications
 Caregivers Assistance
– Do not need to keep a bunch of keys. Only
one iButton will do the work
 Elder Assistance
– They do not need to enter all their personal
information again and again. Only one touch
of iButton is sufficient
– They can enter their ATM card information
and PIN with iButton
– Vending Machine Operation Assistance
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3. Factors Influencing Sensor
Network Design
A. Fault Tolerance (Reliability)
B. Scalability
C. Production Costs
D. Hardware Constraints
E. Sensor Network Topology
F. Operating Environment
G. Transmission Media
H. Power Consumption
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A. Fault Tolerance
(Reliability)

Sensor nodes may fail or be blocked due to lack of power
have physical damage, or environmental interference.
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The failure of sensor nodes should not affect the overall
task of the sensor network.
This is called RELIABILITY or FAULT TOLERANCE,
i.e., ability to sustain sensor network
functionality without any interruption
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Fault Tolerance
(Reliability) (Ctn’d)

Reliability (Fault Tolerance) of a sensor node is modeled:
R ( t )  exp(  t )
k
k
i.e., by Poisson distribution, to capture the probability of not
having a failure within the time interval (0,t)
with lambda_k is the failure rate of the sensor node k and
t is the time period.
G. Hoblos, M. Staroswiecki, and A. Aitouche, “Optimal Design of Fault Tolerant Sensor Networks,”
IEEE International Conference on Control Applications, pp. 467-472, Anchorage, AK, September 2000.
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Fault Tolerance
(Reliability) (Ctn’d)
EXAMPLE:
Suppose: lambda = 3.5 * 10-3

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t=10sec  R = 0.97
t=20sec  R= 0.93
t=30sec  R= 0.9
t=50sec  R=0.84
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Fault Tolerance
(Reliability) (Ctn’d)

Reliability (Fault Tolerance) of a broadcast range with
N sensor nodes is calculated from:
N
R(t )  1   [1  Rk (t )]
k 1
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Fault Tolerance
(Reliability) (Ctn’d)
EXAMPLE:
 How many sensor nodes are needed within a broadcast
radius (range) to have 99% fault tolerated network?
Assuming all sensors within the radio range have same
reliability, prev. equation becomes
R(t )  1  [1  R(t )]
N
Drop t and substitute f = (1 – R).
o.99 = 1 – fN  N = 2
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Fault Tolerance
(Reliability) (Ctn’d)
REMARK:
1. Protocols and algorithms may be designed to address
the level of fault tolerance required by sensor
networks.
2. If the environment has little interference, then the
requirements can be more relaxed.
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Fault Tolerance
(Reliability) (Ctn’d)
Examples:
1. House to keep track of humidity and temperature
levels  the sensors cannot be damaged easily or interfered
by environments  low fault tolerance (reliability)
requirement!!!!
2. Battlefield for surveillance the sensed data are critical and
sensors can be destroyed by enemies  high fault tolerance
(reliability) requirement!!!
Bottomline: Fault Tolerance (Reliability)
depends heavily on applications!!!
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B. Scalability

The number of sensor nodes may reach millions in studying
a field/application

The density of sensor nodes can range from few to several
hundreds in a region (cluster) which can be less than 10m in
diameter.
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Scalability (Ctn’d)
The Sensor Node Density: i.e., the number of expected nodes
within the radio range R:
 ( R)  ( N  R ) / A
2
where N is the number of scattered sensor nodes
in region A and R is the radio transmission range.
Basically:  is the number of sensor nodes within the
transmission radius of each sensor node in region A.
The number of sensor nodes in a region is used to indicate the
node density depends on the application.
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Network Configuration
Sink node
Radio Range R
Sensor nodes
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Scalability (Ctn’d)
Assuming that connection establishment is equally
likely with any node within the radio range R of
the given node, the expected hop distance is:
dhop = 2R/3
e.g., R=20m  13.33m
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Network Configuration
dnei  Expected distance to the nearest neighbor, may or may not be communicating neighbor.
dhop  Expected distance to the next hop, i.e., distance to communicating neighbor. dhop>=dnei
Sink node
Radio Range R
dne
i
dho
p
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Sensor nodes
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Scalability (Ctn’d)
EXAMPLE:
Assume sensor nodes are evenly distributed in the sensor
field, determine the node density if 200 sensor nodes
are deployed in a 50x50 m2 region where each sensor
node has a broadcast radius of 5 m.
Use the eq.
mu (R) = (200 * pi * 52 )/(50*50) = 2 * pi
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Scalability (Cont’d)
Examples:
1. Machine Diagnosis Application:
less than 300 sensor nodes in a 5 m x 5 m region.
2. Vehicle Tracking Application:
Around 10 sensor nodes per cluster/region.
3. Home Application: 2 dozens or more.
4. Habitat Monitoring Application: Range from 25 to 100 nodes/cluster
5. Personal Applications:
Ranges from 100s to 1000s, e.g., clothing, eye glasses, shoes, watch, jewelry
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C. Production Costs
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Cost of sensors must be low so that the
sensor networks can be justified!!!
PicoNode: less than $1
Bluetooth system: around $10,THE OBJECTIVE FOR SENSOR COSTS
must be lower than $1!!!!!!!
Currently:  COTS Dust Motes 
ranges from $25 to $172
(STILL VERY EXPENSIVE!!!!)
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D. Sensor Node Hardware
A Sensor Node
Location Finding System
SENSING UNIT
Mobilizer
Processor
Transceiver
Sensor ADC
Memory
Power Unit
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Small
 Low power
 Low bit rate
 High density
 Low cost (dispensable
 Autonomous
 Adaptive

PROCESSING UNIT
Power Generator
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E. Sensor Network Topology
Internet,
Satellite,
etc
Sink
Several thousand nodes
 Nodes are tens of feet
of each other
 Densities as high as 20
nodes/m3

Sink
Task
Manager
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Sensor Network Topology (Ctn’d)
Topology maintenance and change:
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Pre-deployment and Deployment Phase
Post Deployment Phase
Re-Deployment of Additional Nodes
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Sensor Network Topology (Ctn’d)
Pre-deployment and Deployment Phase
Sensor networks can be deployed by:
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Dropping from a plane
Delivering in an artillery shell, rocket or missile
Throwing by a catapult (from a ship board, etc.)
Placing in factory
Being placed one by one by a human or a robot
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Sensor Network Topology (Ctn’d)
Initial deployment schemes must
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reduce installation cost
eliminate the need for any pre-organization
and pre-planning
increase the flexibility of arrangement
promote self organization and fault tolerance.
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Sensor Network Topology (Ctn’d)
POST-DEPLOYMENT PHASE
After deployment, topology changes are due to change
in sensor nodes’
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position
reachability (due to jamming, noise, moving
obstacles, etc.)
available energy
malfunctioning
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F. Operating Environment
Sensor networks may work
 in busy intersections
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in the interior of a large machinery
at the bottom of an ocean
inside a twister
at the surface of an ocean
in a biologically or chemically contaminated field in a
battlefield beyond the enemy lines
in a house or a large building
in a large warehouse
attached to animals
attached to fast moving vehicles
in a drain or river moving with current
……………………
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G. TRANSMISSION MEDIA

Radio or Infrared or Optical Media
ISM (Industrial, Scientific and Medical Bands)
 433 MHz ISM Band in Europe and 915 MHz
as well as 2.4 GHz ISM Bands in North
America.
REASONS: Free radio, huge spectrum allocation
and global availability.

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Transmission Media
 In a Multihop sensor network nodes are linked by
Wireless medium
– Radio Frequency (RF)
 Most of the current sensor node HW is based on it
 Do not need Line of Sight
 Can hide these sensors
– Infrared (IR)
 License free
 Robust to interference
 Cheaper and easier to build
 Require line of sight
 Short Range Solution
– Optical Media
 Require Line of sight
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H. POWER CONSUMPTION
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Sensor node has limited power source (~1.2V).
Sensor node LIFETIME depends on battery
lifetime
Sensors can be a DATA ORIGINATOR or a
DATA ROUTER.
Power conservation and power management
are important  POWER AWARE PROTOCOLS
must be developed.
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Power Consumption (Ctn’d)
•
Power consumption in a sensor network can be divided
into three domains
Communication
 Data Processing
 Sensing

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Power Consumption (Ctn’d)
Communication
A sensor expends maximum energy in data
communication (both for transmission and
reception).
NOTE:
For short range communication with low radiation power (~0 dbm),
transmission and reception power costs are approximately the same,
(e.g., modern low power short range transceivers consume between
15 and 300 milliwatts of power when sending and receiving).
Transceiver circuitry has both active and start-up
power consumption
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Power Consumption (Ctn’d)

Power consumption for data communication (Pc)
Pc = Pte + Pre + P0
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
Pte/re
P0
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is the power consumed in the transmitter/receiver
electronics (including the start-up power)
is the output transmit power
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Power Consumption in Data Communication
(PC) (Detailed Formula)
Pc  NT [ PT (Ton  Tst )  Pout (Ton )]  N R [ PR ( Ron  Rst )]
where
PT is power consumed by transmitter
PR is power consumed by receiver
Pout is output power of transmitter NT is the number of times
Ton is time for “transmitter on”
transmitter is switched on per
Ron is time for “receiver on”
unit time
Tst is start-up time for transmitter N is the number of times receiver
R
Rst is start-up time for receiver
is switched on per unit time
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Power Consumption in Communication (Ctn’d)

Ton = L / R
where L is the packet size and R is the data rate.
Low power radio transceiver has typical PT and
PR values around 20 dBm and Pout close to 0 dBm.
Note that PicoRadio aims at a Pc value of –20 dBm.
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Power Consumption in Communication (Ctn’d)
START-UP POWER: REMARK:
Sensors communicate in short data packets
 Start-up power starts dominating as packet
size is reduced
 It is inefficient to turn the transceiver ON and OFF
because a large amount of power is spent in
turning the transceiver back ON each time.

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Power Consumption in Data Processing
(Ctn’d)
This is much less than in communication.
EXAMPLE:
Energy cost of transmitting 1 KB a distance of
100 m is approx. equal to executing 3 Million
instructions by a 100 million instructions per
second processor.

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Local data processing is crucial in minimizing
power consumption in a multi-hop network
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Power Consumption in Data Processing
(Ctn’d)


Complementary Metal Oxide Semiconductor
(CMOS) technology used in designing processors
has energy limitations
Dynamic Voltage Scaling and other Low power
CPU organization strategies need to be explored
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Power Consumption in Data Processing (Pp)
Pp  C V
2
dd
 f  Vdd  Io  exp{Vdd / n'VT }
Where
C is the total switching capacitance; Vdd is the voltage swing;
F is the switching frequency
The second term indicates the power loss due to leakage currents.
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Power Consumption (Ctn’d)
(Another Simple Energy Model)
Assuming a sensor node is only operating in
transmit and receive modes with the following
assumptions:
 Energy to run circuitry:
E_{elec} = 50 nJ/bit
 Energy for radio transmission:
E_{amp} = 100 pJ/bit/m2
 Energy for sending k bits over distance d
E_Tx (k,D) = E_{elec} * k + E_{amp} * k * d2
 Energy for receiving k bits:
E_Rx (k,D) = E_{elec} * k
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ENERGY MODEL
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Power Consumption (Ctn’d)
(Another Simple Energy Model)
What is the energy consumption if 1 Mbit of
information is transferred from the source
to the sink where the source and sink are
separated by 100 meters and the broadcast
radius of each node is 5 meters?
Assume the neighbor nodes are overhearing
each other’s broadcast.
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Power Consumption (Ctn’d)
(Another Simple Energy Model)
100 meters / 5 meters = 20 pairs of transmitting and
receiving nodes (one node transmits and one node receives)
E_Tx (k,D) = E_{elec} * k + E_{amp} * k * D2
E_{Tx} = 50 nJ/bit . 106 + 100 pJ/bit/m2 . 106 . 52 =
= 0.5J + 0.0025 J = 0.0525 J
E_Rx (k,D) = E_{elec} * k
E_{Rx} = 0.05 J
E_{pair} = E_{Tx} + E_{Rx} = 0.1025J
E_{T} = 20 . E_{pair} = 20. 0.1025J = 2.050 J
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Power Consumption in Sensing (Ctn’d)
Depends on
 Application
 Nature of sensing: Sporadic or Constant
 Detection complexity
 Ambient noise levels
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Sensor Networks
Communication Architecture
Sensor Node
Internet,
Satellite,
etc
Task
Manager
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Sink
F


E
C
B
A
Sensor Field
D
Collect data
Route data back to the sink
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Sensor Networks Communication
Architecture
Data Link Layer
Physical Layer
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Task Management Plane
Network Layer
Mobility Management Plane
Transport Layer
Power Management Plane
Application Layer
Used by sink and all sensor nodes
 Combines power and routing awareness
 Integrates data with networking protocols
 Communicates power efficiently through
wireless medium and
 Promotes cooperative efforts.

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WHY CAN’T AD-HOC NETWORK
PROTOCOLS BE USED HERE?
 Number of sensor nodes can be several orders of
magnitude higher
 Sensor nodes are densely deployed and are prone to
failures
 The topology of a sensor network changes very
frequently due to node mobility and node failure
 Sensor nodes are limited in power, computational
capacities, and memory
 May not have global ID like IP address.
 Need tight integration with sensing tasks.
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5. APPLICATON LAYER FRAMEWORK



Sensor Network Management Protocol (SMP)
Task Assignment and Data Advertisement Protocol
Sensor Query and Data Dissemination Protocol
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Sensor Network Topology
Internet,
Satellite,
etc
Users
sensor node
gateway (gnode)
Server
Task
Manager
(Database)
wireless link
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APPLICATON LAYER
SMP: Sensor Managament Protocol
System Administrators interact with Sensors using SMP.
TASKS:







Moving the sensor nodes
Turning sensors on and off
Querying the sensor network configuration and the status of
nodes and re-configuring the sensor network
Authentication, key distribution and security in data
communication
Time-synchronization of the sensor nodes
Exchanging data related to the location finding algorithms
Introducing the rules related to data aggregation,
attribute-based naming and clustering to the sensor nodes
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APPLICATON LAYER
(Query Processing)
Users can request data from the network-> Efficient Query Processing
User Query Types:
1. HISTORICAL QUERIES:
Used for analysis of historical data stored in a storage area (PC),
e.g., what was the temperature 2 hours back in the NW quadrant.
2. ONE TIME QUERIES:
Gives a snapshot of the network, e.g., what is the current
temperature in the NW quadrant.
3. PERSISTANT QUERIES:
Used to monitor the network over a time interval with respect to
some parameters, e.g., report the temperature for the next 2 hours.
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QUERYING
– Continuous
 Sensors communicate their data continuously at a prespecified rate.
– Event Driven
 The sensors report information only when the event of interest occurs.
– Observer Initiated (request-reply):
 Sensors only report their results in response to an explicit request from
the observer.
Aggregate queries
Complex queries
Queries for replicated data
– Hybrid
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APPLICATON LAYER
Sensor Query and Tasking Language (SQTL):
(C-C Shen, et.al., “Sensor Information Networking Architecture and Applications”, IEEE
Personal Communications Magazine, pp. 52-59, August 2001.)


SQTL is a procedural scripting language.
It provides interfaces to access sensor hardware:
- getTemperature, turnOn
for location awareness:
- isNeighbor, getPosition
and for communication:
- tell, execute.
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APPLICATON LAYER
Sensor Query and Tasking Language (SQTL):

By using the upon command, a programmer can create
an event handling block for three types of events:
- Events generated when a message is received by a sensor node,
- Events triggered periodically,
- Events caused by the expiration of a timer.

These types of events are defined by SQTL keywords
receive, every and expire, respectively.
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Simple Abtract Querying Example
Select [ task, time, location, [distinct | all], amplitude,
[[avg | min |max | count | sum ] (amplitude)]]
from [any , every , aggregate m]
where [ power available [<|>] PA |
location [in | not in] RECT |
tmin < time < tmax |
task = t |
amplitude [<|==|>] a ]
group by task
based on [time limit = lt | packet limit = lp |
resolution = r | region = xy]
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Data Centric Query
 Attribute-based
naming architecture
 Data centric
protocol
 Observer sends a
query and gets the
response from valid
sensor node
 No global ID
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APPLICATON LAYER
Task Assignment and Data Advertisement Protocol

INTEREST DISSEMINATION
* Users send their interest to a sensor node,
a subset of the nodes or the entire network.
* This interest may be about a certain attribute
of the sensor field or a triggering event.

ADVERTISEMENT OF AVAILABLE DATA
* Sensor nodes advertise the available data to
the users and the users query the data which
they are interested in.
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APPLICATON LAYER
Sensor Query and Data Dissemination Protocol
Provides
user applicatons with interfaces to issue
queries, respond to queries and collect incoming
replies.
These
queries are not issued to particular nodes, instead
ATTRIBUTE
BASED NAMING (QUERY)
“The locations of the nodes that sense temperature
higher than 70F”
LOCATION BASED NAMING (QUERY)
“Temperatures read by the nodes in region A”
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Interest Dissemination


Interest dissemination is performed to assign the sensing tasks to the sensor nodes.
Either sinks broadcast the interest or sensor nodes broadcast an advertisement for
the available data and wait for a request from the sinks.
71
75
68
Sink
67
66
71
71
68
71
69
Query:
Sensor nodes that read >70oF temperature
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Data Aggregation (Data Fusion)

The sink asks the sensor nodes to report certain conditions.
Data coming from multiple sensor nodes are aggregated.
71
75
68
Sink
66
67
71
71
68
71
69
Query:
Sensor nodes that read >70oF temperature
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Location Awareness
(Attribute Based Naming)

Query an Attribute
of the sensor field
Region A
71
75
68
Sink
67
66
71
71
Region C
Query:
Temperatures read by the nodes in
Region A
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68
69
Region B
Important
for broadcasting,
multicasting, geocasting and anycasting
92
APPLICATON LAYER RESEARCH NEEDS




Sensor Network Management Protocol
Task Assignment and Data Advertisement Protocol
Sensor Query and Data Dissemination Protocol
Sophisticated GUI
(Graphical User Interface) Tool
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NETWORK LAYER
(ROUTING BASIC KNOWLEDGE)
The constraints to calculate the routes:
1. Additive Metrics:
Delay, hop count, distance, assigned costs (sysadmin preference),
average queue length...
2. Bottleneck Metrics:
Bandwidth, residual capacity and other bandwidth related metrics.
REMARK:
All routing algorithms are based on the same principle used as in Dijkstra's,
which is used to find the minimum cost path from source to destination.
Dikstra and Bellman solve the SHORTEST PATH PROBLEM…
RIP (Distant Vector Algorithm) -> Bellman/Ford Algorithm
OSPF (Open Shortest Path Algorithm)  Dikstra Algorithm
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Routing Algorithms Constraints Regarding
Power Efficiency (Energy Efficient Routing)
E (PA=1)
F (PA=4)
 Maximum power available (PA) route
Minimum hop route
 Minimum energy route
D (PA=3)
T
Sink
 Maximum minimum PA node
route (Route along which the
minimum PA is larger than the
A (PA=2)
B (PA=2)
C (PA=2) minimum PAs of the other routes
is preferred, e.g., Route 3 is the
Route 1: Sink-A-B-T (PA=4)
most efficient; Route 1 is the
Route 2: Sink-A-B-C-T (PA=6)
second).
Route 3: Sink-D-T (PA=3)
Route 4: Sink-E-F-T (PA=5)

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Why can’t we use conventional
routing algorithms here?
Global (Unique) addresses, local addresses.
Unique node addresses cannot be used in many sensor
networks
- sheer number of nodes
- energy constraints
- data centric approach
Node addressing is needed for
- node management
- sensor management
- querying
- data aggregation and fusion
- service discovery
- routing
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Addressing in Sensor Networks
1. Attribute based naming and data centric routing
2. Spatial addressing (location awareness)
3. Address reuse
4. Query mapping.
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NETWORK LAYER
(ROUTING for SENSOR NETWORKS)
Important considerations:




Sensor networks are mostly data centric
An ideal sensor network has attribute based
addressing and location awareness
Data aggregation is useful unless it does not
hinder collaborative effort
Power efficiency is always a key factor
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Some Concepts
 Data-Centric
– Node doesn't need an identity
 What is the temp at node #27 ?
– Data is named by attributes
 Where are the nodes whose temp recently exceeded 30
degrees ?
 How many pedestrians do you observe in region X?
 Tell me in what direction that vehicle in region Y is
moving?
 Application-Specific
– Nodes can perform application specific data
aggregation, caching and forwarding
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Attribute Based Naming
Data-Centric Routing


Interest dissemination is performed to assign the sensing tasks to the sensor nodes.
Either sinks broadcast the interest or sensor nodes broadcast an advertisement for
the available data and wait for a request from the sinks.
71
75
68
Sink
67
66
71
71
68
Query:
Nodes that read >70oF temperature
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71
69
100
Data Centric Routing
 Attribute-based
naming architecture
 Data centric
protocol
 Observer sends a
query and gets the
response from valid
sensor node
 No global ID
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Data Aggregation (Data Fusion)



To solve the implosion and overlap problems in data centric routing.
Sensor network is perceived as a reverse multicast tree.
The sink asks the sensor nodes to report certain conditions. Data coming from multiple sensor nodes
are aggregated.
71
75
68
Sink
66
67
71
71
68
71
69
Query:
Nodes that read >70oF temperature
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Data Aggregation
Categorization of Data Aggregation Schemes:
1. Temporal or spatial aggregation
2. Snapshot or periodical aggregation
3. Centralized or distributed aggregation
4. Early or late aggregation
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Polygonal (Spatial) Addressing
Location Awareness
Region A
71
75
68
Sink
67
66
71
71
Region C
Query:
Temperatures read by the nodes in
Region A
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68
69
Region B
Important
for broadcasting,
multicasting, geocasting and
104
anycasting
Taxonomy of Routing Protocols
for Sensor Networks
Categorization of Routing Protocols for Wireless Sensor Networks:
(K. Akkaya, M. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,” Elsevier AdHoc Networks, 2004)
1. Data Centric Protocols
Flooding, Gossiping, SPIN, SAR (Sequential Assignment
Routing) , Directed Diffusion, Rumor Routing, Gradient Based
Routing, Constrained Anisotropic Diffused Routing, COUGAR,
ACQUIRE
2. Hierarchical
LEACH, TEEN (Threshold Sensitive Energy Efficient Sensor
APTEEN, PEGASIS, Energy Aware Scheme
Network Protocol),
3. Location Based
MECN, SMECN (Small Minimum Energy Com Netw), GAF
(Geographic Adaptive Fidelity), GEAR
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Conventional Approach
FLOODING
Broadcast data to all neighbor nodes
A
C
B
D
E
F
G
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ROUTING ALGORITHMS
Gossiping
GOSSIPING:
Sends data to one randomly selected neighbor.
Example:
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Problems of
Flooding and Gossiping
PROBLEMS:
Although these techniques are simple and reactive, they have
some disadvantages including:
* Implosion
(NOTE: Gossiping avoids this by selecting only one node; but this causes delays to
propagate the data through the network)
* Overlap
* Resource Blindness
* Power (Energy) Inefficient
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Problems
Implosion
(a)
A
B
(a)
(a)
C
D
Data Overlap
q
r
A
s
B
(a)
(q,r)
C
(r,s)
Resource Blindness
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No knowledge about the
available power of resources
109
Gossiping
 Uses randomization to save energy
Selects a single node at random and sends the data
to it
 Avoids implosions
 Distributes information slowly
 Energy dissipates slowly
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The Optimum Protocol
A
“Ideal”
–
–
–
–
Shortest-path routes
Avoids overlap
Minimum energy
Need global topology
information
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C
B
D
E
F
G
111
Ideal Dissemination
No implosion and
no overlap
Disseminate in
shortest possible
time
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SPIN: Sensor Protocol for
Information via Negotiation
(W.R. Heinzelman, J. Kulik, and H. Balakrishan, “Adaptive Protocols for
Information Dissemination in Wireless Sensor Networks”,
Proc. ACM MobiCom’99, pp. 174-185, 1999 )
Two basic ideas:
 Sensors communicate with each other
about the data that they already have and
the data they still need to obtain


to conserve energy and operate efficiently
exchanging data about sensor data may be cheap
 Sensors must monitor and adapt to changes
in their own energy resources
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SPIN
Uses three types of messages: ADV, REQ, and DATA.
When a sensor node has something new, it broadcasts
an advertisement (ADV) packet that contains the new
data, i.e., the meta data.
- Interested nodes send a request (REQ) packet.
Data is sent to the nodes that request by DATA
packets.
This will be repeated until all nodes will get a copy.
-
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SPIN
Good for disseminating information to all sensor nodes.
 SPIN is based on data-centric routing where the sensors broadcast an
advertisement for the available data and wait for a request from
interested sinks

1.
2.
1. ADV
2. REQ
3. DATA
3.
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SPIN
Meta-Data <=> Data Naming
ADV
A
B
REQ
A
B
DATA
A
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B
 ADV- advertise/name
data
 REQ- request specific
data
 DATA- requested data
116
SPIN
ADV
REQ
DATA
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ADV
DATA
REQ
117
EXAMPLE
Sensor A sends meta-data to neighbor
A
B
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Sensor B requests data from Sensor A
A
B
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Sensor A sends data to Sensor B
A
B
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Sensor B aggregates data and sends
meta-data for A and B to neighbors
A
B
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All but 1 neighbor request data
A
B
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Sensor B sends requested data to
neighbors
A
B
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SPIN-1 Protocol
SPIN-1
– 3-stage handshake protocol
– Advantages
Simple
Implosion avoidance

Disadvantages
* Cannot isolate the nodes that do not want to receive the
information.
* Consume unnecessary power.
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SPIN-2
Spin-2
– SPIN-1 + low-energy threshold
– Modifies behavior based on current
energy resources
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SPIN-2
 Adds a simple energy conservation heuristic
 When energy is plentiful, SPIN-2 behaves
like SPIN-1
 When energy approaches a low-energy
threshold, SPIN-2 node reduces its
participation in the protocol (DORMANT)
 participate in a stage of protocol only if the node
believes that it can complete all the remaining stages
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SPIN Algorithm Variants
 Flooding -- Each node floods new data to
all of its neighbors.
 Gossiping -- Each node floods all its data
to one, randomly selected neighbor.
 Negotiating -- nodes decide what data to
send based on meta-data advertisements.
SPIN-1
Zzz...
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 Sleeping -- Same as negotiating, except
that nodes stop sending messages when
energy is low. SPIN-2
127
CONCLUSIONS
 Flooding converges first
– No delays
 SPIN-1
– Reduces energy by 70%
– No redundant DATA messages
 SPIN-2 distributes
– 10% more data per unit energy than
SPIN-1
– 60% more data per unit energy than
flooding
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ROUTING ALGORITHM
(DIRECTED DIFFUSION)
(C. Intanagonwiwat, R. Gowindan and D. Estrin, “Directed Diffusion: A Scalable and Robust
Communication Paradigm for Sensor Networks”, Proc. ACM MobiCom’00, pp. 56-67, 2000.)
This is a DATA CENTRIC ROUTING scheme!!!!
- The idea aims at diffusing data through sensor nodes by using
a naming scheme for the data.
- The main reason behind this is to get rid off unnecessary
operation of routing schemes to save Energy.
Also Robustness and Scaling requirements need to be considered.
-
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Data Centric
 Data-Centric
– Sensor node does not need an identity
 What is the temp at node #27 ?
– Data is named by attributes
 Where are the nodes whose temp recently exceeded 30
degrees ?
 How many pedestrians do you observe in region X?
 Tell me in what direction that vehicle in region Y is
moving?
 Application-Specific
– Nodes can perform application specific data
aggregation, caching and forwarding
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DIRECTED DIFFUSION
DD is data centric, i.e., data generated by sensor nodes
is NAMED by ATTRIBUTE-VALUE pairs.
* A sensor node requests data by sending interests
for named data.
* Data matching the interest is then drawn down towards
that node.
* Intermediate sensor nodes can cache or transform data
and may direct interests based on previously cached data.
*
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DIRECTED DIFFUSION
* An arbitrary sensor node (usually the SINK) uses attribute-value pairs
(interests) for the data and queries the sensors in an on-demand basis.
* In order to create a query, an interest is defined using a list of
attribute-value pairs such as name of objects, interval, duration,
geographical area, etc.
* The sink queries the sensors in an on-demand basis using these pairs.
* The sink broadcasts this interest to sensor nodes.
* Each sensor node then stores this interest entry in its cache.
* The interests in the caches are then used to compare the received
data with the values in the interests.
-
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DIRECTED DIFFUSION
Example:
* The users query is transformed into an interest that is diffused towards nodes in
regions X or Y.
* When a node in that region receives an interest it activates its sensors which begin
collecting information about pedestrians.
* When the sensors report the presence of pedestrians this returns along the
reverse path of interest propagation.
* Intermediate nodes might aggregate the data, e.g., more accurately pinpoint the
pedestrians location by combining reports from several sensors.
*
An important feature of directed diffusion is that interest and data propagation and
aggregation are determined by localized interactions (message changes between
neighbors or nodes within some vicinity)
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DIRECTED DIFFUSION
Data is named using attribute-value pairs, e.g.,
Example: (Animal Tracking Task)
Type = four legged animal (detect animal location)
Interval = 20 ms (send back events every 20 ms)
Duration = 10 seconds (.. for the next 10 seconds)
Rec = [-100,100,200,00] (from sensors within the rectangle)
The task description specifies an interest for data matching for attributes
 called INTEREST.
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DIRECTED DIFFUSION
The data sent in response to interests are also named similarly.
Example:
Sensor detecting the animal generates the following data:
Type – four legged animal (type of animal seen)
Instance= elephant (instance of this type)
Locaton = (125,220) (node location)
Intensity = 0.6 (signal amplitude measure)
Confidence = 085 (confidence in the match)
Timestamp= 01:20:40 (event generation time)
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Directed Diffusion
Source
Sink
Data
Delivery
Gradient
Setup
Interest
Propagation
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DIRECTED DIFFUSION
INTERESTS and GRADIENTS
The named task description constitutes an INTEREST.
An interest is injected into the network at some (arbitrary) node in the network
Suppose it is SINK.
INTERESTS are diffused through the sensor network.
Example:
A task with a specified type and rect, a duration of 10 minutes and an
interval of 10 ms is initiated by a sensor node in the network.
* The interval parameter specifies an event data rate.
* Here the specified data rate is 100 events per second.
* The sink node records the task, the task state is purged from the node
after the time indicated by the duration attribute.
•
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DIRECTED DIFFUSION
* For each active task, SINK periodically broadcasts an interest message
to each of its neighbors.
* This initial interest contains the specified rect and duration attributes,
but contains a much larger interval attribute.
•
Every node maintains an interest cache.
* Each item in the cache corresponds to a distinct interest.
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DIRECTED DIFFUSION
An ENTRY in the interest cache has several fields:
* A TIMESTAMP field (timestamp of the last received matching
interest) and several GRADIENT fields up to one per neighbor.
* A GRADIENT is a relay link to a neighbor from which the interest
was received.
-*
-
Each GRADIENT contains
A data rate field
(requested by the specific neighbor)
A duration field
(approximate lifetime of the interest)
REMARK: Hence by utilizing interest and gradients, paths are
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established between sink and sources, i.e., sensors.
139
DIRECTED DIFFUSION
When a node receives an interest it checks to see of the interest exists
in the cache.
If no matching exists, the node creates a new entry.
If there exists an entry, but no gradient for the sender of the interest,
the node adds a gradient with the specified value.
It also updates the entry’s timestamp and duration fields.
Finally, if both an entry and gradient exist, the node simply
updates the timestamp and duration fields.
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Directed Diffusion
Features
Sink sends interest, i.e., task descriptor, to all sensor nodes.
 Interest is named by assigning attribute-value pairs.

source
source
sink
Interest Propagation
source
sink
Gradient Setup
sink
Data Delivery
Drawbacks
Cannot change interest unless a new interest is broadcast.
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LEACH
Low Energy Adaptive Clustering Hierarchy (LEACH)
(W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication
Protocol for Wireless Microsensor Networks,'' IEEE Proceedings of the Hawaii International
Conference on System Sciences, pp. 1-10, January, 2000.)
-
* LEACH is a clustering based protocol which minimizes energy dissipation
in sensor networks.
Idea:
* Randomly select sensor nodes as cluster heads, so the high energy
dissipation in communicating with the base station is spread to all sensor
nodes in the sensor network.
* Forming clusters is based on the received signal strength.
* Cluster heads can then be used kind of routers (relays) to the sink.
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LEACH
Two Phases: Set-up Phase and Steady-Phase
In Set-up Phase:
* Sensors may elect themselves to be a local cluster head at any time with
a certain probability. (Reason: to balance the energy dissipation)
* A sensor node chooses a random number between 0 and 1.
* If this random number is less than the threshold T(n), the sensor node
becomes a cluster-head.
T(n) = P / {1 – P[r mod (1/P)]} if n is element of G
where P
r
G
is the desired percentage to become a cluster head (e.g., 0.05)
is the current round
is the set of nodes that have not been a cluster head in the last 1/P
rounds.
* After the cluster heads are selected, the cluster heads advertise to all
sensor nodes in the network that they are the new cluster heads.
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Dynamic Clusters
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LEACH
Once the nodes receive the advertisement, they determine the cluster
that they want to belong based on the signal strength of the advertisement
from the cluster heads to the sensor nodes.
The nodes inform the appropriate cluster heads that they will be a member
of the cluster.
Afterwards the cluster heads assign the time on which the sensor nodes can
send data to them.
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LEACH
STEADY STATE PHASE:
Sensors begin to sense and transmit data to the cluster heads which
aggregate data from the nodes in their clusters.
After a certain period of time spent on the steady state,
the network goes into start-up phase again and enters another round of
selecting cluster heads.
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LEACH
 Optimum Number of Clusters ---????????
- too few: nodes far from cluster heads
– too many: many nodes send data to SINK.
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LEACH
 Achieves over a factor of 7 reduction in energy dissipation
compared to direct communication.
 The nodes die randomly and dynamic clustering increases
lifetime of the system.
 It is completely distributed and requires no global
knowledge of the network.
 It uses single hop routing where each node can transmit
directly to the cluster head and the sink.
 It is not applicable to networks deployed in large regions.
 Furthermore, the idea of dynamic clustering brings extra
overhead, e.g., head changes, advertisements etc. which
may diminish the gain in energy consumption.
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Other Protocols
1. Energy Aware Routing
R. Shah, J. Rabaey, “Energy Aware Routing for Low Energy Ad Hoc Sensor
Networks,” IEEE WCNC’02, Orlando, March 2002.
2. Rumor Routing
D. Braginsky, D. Estrin, “Rumor Routing Algorithm for Sensor Networks,”
ACM WSNA’02, Atlanta, October 2002.
3. Threshold sensitive Energy Efficient sensor Network (TEEN)
A. Manjeshwar, D.P. Agrawal, “TEEN: A Protocol for Enhanced Efficiency in
Wireless Sensor Networks,” IEEE WCNC’02, Orlando, March 2002.
4. Constrained Anisotropic Diffusion Routing (CADR)
M. Chu, H.Hausecker, F. Zhao, “Scalable Information-Driven Sensor Querying
and Routing for Ad Hoc Heterogeneous Sensor Networks,” International Journal
of High Performance Computing Applications, Vol. 16, No. 3, August 2002.
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Other Protocols
5. Power Efficient Gathering in Sensor Information Systems
(PEGASIS)
S. Lindsey, C.S. Raghavendra, “PEGASIS: Power Efficient Gathering in Sensor
Information Systems,” IEEE Aerospace Conference, Montana, March 2002.
6. Self Organizing Protocol
L. Subramanian, R.H. Katz, “An Architecture for Building Self Configurable
Systems,” IEEE/ACM Workshop on Mobile Ad Hoc Networking and
Computing, Boston, August 2000.
7. Geographic Adaptive Fidelity (GAF)
Y. Yu, J. Heideman, D. Estrin, “Geography-informed Energy Conservation for
Ad Hoc Routing,” ACM MobiCom’01, Rome, July 2001.
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Open Research Issues
• Store and Forward Technique
that combines data fusion and aggregation.
• Routing for Mobile Sensors
Investigate multi-hop routing techniques for
high mobility environments.
• Priority Routing
Design routing techniques that allow different priority
of data to be aggregated, fused, and relayed.
• 3D Routing
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TRANSPORT LAYER
(PRIOR KNOWLEDGE)
 END TO END RELIABILITY
 CONGESTION CONTROL
 TCP (Transmission Control Protocol) for Data Traffic
 UDP (User Datagram Protocol) for Real Time Traffic
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Transport Layer
Internet,
Satellite,
etc
Sink
Sink
End-to-end
communication
between a sensor
node and user
 End to end reliable
event transfer

User
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TRANSPORT LAYER
Related Work
 RMST (Reliable Multisegment Transport)
F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor Networks,”
In Proc. IEEE SNPA’03, May 2003, Anchorage, Alaska, USA





RMST is a transport layer protocol for directed diffusion.
RMST provides end-to-end data-packet transfer reliability.
RMST is a selective NACK-based protocol that can be
configured for in-network caching and repair.
There are two modes for RMST:
Caching Mode and Non-Caching Mode.
CACHING MODE:
A number of nodes along a reinforced path,
(path being used to convey the data to the sink by directed
diffusion), are assigned as RMST nodes.
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Reliable Multi-Segment Transport
(RMST)
Each RMST node caches the fragments
identified by FragNo of a flow identified by
RmstNo.
 Watchdog timers are maintained for each
flow. When a fragment is not received before
the timer expires, a negative
acknowledgement is sent backward in the
reinforced path.
 The first RMST node that has the required
fragment along the path retransmits the
fragment.
 Sink acts as the last RMST node. In noncaching mode, sink is the only RMST node.
 RMST relies on directed diffusion scheme for
recovery from the failed reinforced paths.

Sink
RMST Node
Source Node
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Related Work
PSFQ - Pump Slowly Fetch Quickly
– Slow injection of packets into the network
– Aggressive hop-by-hop recovery in case of packet losses
– “PUMP” performs controlled flooding and requires each intermediate
node to create and maintain a data cache to be used for local loss
recovery and in-sequence data delivery.
– Applicable only to strict sensor-sensor guaranteed delivery
– And for control and management end-to-end reliability for the
downlink from sink to sensors
– Does not address congestion control
C. Y. Wan, A. T. Campbell and L. Krishnamurthy, “PSFQ: A Reliable Transport Protocol for Wireless
Sensor Networks,” In Proc. ACM WSNA’02, September 2002, Atlanta, GA
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Pump Slowly Fetch Quickly
(PSFQ)
PSFQ comprises three functions:
* Message Relaying (PUMP operation),
* Relay initiated error recovery (FETCH operation) and
* Selective status reporting (REPORT operation).
 Every intermediate node maintains a data cache.
 A node that receives a packet checks its content against its local
cache, and discards any duplicates.
 If the received packet is new, the TTL field in the packet is
decremented.
 If the TTL field is higher than 0 after being decremented, and there
is no gap in the packet sequence numbers, the packet is scheduled to
be forwarded.
 The packets are delayed for a random period between Tmin and
Tmax, and then relayed.
 A node goes to FETCH mode once a sequence number gap is detected.
 The node in FETCH mode requests a retransmission from neighboring
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nodes.

Related Work
 Wireless TCP variants are NOT suitable for sensor
networks
– Different notion of end-to-end reliability
– Huge buffering requirements
– ACKing is energy draining
 BOTTOMLINE: Traditional end-to-end guaranteed
reliability (TCP solutions) cannot be applied here.
 New Reliability Notion is required!!!
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Event-to-Sink Reliability (ESRT)
O. B. Akan, I. F. Akyildiz and Y. Sankarasubramaniam,
to appear in IEEE Transactions on Networking, Fall 2004.
Also in Proc. of ACM MobiHoc’03, Annapolis, Maryland, June 2003.
Event Radius
Sink
Sensor nodes
 Sensor networks are event-driven
 Multiple correlated data flows from event to sink
 GOAL: To reliably detect/estimate event features
based on the collective reports of several sensor
nodes observing the event.
  Event-to-sink collective reliability notion
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Event-to-Sink Reliable Transport (ESRT)
ESRT is the first scheme that focuses on the end-to-end reliable event
transfer.
 The end-to-end event transfer reliability is controlled based on the
reporting frequencies of sensor nodes.
b
a
c
Sink
d
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End-to-end Reliable Event Transfer
event region
sensor coverage
r
sensor range
b
a
c
Sink
r
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163
Event-to-Sink Reliability
 Sink decides about event features every  time units (decision intervals)
 DEFINITION 1: Observed Event Reliability
ri is the number of data packets received in decision interval i at sink
 DEFINITION 2: Desired Event Reliability
R is the number of packets required for reliable event detection
(application specific and is known a-priori at the sink)
(If ri > R, then the event is reliably detected. Else, appropriate
actions must be taken to achieve R.)
 DEFINITION 3: Reporting Rate
f is the frequency of packet transmissions at a source node
TRANSPORT PROBLEM IN SENSOR NETWORKS:
To configure the reporting rate, f, of source nodes so as to achieve the
required event detection reliability, R, at the sink with minimum resource
utilization.
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r vs f relationship
 r shows initial linear increase with f until f = fmax
 For f > fmax , r drops due to congestion because the network is unable to handle the
increased injection of data packets
 This behavior is independent of the number of nodes n
 fmax decreases with increasing n (congestion occurs at lower reporting frequencies
with greater number of source nodes n)
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ESRT: Event-to-Sink
Reliable Transport
 OBJECTIVE:
Achieve reliable event detection with minimum energy
expenditure and congestion resolution.
 SALIENT FEATURES:
– Self-configuration – Adapts to random, dynamic network
topology
– Collective identification – Does not require individual node IDs
– Biased implementation – Graceful transfer of complexity to
the sink
 Sensor nodes need only two additional functions
– Implement a congestion detection mechanism
– Listen to sink broadcasts for frequency updates
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ESRT: Protocol Overview
 Determine reporting frequency f to achieve desired
reliability R with minimum resource utilization
 Source (Sensor nodes):
– Send data with reporting frequency f
f
– Monitor buffer level and notify
congestion to the sink
 Sink:
– Measures the observed event reliability ri at the end of decision
interval i
– Normalized reliability  i = ri / R
– Performs congestion decision based on the feedback from the
sources nodes (to determine f >< fmax).
– Update f based on i and f >< fmax (congestion) to achieve
desired event reliability R
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ESRT: Network States
State
Description
Condition
(NC,LR)
(No congestion, Low reliability)
f < fmax and  < 1 - 
(NC,HR)
(No congestion, High reliability)
f  fmax and  > 1+ 
(C,HR)
(Congestion, High reliability)
f < fmax and  > 1
(C,LR)
(Congestion, Low reliability)
f < fmax and   1
Optimal Operating Region
f < fmax and   [1- , 1+ ]
OOR
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ESRT:
Congestion Detection Mechanism
 ACK/NACK not suitable
 We use local buffer level monitoring in sensor
nodes
B
bk : Buffer fullness level at the
end of reporting interval k
Db : Buffer length increment
f
B : Buffer size
f
: reporting frequency
bk
bk-1
Db
 Mark Congestion Notification (CN ) field in packet if congested, i.e.,
bk + Db > B (the node infers that it will experience congestion in the next reporting interval)
Event
ID
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CN
(1 bit)
Time
Destination
Stamp
Payload
FEC
169
ESRT: Frequency Update
State
(NC,LR)
Frequency Update
fi+1 = fi / i
Comments
Multiplicative increase, achieve desired reliability asap
fi+1 = fi (i + 1) / 2i
Conservative decrease, no compromise on reliability
(C,HR)
fi+1 = fi / i
Aggressive decrease to state (NC,HR)
(C,LR)
fi+1 = fi i
(NC,HR)
OOR
fi+1 = fi
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Exponential decrease, relieve congestion asap
Unchanged
170
ESRT Performance
S0 = (NC,LR)
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S0 = (NC,HR)
171
ESRT Performance
S0 = (C,HR)
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172
Conclusions
 Sensor network paradigm necessitates the notion of
event-to-sink reliability
 Existing end-to-end guaranteed reliability solutions
lead to over-utilization of scarce sensor resources
 ESRT is a novel solution propose exclusively for
reliable event transport in sensor networks
– Tailored for sensor environments
– Biased implementation
– Energy conservation
– Collective identification, self-configuration
– ESRT can also address concurrent multiple events
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Open Research Issues
 Extend ESRT to address reliable transport of
concurrent multiple events in the sensor field.
 Explore possible other reliability metrics
– Total expected mean square distortion
– Minimum mean squared error estimation
 Develop unified transport layer protocols for sink-tosensors and bi-directional reliable transport in WSN
 Research to integrate WSN domain into NGWI (Next
Generation Wireless Internet)
– Adaptive Transport Protocols for WSN-Ad Hoc
environments
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Medium Access Control (MAC)


Multiple users need to access the limited
available communication resources.
MAC aims at providing fair and efficient
resource access
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Medium Access Control (MAC)
(Prior Knowledge)







ALOHA
Slotted ALOHA
Reserved ALOHA
CSMA (nonpersistant, p-persistant,1-persistant)
TDMA
FDMA
CDMA
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Aloha/Slotted Aloha
Aloha
collision
sender A
sender B
sender C
Slotted Aloha
t
collision
sender A
sender B
sender C
t
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FDMA (Frequency Division
Multiple Access)
Frequency
User n
…
User 2
User 1
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Time
178
FDMA Bandwidth Structure
1
2
3
4
…
n
Frequency
Total bandwidth
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FDMA Channel Allocation
User 1
User 2
…
User n
Mobile
Stations
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Frequency 1
Frequency 2
…
Frequency n
Base Station
180
TDMA (Time Division Multiple
Access)
…
User n
User 2
User 1
Frequency
Time
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TDMA Frame Structure
1
2
3
4
…
n
Time
Frame
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TDMA Frame Allocation
Time
1
…
User 2
User n
Mobile Stations
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Time 2
…
User 1
…
Time n
Base Station
183
CDMA (Code Division Multiple
Access )
.
User 1
..
User 2
User n
Frequency
Time
Code
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Medium Access Control (MAC)
Existing MAC protocols cannot be used for sensor
networks because sensor MACs must have inbuilt
Power management, mobility management and
failure recovery strategies
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Medium Access Control (MAC)
for Sensor Networks
 Self-Organizing Medium Access Control for Sensor


Networks (SMACS) and Eavesdrop and Register (EAR)
Hybrid TDMA-FDMA
CSMA based
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Medium Access Control (MAC)
SMACS and EAR
 Available bandwidth is far greater than the maximum data rate of sensors
Neighbor discovery and channel assignment combined
 Random wake up during the connection phase


In EAR mobile nodes are given full control of the connection process

Mobile nodes keep a record of neighbor nodes

EAR is transparent to SMACS
Shortcomings
Nodes belonging to different subnets might not be able to connect
 A mainly static network is assumed

W. Ye, J. Heidemann and D. Estrin, “An Energy Efficient MAC Protocol for Wireless
Sensor Networks,” In Proc. ACM MOBICOM ’01, pp. 221–235, Rome, Italy 2001
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CSMA Based
 Traffic in sensor networks is highly correlated,
dominantly periodic, variable.
Constant listening times are energy efficient
 Random delay avoids repeated collisions


Not suitable for delay-sensitive applications

Under higher load, RTS/CTS involves considerable
messaging overhead
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Motivation for Our Work
 WSN are characterized by dense deployment of sensor
nodes
 MAC Layer Challenges
– Limited power resources
– Need for a self-configurable, distributed protocol
– Data centric approach rather than per-node fairness
Exploit spatial correlation to reduce
transmissions in MAC layer !
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Collaborative Medium Access Based on Spatial
Correlation in Sensor Networks
M. C. Vuran and I. F. Akyildiz, December 2003.
1
2
3
4
5
S





Nodes ni observe variables Xi , i=1,2,3,4,5
Minimum of 5 transmissions are required
Due to correlation, assume X1=X2 and X3=X4
Only 3 transmissions needed!
Regulate medium access to decrease number of transmissions!
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Definitions
 Correlation region of node ni
– Region of radius r centered around node ni
 Correlation neighbors of node ni
– Nodes inside the correlation region of node ni
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Collaborative MAC Protocol
If a node ni transmits data then all its
correlation neighbors have redundant
information
Route-thru data has higher priority
over generated data
Filter out transmission of redundant data
and prioritize filtered data through the
network!
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Collaborative MAC Protocol
Two reasons for medium access;
 Source function:
Transmit event information
 Router function:
Forward packets from other
nodes in the multi-hop path to
the sink
 Two components
– Event MAC (E-MAC)
– Network MAC (N-MAC)
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Event MAC (E-MAC)
 Aims to filter out correlated sensor records
 First Contention Phase (FCS)
– Nodes contend using IEEE 802.11 structure for
the first time
 After a node ni captures the channel all the
correlation neighbors of ni
– Drop their packets
– Enter Suspicious Sleep State (SSS)
 Nodes enter FCS after a period of time to
maintain equal load-sharing
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Network MAC (N-MAC)
 Since correlation is filtered out by E-MAC,
route-thru packet has higher priority
 N-MAC prioritizes these packets during
medium access using
– Smaller backoff window size
– PIFS (<SIFS) during contention
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Performance
Both energy consumption and latency decreases when
spatial correlation is exploited
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Conclusions
 Spatial correlation in sensor networks is exploited in the MAC
layer
 MAC protocol collaboratively regulates medium access such
that redundant transmissions is suppressed
 Event MAC (E-MAC) filters out correlation whereas Network
MAC (N-MAC) prioritizes the route-thru packets
 Number of transmissions are reduced instead of number of
transmitted bits
 Collaborative Medium Access achieves low energy
consumption as well as improving event detection latency
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MEDIUM ACCESS CONTROL (MAC)
FURTHER RESEARCH NEEDS
 MAC for sensor networks must have inbuilt power



management, mobility management and failure recovery
strategies
Need for a self-configurable, distributed protocols
Data centric approach rather than per-node fairness
Develop MACs which differentiate Multimedia Traffic
 Exploit Spatial & Temporal Correlation
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Error Control
 Some sensor network applications like mobile tracking
require high data precision


Coding gain is generally expressed in terms of the additional
transmit power needed to obtain the same BER without coding
FEC is preferred over ARQ

Since power consumption is crucial, we must take into
account encoding and decoding energy expenditures

Coding is profitable only if the encoding and decoding
power consumption is less than the coding gain.
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ERROR CONTROL
RESEARCH NEEDS

Design of suitable FEC codes with minimal encoding
and relatively higher decoding complexities

Feasibility of ARQ schemes in multihop sensor networks
(hop by hop ARQ instead of end-to-end). This is needed for
reliable communications (data critical)

Adaptive/Hybrid FEC/ARQ schemes

Extension to Rayleigh/Rician fading conditions with mobile
nodes
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Optimal Packet Size for Wireless Sensor
Networks
Y. Sankarasubramaniam, I. F. Akyildiz, S. McLaughlin, ”Optimal Packet Size
for Wireless Sensor Networks”, IEEE SNPA, May 2003.


Determining the optimal packet size for sensor networks is
necessary to operate at high energy efficiencies.
The multihop wireless channel and energy consumption
characteristics are the two most important factors that
influence choice of packet size.
Header (2) Payload (<=73)
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201
PHYSICAL LAYER

New Channel Models (I/O/Underwater/Deep Space)

Explore Antennae Techniques
(e.g., Smart Antennaes)

Software Radios??

New Modulation Schemes

SYNCH Schemes

FEC Schemes on the Bit Level

New Data Encryption

Investigate UWB
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FINAL REMARKS
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Basic Research Needs
•
An Analytical Framework for Sensor Networks
 Find a Basic Generic Architecture and Protocol
Development which can be tailored to specific
applications.
• More theoretical investigations of the
Architecture and Protocol
developments
• Network Configuration and Planning Schemes
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FURTHER OPEN RESEARCH ISSUES
 Research to integrate WSN domain into NGWI (Next
Generation Wireless Internet)
e.g., interactions of Sensor and AdHoc Networks or Sensor
and Satellite or any other combinations…
 Explore the SENSOR/ACTOR NETWORKS
 Explore the SENSOR-ANTISENSOR NETWORKS
 SECURITY ISSUES
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Some Applications
• Clear Demonstration of Testbeds and Realistic Applications
• Not only data or audio but also video as well as integrated
traffic.
SOME OF OUR EFFORTS IN BWN LAB @ GaTech
•
•
•
•
•
MAN  for Meteorological Observations
SpINet  for Mars Surface
Airport Security  Sensors/Actors
Sensor Wars
Wide Area Multi-campus Sensor Network
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FURTHER CHALLENGES
Protocol Stack
• Follow the TCP/IP Stack, i.e., keep the
Strict Layer Approach ???
• Or Interleave the Layer functionalities???
• Cross Layer Optimization
• Standardization???
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Commercial Viability
of WSN Applications
 Within the next few years, distributed sensing and
computing will be everywhere, i.e., homes, offices,
factories, automobiles, shopping centers, supermarkets, farms, forests, rivers and lakes.
 Some of the immediate commercial applications of
wireless sensor networks are
–
–
–
–
–
–
–
–
Industrial automation (process control)
Defense (unattended sensors, real-time monitoring)
Utilities (automated meter reading),
Weather prediction
Security (environment, building etc.)
Building automation (HVAC controllers).
Disaster relief operations
Medical and health monitoring and instrumentation
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Commercial Viability
of WSN Applications
 XSILOGY Solutions is a company which provides wireless sensor
network solutions for various commercial applications such as
tank inventory management, stream distribution systems,
commercial buildings, environmental monitoring, homeland
defense etc.
http://www.xsilogy.com/home/main/index.html
 In-Q-Tel provides distributed data collection solutions with
sensor network deployment.
http://www.in-q-tel.com/tech/dd.html
 ENSCO Inc. invests in wireless sensor networks for
meteorological applications.
http://www.ensco.com/products/homeland/msis/msis_rnd.htm
 EMBER provides wireless sensor network solutions for
industrial automation, defense, and building automation.
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http://www.ember.com
209
Commercial Viability
of WSN Applications
 H900 Wireless SensorNet System(TM), the first commercially
available end-to-end, low-power, bi-directional, wireless mesh
networking system for commercial sensors and controls is
developed by the company called Sensicast Systems. The
company targets wide range of commercial applications from
energy to homeland security.
http://www.sensicast.com
 The Sensor-based Perimeter Security product is introduced by a
company called SOFLINX Corp. (a wireless sensor network
software company)
http://www.soflinx.com
 XYZ On A Chip: Integrated Wireless Sensor Networks for the
Control of the Indoor Environment In Buildings is another
commercial application project currently performed by Berkeley.
http://www.cbe.berkeley.edu/research/briefs-wirelessxyz.htm
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Commercial Viability
of WSN Applications
 The Crossbow wireless sensor products and its environmental
monitoring and other related industrial applications of such as
surveillance, bridges, structures, air quality/food quality,
industrial automation, process control are introduced.
http://www.xbow.com
 Japan's Omron Corp has two wireless sensor projects in the US
that it hopes to commercialize in the near future. Omron's
Hagoromo Wireless Web Sensor project consists of wireless
nodes equipped with various sensing abilities for providing
security for major cargo-shipping ports around the world.
http://www.omron.com
 Possible business opportunity with a big home improvement
store chain, Home Depot, with Intel and Berkeley using wireless
sensor networks
http://www.svbizink.com/otherfeatures/spotlight.asp?iid=314
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Commercial Viability
of WSN Applications
 Millennial Net builds wireless networks combining sensor
interface endpoints and routers with gateways for industrial
and building automation, security, and telemetry
http://www.millennial.net
 CSEM provides sensing and actuation solutions
http://www.csem.ch/fs/acuating.htm
 Dust Inc. develops the next-generation hardware and
software for wireless sensor networks
http://www.dust-inc.com
 Integration Associates designs sensors used in medical,
automotive, industrial, and military applications to costeffective designs for handheld consumer appliances, barcode
readers, and wireless computer input devices
http://www.integration.com
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Commercial Viability
of WSN Applications
 Melexis produces advanced integrated semiconductors, sensor
ICs, and programmable sensor IC systems.
http://www.melexis.com
 ZMD designs, manufactures and markets high performance,
low power mixed signal ASIC and ASSP solutions for wireless
and sensor integrated circuits.
http://www.zmd.biz
 Chipcon produces low-cost and low-power single-chip 2.4 GHz
ISM band transceiver design for sensors.
http://www.chipcon.com
 ZigBee Alliance develops a standard for wireless low-power,
low-rate devices.
http://www.zigbee.com
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