Wireless SensornetworksConcepts, Protocolls and Applications
Hon.-Prof. Dr. rer. nat. Peter Langendörferleader of the research group of sensor nets
telefon: 0335 5625 350fax: 0335 5625 671
e-mail: langendoerfer [ at ] ihp-microelectronics.comweb: www.tu-cottbus.de/systeme
Chapter 1Introduction, Applications and Challenges
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general information
• lecture dates– exercise each time after lecture (starts on demand)
• exam at the beginning of the vacations by exam or orally • certificate by proof of their participation in lecture
(list of participants: at least 5 participated)• documents for lecture and exercise on chair website• for rescheduling information or other announcements
will be publish on chair website and/or by email(please register in LEHVIS system)
• www.tu-cottbus.de/systeme
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Literatur und Quellen
• Protocols and Architectures for Wireless Sensor NetworksProf. Holger Karl; Andreas Willig, Wiley, ISBN 0-470-09510-5
• Distributed Sensor NetworksS. Sitharama Iyengar and Richard. R. Brooks, Chapman & Hall/CRC, ISBN 1-58488-383-9
• Wireless Sensor Networks, Architectures and ProtocolsEdgar H. Callaway, Jr, Auerbach Publications ISBN 0-8493-1823-8
• Sensor Technology HandbookJohn S. Wilson, Newnes ISBN 0-7506-7729-5
• Ad Hoc Wireless NetworksMohamed Ilyas, CRC Press, ISBN 0-8493-1332-5
• Präsentationen aus dem WWRF• Folien des Kollegen Karl aus Paderborn
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lecture overview
• Introduction, Applications and Challenges• Single Node Architectures • Physical Layer• MAC Protocols• LLC Protocols• Routing Protocols• Network Architectures• DSN Architectures• Power Management
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infrastructure-based wireless networks
• Typical wireless network: Based on infrastructure– e.g., GSM, UMTS, …– base stations connected to a wired backbone network– mobile entities communicate wirelessly to these base stations– traffic between different mobile entities is relayed by base stations and
wired backbone– mobility is supported by switching from one base station to another– backbone infrastructure required for administrative tasks
IP backbone
ServerRouter
Gateways
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infrastructure-based wireless networks (2)
• Which are the limits ?• What if …
– … no infrastructure is available ?• e.g., in disaster areas
– … it is too expensive/inconvenient to set up ?• e.g., in remote, large construction sites
– … there is no time to set it up ?• e.g., in military operations
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possible applications forinfrastructure-free networks
• military networking: tanks, soldiers, …• finding out empty parking lots in a city, without asking a
server• search-and-rescue in an avalanche• personal area networking (watch, glasses, PDA, medical
appliance, …)
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• factory floor automation• disaster recovery
• car-to-car communication
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sensor equipment
tiny 1cm³ Particleincludes sensors, battery,CPU, communication
source: www.teco.edu
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sensor nodes
UC Berkeley: COTS Dust
UC Berkeley: COTS Dust UC Berkeley: Smart Dust
UCLA: WINSRockwell: WINS
JPL: Sensor Webs
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Sensor Node
Processor Radio Frontend
Sensor Interface
Antenna
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Sensorknoten
Processor Radio Frontend
Sensor Internface
Antenne
Power Mgmt.
Power Supply
Microcontroller Memory Hardware-Accelerator
Analogue Frontend
Baseband Base band
Sensor
Communication Interface
I/O
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IHP Sensor nodes
Power Mgmt.
Power Supply
Microcontroller Speicher Hardware-Beschleuniger
Analoges Frontend
Baseband Basisband
Sensor
Kommunikationsschnittstelle
Ein-/Ausgabe
Memory250KB
SPI Baseband 868MHz
HW AccECC, AES
IPMS430
On board comm.
Power Mgmt.
Power Supply
Microcontroller Speicher Hardware-Beschleuniger
Analoges Frontend
Baseband Basisband
Sensor
Kommunikationsschnittstelle
Ein-/Ausgabe
Memory250KB
SPI Baseband 868MHz
HW AccECC, AES
IPMS430
On board comm.
Tandem Node
First Tandem node, security flavour for BSI
FeuerWhere Node designed by IHP
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sensors and local infrastructure
• location aware mall – Metro Future-Store– location aware shopping system– finds location of products
• ubiquitous mall – mobile communication + sensors/RFID
tagsSensor node• tiny 1cm³• sensors, • battery,• CPU, • communicationSource: www.teco.edu
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telecom and internet world
• most modern cell phones combine features of former PDAs plus:– internet access– NFC– payment functionality
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sensors and internet
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applications
• bird observation on Great Duck Island– interest: breeding behavior: usage of burrows,
environment, breeding sites– nodes located in burrows and on surface– measurement: humidity, pressure, temperature,
ambient light (every minute)– infrared sensors detect presence of birds– ad-hoc clusters with dedicated node for long-range
communication
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applications (2)
• ZebraNet– interest: behavior of individual animals, interactions,
human impact– hundreds of square kilometers, years of observation,
every 3 minutes– animals carry nodes with GPS and sensors (now
light, more coming)– data transferred whenever nodes come close
together– mobile base station (car or plane) collects data from
time to time• related: cattle herding using “virtual fences”
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applications (3)
• disaster relief operations– drop sensor nodes from an aircraft over a
wildfire– each node measures temperature– derive a “temperature map”
• biodiversity mapping– use sensor nodes to observe wildlife
• intelligent buildings (or bridges)– reduce energy wastage by proper humidity,
ventilation, air conditioning (HVAC) control – needs measurements about room
occupancy, temperature, air flow, …
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sensors and local infrastructure
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Tunnel Monitoring
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application (3)
• glacier monitoring– interest: monitor glacier dynamics to understand climate– nodes in drill holes measure pressure, temperature, tilt– base station on glacier uses differential GPS, transmits data via
GSM– major problem: radio communication through ice and water
• ocean water monitoring– interest: global, long-term coverage of ocean and climate– measure temperature, salinity, ocean profile continuously– nodes cycle to 2000m depth every ten days– data transmitted to satellite when on surface
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application (4)
• vital sign monitoring– Interest: monitor vital signs of patients in hospital using WSN– Better accuracy and patient comfort compared to conventional
approaches– Components: patient identifier, medical sensors, display device,
setup pen– Staff uses setup pen to set up associations between body area
nodes• parts assembly
– Interest: assist assembly of do-it-yourself furniture– Parts and tools equipped with sensor nodes– Use force sensors (joints), gyroscope (screwdriver),
accelerometer (hammer)– Ad-hoc network detects activities, feedback via LEDs in furniture
parts
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application (5)
• power monitoring– interest: save power in large office building– sensor node connected to each power outlet– transceiver nodes form multihop network to central unit, gateway
to internet
• other applications– grape monitoring: conditions which influence plant growth– cold chain management: monitor food temperature compliance– avalanche rescue: assist rescue of avalanche victims– military vehicle tracking: find and track e.g. tanks– self-healing mine field: Intact mines hop into a breach– sniper localization: locate snipers and bullet trajectories
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application scenarios
• facility management– intrusion detection into industrial sites– control of leakages in chemical plants, …
• machine surveillance and preventive maintenance– embed sensing/control functions into places no cable has gone
before – e.g., tire pressure monitoring
• precision agriculture– bring out fertilizer/pesticides/irrigation only where needed
• medicine and health care– post-operative or intensive care
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application scenarios (2)
• logistics– equip goods (parcels, containers) with a sensor node– track their whereabouts – total asset management– note: passive readout might suffice – compare RFIDs
• telematics– provide better traffic control by obtaining finer-grained
information about traffic conditions– intelligent roadside– cars as the sensor nodes
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Application Areas
Industrial Automation Homeland Security
Telemedicine Context aware systems
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Geographical setting and system req.
Demonstration side• 65 Ground water measurement
points• 12,6km² area• 250m to 2000m distance • Rural/forest area• No power supply
Requirements• Automatic measurement (min. once
a day)• Radio transmission• Local buffering of measurement
results• 10 year maintenance free operation• Temperature range -30°Cto +40°C• Protection against vandalism and
animals
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Centralised server• GPRS/GSM connection
node• Local Internet-Server• Solar module
IQlevel System
Low Power Wireless Sensor Network• 868MHz Long Distance Radio• Ultra Low Power Micro controller• Low Duty Cycle Protocol• Crypto-based security• 10 years life time• Mesh-Network incl. adaptive
routingDigital probe
• Ultra Low Power Micro controller• Modular probe• Pressure-, ph-value-, sulphate- and
elect. conductivity measurements • Buffering of measurement results
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Protecting First Responders
Vital parameters:• Body core temperature• Pulse• Blood oxygen saturation
Environmental data:• Remaining air in the breathing apparatus• Temperature inside protective clothing• Temperature at surface of protective clothing • Environmental temperature appr. 2 m above the head of fire
fighters• Relative humidity inside protective clothing• Relative humidity around the fire fighters• Explosive gas and/or explosive pyrolysis products
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Data handling
• Buffering of all measurement data in the BAN
• In network processing (local evaluation)
• Timely transmission according a red-yellow-green model
– Red: acute life threatening situation, immediate data transmission (continuously)
– Yellow: situation might become life threatening in a short time scale, data transmission latest 10 sec. after measurement
– Green: no threat at all, transmission of data every 60 seconds as self-test
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Harsh Environmental Conditions
• Temperature up to 1000°C• Saturated steam atmosphere• No sight due to smoke• Extremely noisy • Aggressive liquids and gas • Ionizing radiation• Blast e.g. after explosion
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Protecting Critical Infrastructure (Drinking Water Pipeline)
Flow rate, pressure, quantity…
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Measurement parameters
Location Distance to next substation
Parameter Unit
flow rate (m³/h) pressure, outlet (bar)
Waterworks Briesen ~1800 m
position of butterfly valve indicationpressure pipe A(1) (bar) pressure pipe N(2) (bar)
Briesen (protection on pipe bursts)
<~5800m
position of butterfly valve indicationflow rate (m³/h) quantity (m³)
pressure pipe A(1) (bar) pressure pipe N(2) (bar)
Jacobsdorf (protection on pipe bursts)
~4800m
position of butterfly valve indication
pressure pipe A(1) (bar) pressure pipe N(2) (bar)
Pilgram/Pagram (protection on pipe bursts)
~2500m
position of butterfly valve indicationflow rate (m³/h)
quantity intake from waterworks with negativ
back flow
(m³) Reservoir 0m
quantity intake from waterworks
(m³)
Measurements are done every 30 seconds
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Pipe access points
34
• Power supply• Distance up to 6 km
• Spot to place additional hardware
• No power supply• Distance up to 3 km (usually
less)
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SCADA Integration
WSAN
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Intended System /software architecture
• MSP430 uC• Standard 868MHz radio (e.g. cc1100)
• + power amplifier + good antenna
• Tailored MAC & network stack • tinyOS
HWtinyOS
NetworkTransportRouting
ForwardingMAC
DCUUpdateservice
ServicesMIB/Identity
CiphersData storage
PROTIDS
Attestation
watchdog
Sensor
SensorcontrolNode Control
radio HWtinyOS
NetworkTransportRouting
ForwardingMAC
DCUUpdateservice
ServicesMIB/Identity
CiphersData storage
PROTIDS
Attestation
watchdog
Sensor
SensorcontrolNode Control
radio
Software Architecture
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Lego like sensor node construction kit
What we would like to have
Secure Sensor Node for CIPSensor Node for Protection of First Responders/Environmental Monitoring
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Construction Kit: Existing Components
• µC/Processors– Leon 2-3; MIPS; MSP430 derivate; 8051
• Radio Front Ends– UWB (802.15.4a), 868MHz (802.15.4 V2006); EN13757-3-4
• Hardware Accelerators/Power management– AES, ECC, TCP Checksum ; PowerSwitches
• Operating systems– tinyOS, Contiki, Reflex (BTU Cottbus); eCos
• Protocols– TCP, 802.15.4 Software (hardware under development); IHP-beaconing
• Middleware– tinyDSM (Event Definition; SQL like query language)
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roles of participants in WSN
• sources of data: Measure data, report them “somewhere”– Typically equip with different kinds of actual sensors
• sinks of data: Interested in receiving data from WSN – May be part of the WSN or external entity, PDA, gateway, …
• actuators: Control some device based on data, usually also a sink
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design space
• deployment– random or installed at chosen spots– one-time or continuous– classes: random/manual; one-time/iterative
• mobility– motion by environment (e.g. wind, water)– motion because attached to mobile entities (e.g. zebras)– motion of automotive nodes– can be desired property or undesirable accident– motion has large impact on network algorithms– classes: immobile/partly/all; occasional/continuous;
active/passive
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design space (2)
• cost, size, resources, energy– form factor depends on application (microscopic to shoebox)– cost from cents to hundreds of euro's– energy, computing, processing resources depend on size– classes: brick, matchbox, grain, dust
• heterogeneity– first approach: identical or indistinguishable nodes only– in practice: a variety of nodes can be very useful– bundle computational or communication resources (cluster
heads)– special capabilities only for some (e.g. GPS)– gateways to external networks (GSM, satellite, Internet)– heterogeneity has large effect on complexity of software– classes: homogeneous/heterogeneous
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design space (3)
• communication modality– How do nodes communicate ?– most common: radio waves, usually sub-gigahertz bands– light beams or laser: smaller, more energy efficient (cf. Smart Dust)– RFID coupling, sound, ultrasound also useful– classes: radio, light, inductive, capacative, sound
• infrastructure– How to construct the communication network ?– infrastructure-based: sensors communicate via base stations only– ad-hoc: direct communication between nodes– infrastructure is costly to deploy, ad-hoc often preferred– ad-hoc allows routers, multihop, message forwarding– classes: infrastructure / ad-hoc
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design space (4)
• network topology– important property:
diameter = max number of hops between any two nodes– single hop (d=1), infrastructure based (d=2), ad-hoc (d big)– topology affects QoS and software complexity– classes: single-hop / star / networked stars / tree / graph
• coverage– depends on range of attached sensors– sensors could cover only part of area of interest, or all, or
multiply– coverage influences observational accuracy, redundancy,
processing– classes: sparse / dense / redundant
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design space (5)
• connectivity– Nodes always connected or only sometimes ?
Network sometimes partitioned ?– connectivity influences communication protocols and data gathering– classes: connected / intermittent / sporadic
• size– range: a few nodes to thousands of nodes
• lifetime– How long does the sensor network exist ?– range: some hours to several years
• other QoS requirements– real-time, robustness, tamper-resistance, eavesdropping resistance,– unobtrusiveness, stealth
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Node Capabilities & Requirements
• Processing power : 8 or 16 bit µC• Memory : 16 to 256 kByte• Energy resources : typical small batteries
1000-5000 mAhenergy harvesting
• Active Time: 1-15 years• Cost : 1-100 $/node
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real life connectivity
• figures show WSN deployed on a flat parking lot
• expected: simple, circular shape of “region of communication” –not realistic
• instead: – correlation between distance and
loss rate is weak; iso-loss-lines are not circular but irregular
– asymmetric links are relatively frequent (up to 15%)
– significant short-term PER variations even for stationary nodes
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three regions of communication
• effective region: PER consistently 10%
• transitional region:anything in between,with large variation for nodes at same distance
• poor region: PER well beyond 90%
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discussion and conclusions
• single hardware platform will not be sufficient to cover applications• avoid application-specific hardware by small set of platforms
– cover different parts of design space– modular approach (exchange components of node) could help
• software situation is even more complex– cover design space with set of protocols, algorithms, basic services– system designer is still faced by complexity of design space
• use middleware as in conventional systems ? No...– aspects of DS are hard to hide from developer (e.g. topology)– must expose characteristics to handle resource limitations – Middleware would introduce significant resource overheads
• unconventional approaches towards general abstractions under discussion, may be tis is even kind of middleware