SENSOR NETWORKS

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1. INTRODUCTIONSENSOR NETWORKS ARCHITECTURE

• Several thousand nodes
• Nodes are tens of feet of each other
• Densities as high as 20 nodes/m3

• 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

• 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

• Small
• Low power
• Low bit rate
• High density
• Low cost (dispensable)
• Autonomous
• Adaptive

SENSING UNIT
PROCESSING UNIT
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Example MICA MotesBWN Lab _at_ 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
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Examples for Sensor Nodes
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Examples for Sensor Nodes
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Zylogs 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
• Seismic
• Low sampling rate magnetic
• Thermal
• Visual
• Infrared
• Acoustic
• Radar. 

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Sensor Networks Applications

• Sensors can monitor ambient conditions including
• 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
• Continuous sensing
• Event detection
• Event identification
• Location sensing
• Local control of actuators

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Sensor Networks Applications

• 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)
• 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/ITOSpe
  eches/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 StrikeProjecting Precise and Persistent
  Offensive Power
• Sea ShieldProjecting Global Defensive Assurance
  
• Sea BasingProjecting 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/proceeding
  s.html

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SAD SEAL Attack Detection Anti-Submarine
Warfare
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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

• 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 (Contd)
Server
Room 2
Room 1
Scanner and phone with embedded sensor nodes.
Computers with embedded sensor nodes.
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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
• - 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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Fault Tolerance(Reliability)

• Sensor nodes may fail or be blocked due to lack
  of power
• have physical damage, or environmental
  interference.
• 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) (Ctnd)

• Reliability (Fault Tolerance) of a sensor node
  is modeled
• 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) (Ctnd)

• EXAMPLE
• Suppose lambda 3.5 10-3 t10sec
  ? R 0.97
• 
  t20sec ? R 0.93
• 
  t30sec ? R 0.9
• 
  t50sec ? R0.84

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Fault Tolerance (Reliability) (Ctnd)

• Reliability (Fault Tolerance) of a broadcast
  range with
• N sensor nodes is calculated from

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Fault Tolerance (Reliability) (Ctnd)

• 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

Drop t and substitute f (1 R). o.99 1 fN
? N 2
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Fault Tolerance(Reliability) (Ctnd)
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) (Ctnd)
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 (Ctnd)
The Sensor Node Density i.e., the number of
expected nodes within the radio range
R 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 (Ctnd)
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., R20m ? 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. dhopgtdnei
Sink node
Radio Range R
dnei
dhop
Sensor nodes
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Scalability (Ctnd)
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 )/(5050) 2 pi
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Scalability (Contd)
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

• 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

• Small
• Low power
• Low bit rate
• High density
• Low cost (dispensable)
• Autonomous
• Adaptive

SENSING UNIT
PROCESSING UNIT
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E. Sensor Network Topology

• Several thousand nodes
• Nodes are tens of feet of each other
• Densities as high as 20 nodes/m3

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Sensor Network Topology (Ctnd)

• Topology maintenance and change
• Pre-deployment and Deployment Phase
• Post Deployment Phase
• Re-Deployment of Additional Nodes

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Sensor Network Topology (Ctnd)

• Pre-deployment and Deployment Phase
• Sensor networks can be deployed by
• 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 (Ctnd)

• Initial deployment schemes must
• 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 (Ctnd)

• POST-DEPLOYMENT PHASE
• After deployment, topology changes are due to
  change in sensor nodes
• 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
• 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

• 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 (Ctnd)

• Power consumption in a sensor network can be
  divided
• into three domains

• Communication
• Data Processing
• Sensing

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Power Consumption (Ctnd)
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 (Ctnd)

• Power consumption for data communication (Pc)

Pc Pte Pre P0

• Pte/re is the power consumed in the
  transmitter/receiver
• electronics (including the start-up
  power)
• P0 is the output transmit power

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Power Consumption in Data Communication (PC)
(Detailed Formula)
where PT is power consumed by transmitter PR
is power consumed by receiver Pout is output
power of transmitter Ton is time for transmitter
on Ron is time for receiver on Tst is start-up
time for transmitter Rst is start-up time for
receiver
NT is the number of times transmitter is
switched on per unit time NR is the number of
times receiver is switched on per unit time
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Power Consumption in Communication (Ctnd)

• 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 (Ctnd)

• 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 (Ctnd)

• 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.
• Local data processing is crucial in minimizing
• power consumption in a multi-hop network

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Power Consumption in Data Processing (Ctnd)

• 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)
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 (Ctnd)(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 (Ctnd) (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 others broadcast.
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Power Consumption (Ctnd) (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 0.05 J E_pair E_Tx
E_Rx 0.1025J E_T 20 . E_pair 20.
0.1025J 2.050 J
E_Rx (k,D) E_elec k
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Power Consumption in Sensing (Ctnd)

• Depends on
• Application
• Nature of sensing Sporadic or Constant
• Detection complexity
• Ambient noise levels

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Sensor Networks Communication Architecture
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Sensor Networks Communication Architecture

• 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 CANT 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
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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-gt
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 ltgt PA
location in not in RECT
tmin lt time lt tmax task t
amplitude ltgt 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.

Sink
Query Sensor nodes that read gt70oF 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.

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Query Sensor nodes that read gt70oF temperature
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Location Awareness (Attribute Based Naming)

• Query an Attribute
• of the sensor field

Region A
Sink
Region C
Region B
Query Temperatures read by the nodes in Region A

• Important for broadcasting,
• multicasting, geocasting and anycasting

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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) -gt Bellman/Ford Algorithm OSPF (Open
Shortest Path Algorithm) ? Dikstra Algorithm

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Routing Algorithms Constraints Regarding Power
Efficiency (Energy Efficient Routing)
E (PA1)
F (PA4)

• Maximum power available (PA) route
• Minimum hop route
• Minimum energy route
• Maximum minimum PA node
• route (Route along which the
• minimum PA is larger than the
• minimum PAs of the other routes
• is preferred, e.g., Route 3 is the
• most efficient Route 1 is the
• second).

D (PA3)
T
Sink
A (PA2)
B (PA2)
C (PA2)
Route 1 Sink-A-B-T (PA4) Route 2 Sink-A-B-C-T
(PA6) Route 3 Sink-D-T (PA3) Route 4
Sink-E-F-T (PA5)
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Why cant 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

99
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

100
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.

Sink
Query Nodes that read gt70oF temperature
101
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

102
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
Query Nodes that read gt70oF temperature
103
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
104
Polygonal (Spatial) Addressing Location
Awareness

Region A
Sink
Region C
Region B
Query Temperatures read by the nodes in Region A

• Important for broadcasting,
• multicasting, geocasting and anycasting

105
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 Network Protocol), APTEEN, PEGASIS,
Energy Aware Scheme 3. Location Based MECN,
SMECN (Small Minimum Energy Com Netw), GAF
(Geographic Adaptive Fidelity), GEAR
106
Conventional ApproachFLOODING
Broadcast data to all neighbor nodes
107
ROUTING ALGORITHMS Gossiping
GOSSIPING Sends data to one randomly selected
neighbor. Example
108
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
109
Problems
Data Overlap
r

• Resource Blindness
• No knowledge about the available power of
  resources

110
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

111
The Optimum Protocol

• Ideal
• Shortest-path routes
• Avoids overlap
• Minimum energy
• Need global topology information

112
Ideal Dissemination

• No implosion and no overlap
• Disseminate in shortest possible time

113
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 MobiCom99, 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

114
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.

115
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.
1. ADV 2. REQ 3. DATA
2.
3.
116
SPIN
Meta-Data ltgt Data Naming
ADV
A
B

• ADV- advertise/name data
• REQ- request specific data
• DATA- requested data

REQ
A
B
DATA
A
B
117
SPIN
118
EXAMPLE Sensor A sends meta-data to neighbor
A
ADV
B
119
Sensor B requests data from Sensor A
A
B
REQ
120
Sensor A sends data to Sensor B
A
DATA
B
121
Sensor B aggregates data and sends meta-data for
A and B to neighbors
A
ADV
ADV
B
ADV
ADV
ADV
ADV
122
All but 1 neighbor request data
A
REQ
REQ
B
REQ
REQ
REQ
123
Sensor B sends requested data to neighbors
A
DATA
DATA
B
DATA
DATA
DATA
124
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.

125
SPIN-2

• Spin-2
• SPIN-1 low-energy threshold
• Modifies behavior based on current energy
  resources

126
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

127
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
• Sleeping -- Same as negotiating, except that
  nodes stop sending messages when energy is low.
  SPIN-2

Zzz...
128
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

129
ROUTING ALGORITHM (DIRECTED DIFFUSION)

• (C. Intanagonwiwat, R. Gowindan and D. Estrin,
  Directed Diffusion A Scalable and Robust
• Communication Paradigm for Sensor Networks,
  Proc. ACM MobiCom00, 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.

130
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

131
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.
132
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.

133
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)
134
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.
135
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 012040
(event generation time)
136
Directed Diffusion

Source
Sink
137
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.

138
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.

139
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
• established between sink and sources,
  i.e., sensors.

140
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
entrys timestamp and duration fields. Finally,
if both an entry and gradient exist, the node
simply updates the timestamp and duration
fields.
141
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
sink
Interest Propagation
Gradient Setup
Data Delivery
Drawbacks
Cannot change interest unless a new interest is
broadcast.
142
Directed Diffusion vs SPIN

• On-Demand Data Query is different.
• In DD ? Sink queries sensors if a specific data
  is available by flooding some tasks.
• In SPIN ? Sensors advertise the availability of
  data allowing sinks to query that data.

143
Directed DiffusionAdvantages and Disadvantages

• DD is data centric ? no need for a node
  addressing mechanism.
• Each node can do aggregation, caching and
  sensing.
• DD is energy efficient since it is on demand
  and no need to maintain gobal network topology.
• Not generally applicable since it is based on a
  query driven data delivery model.
• For applications needing continuous data
  delivery (e.g., environmental monitoring) ? DD is
  not a good choice.
• Naming schemes are application dependent and
  each time must be defined a-priori.
• Matching process for data and queries cause
  some overhead at sensors.

144
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.

145
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 Pr mod (1/P) if n
  is element of G
• where P is the desired percentage to become a
  cluster head (e.g., 0.05)
• r is the current round
• G 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.
• Each node accesses the network through the
  cluster head that requires
• minimum energy to reach.

146
Dynamic Clusters
147
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.
148
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.
149
LEACH

• Optimum Number of Clusters ---????????
• - too few nodes far from cluster heads
• too many many nodes send data to SINK.

150
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.

151
Other Protocols
1. Energy Aware Routing R. Shah, J. Rabaey,
Energy Aware Routing for Low Energy Ad Hoc
Sensor Networks, IEEE WCNC02, Orlando,
March 2002. 2. Rumor Routing D. Braginsky,
D. Estrin, Rumor Routing Algorithm for Sensor
Networks, ACM WSNA02, 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 WCNC02, 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.
152
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
MobiCom01, Rome, July 2001.
153
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

154
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

155
Transport Layer

• End-to-end communication between a sensor node
  and user
• End to end reliable event transfer

156
TRANSPORT LAYERRelated Work

• RMST (Reliable Multisegment Transport)
• F. Stann and J. Heidemann, RMST Reliable
  Data Transport in Sensor Networks,
• In Proc. IEEE SNPA03, 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.

157
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 non-caching
  mode, sink is the only RMST node.
• RMST relies on directed diffusion scheme for
  recovery from the failed reinforced paths.

RMST Node
Source Node
158
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 WSNA02,
September 2002, Atlanta, GA
159
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.