Superimposed Radio Signals for Wireless Sensor Networks

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Superimposed Radio Signals for Wireless Sensor
Networks

• Albert Krohn, TecO,
• University of Karlsruhe

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Important terms in this work

• What are sensor networks?
• Wireless networks consisting of small computers
• The computers are weak in resources
• Some KByte memory, some MHz clock speed, no
  co-processors, battery powered
• The wireless communication is the main advantage
• Every node is measuring device and at the same
  time data relay for multi-hop communication
• Large areas can be monitored fine-granular
• What are superimposed radio signals?
• Radio signals, that are emitted at the same time
  and in the same frequency band

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Summary PhD thesis proposal

• Thesis
• In sensor network, superimposed radio signals can
  solve principal 4 problems, which are
  synchronization, reliability, channel use and
  data fusion
• Problem
• Superimposed radio signals for wireless sensor
  network are a promising but yet insufficiently
  researched mechanism the low resources of WSN do
  not allow to use traditional theory of
  superimposed radio signals
• Contributions
• ESK new modulation scheme for superimposed radio
  signals in WSN
• For low resource hardware enables new methods of
  synchronization, channel access
• enables cooperative transmission to increase the
  connectivity and reliability
• SDJS communication protocol based on
  superimposed radio signals
• Probabilistic data communication enables fast
  parameter estimation and data fusion directly on
  the physical medium
• Real implementation and successful evaluation of
  all mechanism and contribution of this thesis in
  the wireless protocol AwareCon

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The structure of the thesis
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Motivation
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Motivation
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Thesis
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Thesis
In sensor network, superimposed radio signals can
solve principal problems like synchronisation,
reliability, channel use and data fusion
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Analysis traditional approaches are not useful
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Analysis traditional approaches are not useful

• Related Work
• Phase-exact synchronization D. III, G.Prince,
  and J. McNeill, 2005
• Using broad-band signalsA. Scaglione et al.,
  2003, 2004, 2005
• Cooperative transmission, Nicholas Laneman,
  2004, 2005

• Target plattforms
• Low Cost sensor networks
• Lowest possible requirements on hardware/software
• Only simple modulations ASK, OOK, 2-FSK
• No synchronization of phase of carrier
• No complex I-Q- Demodulation

• conclusion new approach necessary
• Lowest possible requirements
• Using pass band signals

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Architecture
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Architecture

• Communication
• N to 1 communication from non-coherent sources
• Superposition summation on the channel
• Receiver works unsynchronized

• Transceiver
• No complex (De-) Modulation
• Built from analog components
• No synchronization of phase or carrier frequency

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System and models
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System and models

• Signals
• Transmitter and receiver should be able to
  perform multistage modulation
• Optimal Maximum-Likelihood detector based on one
  sample
• Problem of destructive interference
• New modulation scheme ESK Krohn, IEEE ICASSP
  2006

• Channel model
• Flat-fading
• Rayleigh
• Common model of the statistics

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ESK derivation of the signal modulation

• Determine the optimal detection thresholds
  gbetween the symbols (out of the common
  statistic)
• With optimal detection thresholds, the
  detection probabilityPd is determined
• 3. This is optimized throughthe choice of the
  bestsignal constellation

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Evaluation, reference implementation
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Evaluation, reference implementation

• Reference implementation
• Master thesis Markus Hermann, TecO, 2005, Demo,
  Krohn et al PERVASIVE 2006, INSS 2006
• Implementation on Particle Computer
• ASK on transmitter side, ML-Detector on receiver
  side,
• Energy normalisation algorithm
• over 1000 measurement samples
• Proof that energies sum up together

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Applications
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Applications
In sensor network, superimposed radio signals can
solve principal problems like synchronisation,
reliability, channel use and data fusion
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Applications

• Parameter estimation, data fusion
• SDJS synchrone, decentralized jam signals, fast
  parameter estimation
• Krohn et al., IEEE ICNP 2004Krohn et
  al., PERVASIVE 2005
• Data fusion for location systems Krohn et al,
  SenSys 2006, submitted
• Reliability
• Superimposed radio signals increase the range of
  sensor nodesKrohn et al., INSS 2006
• Channel access
• ToMAC real-time channel access methodKrohn et
  al., INSS 2005
• Wireless CSMA/CR running mater thesis at TecO

• Synchronization
• AwareConV5
• Beigl, Krohn et al., UBICOMP 2003
  Decker, Krohn et al., IPSN 2005

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Applications - reliability

• Increasing Connectivity in Wireless Sensor
  Network using Cooperative Transmission, Krohn
  et al., INSS 2006
• Sparse sensor network scenarios can result in
  partitioning of the network.
• Reasons for sparse settings
• Random installation process (z.B. dropping from a
  plane)
• Changes in the environment (z.B. growth of
  plants, LOFAR Project)
• Aging and malfunction (batteries run low,
  defects)
• mobility (e.g. monitoring or animals)
• Superimposed radio signals can re-connect those
  partitioned networks

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Partitioning of networks example

• With partitioned networks, the communication
  cant be reliable anymore

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Accumulating cooperative transmission
without cooperative transmission networks
stays partitioned
with cooperative transmission the nodes
accumulate their transmit power such that
they can reach the destination
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Simulation

• Parameters
• area 500m x 500m 250.000 m²
• Number of nodes 10..200
• Nominal radio range 50m, Fading Exponent 2
• Number of random topologies for each density 100

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Summary PhD thesis proposal

• Thesis
• In sensor network, superimposed radio signals can
  solve principal problems like synchronization,
  reliability, channel use and data fusion
• contributions
• ESK new modulation model for superimposed radio
  signals
• Very low requirements on hardware/software
• Large application area even RFID
• Jam signaling suppresses destructive interference
• Enables new methods of synchronizations
• Enables CSMA/CR for the channel access in
  wireless systems
• enables cooperative transmission to increase the
  connectivity and reliability
• SDJS communication protocol based on
  superimposed radio signals
• Probabilistic data communication enables fast
  parameter estimation and data fusion directly on
  the physical medium
• Real implementation and successful evaluation of
  all mechanism and contribution of this thesis in
  the wireless protocol AwareCon

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Dissertationsvorhaben

• Inhaltliche Arbeiten 100 bis April 2006
• Modelle (90)
• Implementierung (80)
• Evaluierung (90)
• Aktuelle Veröffentlichungen
• ESK Übertragungsmodell ICASSP 2006
• Referenzimplementierung PERVASIVE 2006 (demo)
• Erreichbarkeit INSS 2006
• Noch geplante Veröffentlichungen
• Datenfusion mit SDJS ACM SenSys 2006
• Erste Version Dissertation bis Juli 2006
• Abgabe Dissertation September 2006

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Transportszenarien und verschiedene Ausprägungen

• Transportszenarien
• Peer-to-Peer Kommunikation Nodes kommunizieren
  untereinander
• Access-Point Kommunikation Nodes kommunizieren
  immer zu einer Senke
• Mobilität, ändernde Umgebung Flooding
• Verschiedene Prinzipien von kooperativem Senden
• Wellenausbreitung
• Nach Aussenden des ersten Paketes wiederholen
  alle Knoten, die es gehört haben, dieses Paket
  einmal
• Akkumulierendes kooperatives Senden
• Nach Aussenden des ersten Paketes wiederholen
  alle Knoten, die es gehört haben, dieses Paket
  n-mal
• Hybridverfahren
• Akkumulierendes kooperatives Senden und
  normales Multi-hop werden zyklisch kombiniert

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Überlagerte Funksignale Cooperative Transmission

• Das Konzept von überlagerten Funksignalen erhöht
  die Sendereichweite von Sensorknoten durch
  Kooperation
• Hierfür sind spezielle Modulationstechniken nötig
  ESK, damit die Überlagerung auch für
  leistungsschwache Sensorknoten erfolgreich ist
  und destruktive Interferenz verringert werden
  kann
• Die Leistungen addieren sich zu (einfaches
  Propagation Modell)