Sensor Networks

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Sensor Networks
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OUTLINE

• Introduction
• Applications
• Communication Architecture
• Protocol Stack
• Research Issues
• Another Challenges
• Conclusion

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Introduction - Definition

• Sensor Network
• Wireless network consisting of low cost, densely
  deployed (may be mobile) sensor nodes.
• Distribution is done in an ad hoc fashion.
• Close to event to be monitored.
• Usually have a limited amount of energy.
• Sensor Nodes
• Battery Power source, low power wireless
  communication.
• Match Box size form factor and power aware CPU.
• Small embedded OS (TinyOS) and program data
  memory is few KB.
• MEMS sensors (measures light, temp, seismic,
  acoustics, stress).

http//www.ensc.sfu.ca/ljilja/cnl/presentations/s
hameem/project816.pdf
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Introduction - Advantages

• Improved Signal-to-Noise Ration (SNR)
• combine sources with different spatial
  perspectives.
• Greater fault tolerance through redundancy
• Coverage of large area
• Multiple sensor types can improve performance
• Sensors close to object/phenomena of interest can
  overcome environmental noise effects
• Can be deployed in regions where infrastructure
  for replenishing energy is not available

http//www.ece.mcgill.ca/coates/publications/shor
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Introduction - Difference

• What makes a wireless sensor network unique?
• Cellular networks, ad-hoc networks are designed
  to
• Optimize QoS Provide high bandwidth
• Provide good throughput/delay characteristics
  under medium/high mobility conditions
• Energy consumption of secondary importance
• Sensor networks
• Many nodes, autonomous operation
• Generally stationary devices (or low mobility)
• Traffic periodic or intermittent, low data rate,
  frequently uni-directional
• Energy management is critical
• Sensing application cannot be ignored !

http//www.ece.mcgill.ca/coates/publications/shor
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Introduction - Uniqueness

• Data-centric networks
• Identity/address of a sensor node is not critical
  its data is the important aspect
• Application specific
• Intermediate nodes can perform data aggregation
  or in-network processing
• Network operation driven by global objectives
  not by individual data transfers.
• Resource constraints call for more tightly
  integrated layers

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Sensors and Wireless Radio
Types of sensors - Pressure, - Temperature -
Light - Biological - Chemical - Strain,
fatigue - Tilt
Sensors

• Capable of surviving harsh environments (heat,
  humidity, corrosion, pollution, radiation, etc).
• Could be deployed in large numbers.

Wireless Radio
From CDMC http//www.ececs.uc.edu/dpa
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Applications

• Medical monitoring (e.g., heart rate, glucose
  level) and localized drug delivery
• Monitoring structural integrity in buildings
• Tracking vehicles, people, chemical agents,
  pollution, weather phenomena
• Seismic monitoring, contaminant/pollution
  monitoring
• Precision agriculture
• The movie Twister is an example of sensor
  network.

http//www.ece.mcgill.ca/coates/publications/shor
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Ecophysiological Modeling Using Sensor Array Data

• Spatially and temporally dense microclimate data
  will allow
• significant advancements in modeling plant
  production

http//cens.ucla.edu/Estrin/
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Habitat and Environmental Sensing
UC James Reserve Habitat Sensing Testbed
Soil microclimate and chemical sensors,
root/fungi imaging systems (mini-rhizotron)
James Reserve and Hall Canyon Research Natural
Area
http//cens.ucla.edu/Estrin/
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Error Resilient Contaminant Monitoring

• Sensor network error resiliency in complex media
  (air-water-soil)
• Working in the context of a real problem in
  Palmdale, CA
• partnering with LA County Sanitation District
• Working in concert with Sensor Group on broadly
  applicable sensors, scalable sensors nitrate and
  other ionic species
• microsensors matching COTS perfomance
• Real-time analysis instead of logging
• model calibration, forecasting

http//cens.ucla.edu/Estrin/
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Contaminant Transport Futures

• Larger scale, multimedia problems
• Linking remote and in situ sensing over multiple
  scales
• Management, visualization, exploration of
  massive, heterogeneous data streams
• NSF CLEANER Initiative
• Challenges
• Multimedia, Multiscale problems (time and space)
• Multidisciplinary
• Management, visualization, exploration of
  massive, heterogeneous data streams

http//cens.ucla.edu/Estrin/
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Seismic Applications

• Multi-Hopped Radio Linked Array features
• Time synchronization
• Network event detect
• Sequenced event transmission
• Deployments planned for UCLA campus and the San
  Andreas Fault (100m-10 km)
• Easily reconfigurable
• Worldwide application
• Factor Building site
• 72 channels of 24-bit data
• 500 samples per second continuous data recording
• Internet accessible real time data monitoring
• Observation of 4 strong earthquakes, including
  Alaska Japan

Fiberoptic link
Radio link
http//cens.ucla.edu/Estrin/
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Science Application Systems

• Biology/Biocomplexity
• Microclimate monitoring
• Triggered image capture
• Marine microorganisms
• Detection of a harmful alga
• Experimental testbed w/autonously adapting sensor
  location

Ecosystems, Biocomplexity
Marine Microorganisms
http//cens.ucla.edu/Estrin/
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Architectural Decisions

• Small size, rugged design, energy-efficient
  operation and low cost
• Limited transmission range -gt multi-hop network
• Communication is energy-expensive
• 3 J energy to transmit 1Kb over 100m equivalent
  to 300 million instructions for a 100 Mops
  processor
• Rough energy rule 1 bit 1000 instructions
• Local processing of information to limit amount
  of data that must be exchanged

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Strategies

• Cooperative signal processing
• Collaboration enhances energy efficiency
• Substantial redundancy in data from
  closely-spaced sensors
• Exploit redundancy of hardware elements
• Deploy higher density of nodes than necessary
• Adjust duty cycle so neighboring nodes are not
  always active
• Adaptive signal processing
• Maintain balance between energy, accuracy and
  rapidity of results
• Hierarchical architecture
• Higher energy, more powerful devices act as
  cluster heads
• Cluster heads control operation of a set of more
  limited devices

http//www.ece.mcgill.ca/coates/publications/shor
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Communication Architecture
Sensor nodes are usually scattered in a sensor
field. Each of these scattered sensor nodes has
the capabilities to collect and route data back
to the sink by a multi-hop infrastructureless
architecture through the sink.
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Communication Architecture (Cont.)

• Design Factor
• Fault Tolerance
• Scalability
• Production Costs
• Hardware Constraints
• Sensor Network Topology
• Environment
• Transmission Media
• Power Consumption

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Communication Architecture (Cont.)

• Design Factor - Fault Tolerance
• The failure of sensor nodes should not affect the
  overall task of the sensor network
• where the failure rate of sensor node k
• t time period
• Reference 2

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Communication Architecture (Cont.)

• Design Factor - Scalability
• The density can range from few sensor nodes to
  few hundred sensor nodes in a region, which can
  be less than 10 m in diameter.
• Reference3

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Communication Architecture (Cont.)

• Design Factor - Production Costs
• The cost of each sensor node has to be kept low
• The state-of-the-art technology allow a Bluetooth
  radio system to be less than US10.
• The cost of a sensor node should be much less
  than US1 in order for the sensor network to be
  feasible.

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Communication Architecture (Cont.)

• Design Factor - Hardware Constraints

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Communication Architecture (Cont.)

• Design Factor - Sensor Network Topology
• Predeployment and deployment phase
• Post-deployment phase
• Redeployment of additional nodes phase

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Communication Architecture (Cont.)

• Design Factor - Environment
• Sensor nodes usually work unattended in remote
  geographic areas.
• At the bottom of an ocean
• In a biologically or chemically contaminated
  field
• In a battlefield
• In a home or large building

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Communication Architecture (Cont.)

• Design Factor (Transmission Media)
• RF circuit
• µAMPS, WINS
• Infrared
• Optical
• Smart Dust mote

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Communication Architecture (Cont.)

• Design Factor - Power Consumption
• Limited power(lt0.5Ah, 1.2V)
• Power Consumption
• Sensing
• Communication
• Data processing

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Protocol Stack
This protocol stack combines power and routing
awareness, integrates data with networking
protocols, communicates power efficiently through
the wireless medium, and promotes cooperative
efforts of sensor nodes.
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Protocol Stack (cont.)

• Physical Layer
• 915MHz (ISM) band
• Ultra wideband (UWB)
• Impulse radio (IR)
• Open research issue
• Modulation schemes
• Strategies to overcome signal propagation effect
• Hardware design

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Protocol Stack (cont.)

• Data Link Layer (Medium Access Control)
• Existing MAC protocols cannot be used
• SMACS (Self-Organizing Medium Access Control for
  sensor Networks) and the EAR (Eaves-drop-And-Regis
  ter) Algorithm
• CSMA-Based Medium Access
• Hybrid TDMA/FDMA-Based

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Protocol Stack (cont.)

• Data Link Layer (Power Saving Modes of Operation)
• Sensor nodes communicate using short data
  packets. The shorter the packets, the more the
  dominance of startup energy.
• Energy-efficient only if the time spent in that
  mode is greater than a certain threshold.
• Number of modes can be characterized by its power
  consumption and latency overhead.

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Protocol Stack (cont.)

• Data Link Layer (Error Control)
• FEC (Forward Error Correction)
• ARQ (Automatic Repeat Quest)

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Protocol Stack (cont.)

• Data Link Layer (Open Research Issues)
• MAC for mobile sensor networks
• Determination of lower bounds on the energy
  required for sensor network self-organization
• Error control coding schemes
• Power-saving modes of operation

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Protocol Stack (cont.)

• Network Layer
• Power efficiency is always an important
  consideration.
• Sensor networks are mostly data-centric.
• Data aggregation is useful only when it does not
  hinder the collaborative effort of the sensor
  nodes.
• An ideal sensor network has attribute-based
  addressing and location awareness.

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Protocol Stack (cont.)

• Network Layer
• Maximum PA route
• Minimum energy (ME) route
• Minimum hop (MH) route
• Maximum minimum PA node route

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Protocol Stack (cont.)

1. The power efficiency of the routes
2. an example of data aggregation
3. the SPIN protocol 15
4. an example of directed diffusion 5

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Protocol Stack (cont.)

• Network Layer
• SMECN (Small Minimum Energy Communication
  Network)
• Flooding
• Gossiping
• Sensor Protocols for information via negotiation
• Sequential assignment routing
• Low-Energy Adaptive Clustering Hierarchy
• Directed diffusion

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Protocol Stack (cont.)

• Network Layer (open research issues)

An overview of the protocols proposed for sensor
networks is given in above table. These protocols
need to be improved or new protocols developed to
address higher topology changes and higher
scalability.
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Protocol Stack (cont.)

• Transport Layer
• TCP with its current transmission window
  mechanisms does match the extreme characteristics
  of the sensor network.
• TCP connections are enabled at sink nodes.
• Communication between the sink and sensor nodes
  maybe purely by UDP-type protocols, because each
  sensor node has limited memory

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Protocol Stack (cont.)

• Transport Layer (open research issues)
• UDP-type protocol are used in the sensor network
• Traditional TCP/UDP protocols are used in the
  internet or satellite network

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Protocol Stack (cont.)

• Application Layer
• SMP (Sensor Management Protocol)
• TADAP (Task Assignment and Data Advertisement
  Protocol)
• SQDDP (Sensor Query and Data Dissemination
  Protocol)

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Operational Challenges

• Adaptive, self-configuring systems that respond
  to an unpredictable environment
• Data processing inside the network
• Perform computation where data is measured to
  extract information (compress)
• Important to reduce communication overhead
• Distributed control and signal processing
• Untethered, unattended large-scale systems
• Low-duty cycle design
• Preserve energy by minimizing communication

http//www.ece.mcgill.ca/coates/publications/shor
tcourse-part1.pdf
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Networking Challengesand Design Principles

• Localization
• Synchronization
• Coverage
• Device management and scheduling
• Connectivity (topology) maintenance
• Routing
• Reliable Data Transport
• Seciruty

http//www.ece.mcgill.ca/coates/publications/shor
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Localization and Synchronization

• Accurate localization and synchronization
• Need to determine where events occur in space
• Critical for fusion of sensor measurements
• Important for coordination of communication
• GPS provides one solution
• Not always available
• Can be too costly, bulky

http//www.ece.mcgill.ca/coates/publications/shor
tcourse-part4.pdf
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Localization

• Most techniques use recursive
• trilateration/multilateration
• Some nodes are assumed to know their position
• (through GPS for instance).
• These act as beacons by periodically transmitting
• their position
• Nodes hearing these beacons use them to
• estimate their position
• May be an iterative process

http//www.ece.mcgill.ca/coates/publications/shor
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Localization (cont.)

• Fine-grained (timing/signal strength) or
  coarsegrained (proximity)
• Fine-grained
• Time of flight
• Signal strength
• Signal pattern matching
• Directionality
• Coarse-grained
• Centroid of beacons

http//www.ece.mcgill.ca/coates/publications/shor
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Localization Challenges

• How many beacons are needed?
• Provide good coverage, avoid excessive
  interference
• Where should beacons be placed?
• Incremental beacon placement
• Place new beacon in region of maximum (average)
  localization error
• Controlling beacons
• Scheduling operation to preserve lifetime

http//www.ece.mcgill.ca/coates/publications/shor
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Synchronization

• Metrics
• Precision
• Peer dispersion or with reference to external
  standard
• Lifetime
• Ranging from persistent to instantaneous
• Scope and Availability
• Geographic span and completeness of coverage
• Efficiency
• Time and energy expenditure
• Cost and form factor

http//www.ece.mcgill.ca/coates/publications/shor
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Sensor Network Synchronization

• Fine-grained, persistent timing is important in
  sensor networks
• Data fusion
• detect/estimate the same event
• Local data processing
• Eliminate duplicates through timestamping
• Existing network timing protocols inadequate
• Often conservative in use of bandwidth (e.g.,
  NTP) but neglect cost of listening
• Heterogeneity of hardware
• Multiple methods of synchronization should be
  available
• Algorithms should be tunable (precision vs.
  energy)

http//www.ece.mcgill.ca/coates/publications/shor
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Coverage

• Measures of coverage
• Area coverage fraction of area covered by
  sensors
• Detectability probability sensors detect event
• Node coverage fraction of sensors covered by
  other sensors
• Maximal breach path intruder is maximum
  distance from sensors over entire path
• Maximum exposure path minimum distance from
  sensors over entire path
• K-coverage entire sensing region must be within
  distance K of a sensor

http//www.ece.mcgill.ca/coates/publications/shor
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Coverage - Maintenance

• Where to position sensors initially?
• Where to add new sensors?
• Where to move sensors?
• When to schedule sensors? (overlapping sensors
  should not operate at same time)
• Locate coverage holes, breach areas, best areas.

http//www.ece.mcgill.ca/coates/publications/shor
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Coverage Node Scheduling

• Turn off redundant nodes
• Extend system lifetime
• Maintain reliability and coverage/detectability
• Rotate active sensor set
• Optimal Schedules
• Possible to find optimal schedules, but
• Requires global knowledge -gt much communication
• Not robust to changes of network state
• Distributed approaches
• Topology control select active routers
• Sensor mode selection select active sensors

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Topology Control

• Goals
• Ensure that devices are activated so that all
  sensors can route data to sink(s)
• Allow non-selected devices to sleep
• Rotate active routers to balance energy
• Ensure robustness sensor losses do not
  disconnect the network
• Example protocols
• Geographic Adaptive Fidelity (GAF)
• Span
• Adaptive Self-Configuring Sensor Network
  Topologies (ASCENT)
• Sparse Topology and Energy Management (STEM)

http//www.ece.mcgill.ca/coates/publications/shor
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Topology Control Examples

• Geographic Adaptive Fidelity (GAF)
• Overlay virtual grid over network
• Activate only one device per cell in grid
• STEM
• Reactively turn on routers when sensors need to
  send data
• Paging channel used to wake up neighbours
• A wake-up message tone or beacon

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Sensor Mode Selection

• Goal
• Select sensing modes (active/inactive, frequency,
  resolution) to ensure network objectives achieved
• Example criteria K-coverage, minimum tracking
  accuracy, maximum missed detection probability
• Allow non-selected sensors to sleep
• Example Protocols
• Probing Environment and Adaptive Sleeping (PEAS)
• Node Self Scheduling Scheme (NSSS)
• Coverage Configuration Protocol (CCP)

http//www.ece.mcgill.ca/coates/publications/shor
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Routing

• Routing Challenges
• Node deployment
• Time-critical applications
• Node/link heterogeneity
• Fault tolerance
• Scalability
• Network dynamics environment and nodes
• Connectivity and coverage

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Routing - Categories

• Data-centric
• Eevent-driven or query-driven
• Sensor Protocols for Information via Negotiation
  (SPIN), Directed Diffusion, GRAdient Broadcast
  (GRAB), Rumour Routing
• Hierarchical (Clustering)
• LEACH, PEGASIS, TEEN and APTEEN, MECN, SOP,
  Sensor Aggregates Routing ...

http//www.ece.mcgill.ca/coates/publications/shor
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Routing Categories (cont.)

• Location-based (Geographic)
• Geographic Adaptive Fidelity (GAF), Geographic
  and Energy-aware Routing (GEAR), Greedy Other
  Adaptive Face Routing (GOAFR)
• QoS-aware
• See pros and cons of some protocols in
  http//www.ensc.sfu.ca/ljilja/cnl/presentations/s
  hameem/project816.pdf.

http//www.ece.mcgill.ca/coates/publications/shor
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Reliable Data Transport

• Transport layer design is difficult because of
  application-specific nature of sensor networks
• Networking layers tend to become fused
  (particularly transport and application)
• Goal design customizable transport layer
• Provide the primitives for reliable transport
• Allow the network designer the flexibility to use
  them according to the application needs.
• Example PSFQ (Pump slowly, Fetch Quickly)

http//www.ece.mcgill.ca/coates/publications/shor
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Security architecture
http//www.i2r.a-star.edu.sg/icsd/SecureSensor/pap
ers/security-map.pdf
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Sensor security categories
http//www.i2r.a-star.edu.sg/icsd/SecureSensor/pap
ers/security-map.pdf
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Conclusion

• Interesting Areas of Research
• Sensor network still at an early stage in terms
  of technology
• Currently there is still nothing in use in the
  real world
• Needs improved or new routing protocols
• The protocols present today have their own set of
  problems
• No work done on contention issues or for high
  network traffic
• Currently most researchers claim that although
  important the present networks thought about does
  not have high network traffic
• Most protocols deal with energy efficiency
• There is significant work that can be done with
  robustness, scalability
• Most results are empirical very little analytical
  work done
• General perception is that it is hard to do such
  work
• Better ways to categorize the model

http//www.ensc.sfu.ca/ljilja/cnl/presentations/s
hameem/project816.pdf
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Current Research Projects